From 3f6c63e3847b32b70e8e08b27e31f2ba02eacbfe Mon Sep 17 00:00:00 2001 From: Prasad Date: Mon, 16 Oct 2017 19:28:39 +0530 Subject: [PATCH 01/19] Initial draft of code for flag detection. --- dataset/iterator.py | 103 ++++++++++++++++++++ detect/image_detector.py | 206 +++++++++++++++++++++++++++++++++++++++ object_detection_app.py | 187 +++++++++++++++++++++++++++++++++++ utils.py | 73 ++++++++++++++ video_detection.py | 109 +++++++++++++++++++++ 5 files changed, 678 insertions(+) create mode 100644 detect/image_detector.py create mode 100644 object_detection_app.py create mode 100644 utils.py create mode 100644 video_detection.py diff --git a/dataset/iterator.py b/dataset/iterator.py index f46efe6..992fd1d 100644 --- a/dataset/iterator.py +++ b/dataset/iterator.py @@ -288,3 +288,106 @@ def _data_augmentation(self, data, label): data = data.astype('float32') data = data - self._mean_pixels return data, label + +class DetTestImageIter(mx.io.DataIter): + """ + Detection Iterator, which will feed data and label to network + Optional data augmentation is performed when providing batch + + Parameters: + ---------- + imdb : Imdb + image database + batch_size : int + batch size + data_shape : int or (int, int) + image shape to be resized + mean_pixels : float or float list + [R, G, B], mean pixel values + """ + def __init__(self, test_images, batch_size, data_shape, \ + mean_pixels=[128, 128, 128]): + super(DetTestImageIter, self).__init__() + + self.test_images = test_images + self.batch_size = batch_size + if isinstance(data_shape, int): + data_shape = (data_shape, data_shape) + self._data_shape = data_shape + self._mean_pixels = mx.nd.array(mean_pixels).reshape((3,1,1)) + + self._current = 0 + self._size = len(test_images) + self._index = np.arange(self._size) + + self._data = None + self._label = None + self._get_batch() + self.resized_data = None + + @property + def provide_data(self): + return [(k, v.shape) for k, v in self._data.items()] + + @property + def provide_label(self): + return [] + + def reset(self): + self._current = 0 + + def iter_next(self): + return self._current < self._size + + def next(self): + if self.iter_next(): + self._get_batch() + data_batch = mx.io.DataBatch(data=list(self._data.values()), + label=list(self._label.values()), + pad=self.getpad(), index=self.getindex()) + self._current += self.batch_size + return data_batch + else: + raise StopIteration + + def getindex(self): + return self._current // self.batch_size + + def getpad(self): + pad = self._current + self.batch_size - self._size + return 0 if pad < 0 else pad + + def _get_batch(self): + """ + Load data/label from dataset + """ + batch_data = mx.nd.zeros((self.batch_size, 3, self._data_shape[0], self._data_shape[1])) + for i in range(self.batch_size): + if (self._current + i) >= self._size: + continue + else: + index = self._index[self._current + i] + img_content = self.test_images[index] + #img = mx.img.imdecode(img_content) + img = mx.nd.array(img_content) + data = self._data_augmentation(img) + batch_data[i] = data + + self._data = {'data': batch_data} + self._label = {'label': None} + + def _data_augmentation(self, data): + """ + perform data augmentations: crop, mirror, resize, sub mean, swap channels... + """ + interp_methods = [cv2.INTER_LINEAR] + interp_method = interp_methods[int(np.random.uniform(0, 1) * len(interp_methods))] + data = mx.img.imresize(data, self._data_shape[1], self._data_shape[0], interp_method) + self.resized_data = data + data = mx.nd.transpose(data, (2,0,1)) + data = data.astype('float32') + data = data - self._mean_pixels + return data + + def current_data(self): + return self.resized_data diff --git a/detect/image_detector.py b/detect/image_detector.py new file mode 100644 index 0000000..7efd863 --- /dev/null +++ b/detect/image_detector.py @@ -0,0 +1,206 @@ +from __future__ import print_function +import mxnet as mx +import numpy as np +from timeit import default_timer as timer +from dataset.iterator import DetTestImageIter +import cv2 + +class ImageDetector(object): + """ + SSD detector which hold a detection network and wraps detection API + + Parameters: + ---------- + symbol : mx.Symbol + detection network Symbol + model_prefix : str + name prefix of trained model + epoch : int + load epoch of trained model + data_shape : int + input data resize shape + mean_pixels : tuple of float + (mean_r, mean_g, mean_b) + batch_size : int + run detection with batch size + ctx : mx.ctx + device to use, if None, use mx.cpu() as default context + """ + def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \ + classes, batch_size=1, ctx=None): + self.ctx = ctx + if self.ctx is None: + self.ctx = mx.cpu() + load_symbol, args, auxs = mx.model.load_checkpoint(model_prefix, epoch) + if symbol is None: + symbol = load_symbol + self.mod = mx.mod.Module(symbol, label_names=None, context=ctx) + self.data_shape = data_shape + self.mod.bind(data_shapes=[('data', (batch_size, 3, data_shape, data_shape))]) + self.mod.set_params(args, auxs) + self.data_shape = data_shape + self.mean_pixels = mean_pixels + self.classes = classes + self.colors = [] + self.fill_random_colors() + + def fill_random_colors(self): + import random + for i in range(len(self.classes)): + self.colors.append((random.random(), random.random(), random.random())) + + print(self.colors) + + def fill_random_colors_int(self): + import random + for i in range(len(self.classes)): + self.colors.append((random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))) + + print(self.colors) + + + def detect(self, det_iter, show_timer=False): + """ + detect all images in iterator + + Parameters: + ---------- + det_iter : DetIter + iterator for all testing images + show_timer : Boolean + whether to print out detection exec time + + Returns: + ---------- + list of detection results + """ + num_images = det_iter._size + result = [] + detections = [] + #if not isinstance(det_iter, mx.io.PrefetchingIter): + # det_iter = mx.io.PrefetchingIter(det_iter) + start = timer() + for pred, _, _ in self.mod.iter_predict(det_iter): + detections.append(pred[0].asnumpy()) + time_elapsed = timer() - start + if show_timer and False: + print("Detection time for {} images: {:.4f} sec".format(num_images, time_elapsed)) + for output in detections: + for i in range(output.shape[0]): + det = output[i, :, :] + res = det[np.where(det[:, 0] >= 0)[0]] + result.append(res) + resized_img = det_iter.current_data() + return result, resized_img + + def im_detect(self, img, show_timer=False): + """ + wrapper for detecting multiple images + + Parameters: + ---------- + im_list : list of str + image path or list of image paths + root_dir : str + directory of input images, optional if image path already + has full directory information + extension : str + image extension, eg. ".jpg", optional + + Returns: + ---------- + list of detection results in format [det0, det1...], det is in + format np.array([id, score, xmin, ymin, xmax, ymax]...) + """ + im_list = [img] + test_iter = DetTestImageIter(im_list, 1, self.data_shape, self.mean_pixels) + return self.detect(test_iter, show_timer) + + def plot_rects(self, img, dets, thresh=0.6): + img_shape = img.shape + for i in range(dets.shape[0]): + cls_id = int(dets[i, 0]) + if cls_id >= 0: + score = dets[i, 1] + #print('Score is {}, class {}'.format(score, cls_id)) + if score > thresh: + xmin = int(dets[i, 2] * img_shape[1]) + ymin = int(dets[i, 3] * img_shape[0]) + xmax = int(dets[i, 4] * img_shape[1]) + ymax = int(dets[i, 5] * img_shape[0]) + + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 4) + + class_name = self.classes[cls_id] + cv2.putText(img, str(cls_id), (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 4) + print('Class id = {}, Score = {}, Country = {}, rect = ({}, {}, {}, {})'.format(cls_id, score, class_name, xmin, ymin, xmax, ymax)) + + def detect_and_visualize_image(self, img, thresh=0.6, show_timer=False): + """ + wrapper for im_detect and visualize_detection + + Parameters: + ---------- + im_list : list of str or str + image path or list of image paths + root_dir : str or None + directory of input images, optional if image path already + has full directory information + extension : str or None + image extension, eg. ".jpg", optional + + Returns: + ---------- + + """ + dets, resized_img = self.im_detect(img, show_timer=show_timer) + resized_img = resized_img.asnumpy() + resized_img /= 255.0 + for k, det in enumerate(dets): + self.plot_rects(resized_img, det, thresh) + return resized_img + + def scale_and_plot_rects(self, img, dets, thresh=0.6): + img_shape = img.shape + for i in range(dets.shape[0]): + cls_id = int(dets[i, 0]) + if cls_id >= 0: + score = dets[i, 1] + #print('Score is {}, class {}'.format(score, cls_id)) + if score > thresh: + xmin = int(dets[i, 2] * img_shape[1]) + ymin = int(dets[i, 3] * img_shape[0]) + xmax = int(dets[i, 4] * img_shape[1]) + ymax = int(dets[i, 5] * img_shape[0]) + + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 8) + + class_name = self.classes[cls_id] + cv2.putText(img, str(cls_id), (xmin, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 4, (0, 0, 255), 8) + score_color = (0, 255, 0) if score > 0.5 else (255, 0, 0) + cv2.putText(img, '{:.3f}'.format(score), (xmax - 140, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 2, score_color, 4) + if score < 0.5: + print('Class id = {}, Score = {}, Thresh = {}'.format(cls_id, score, thresh)) + + def detect_and_layover_image(self, img, thresh=0.6, show_timer=False): + """ + wrapper for im_detect and visualize_detection + + Parameters: + ---------- + im_list : list of str or str + image path or list of image paths + root_dir : str or None + directory of input images, optional if image path already + has full directory information + extension : str or None + image extension, eg. ".jpg", optional + + Returns: + ---------- + + """ + dets, _ = self.im_detect(img, show_timer=show_timer) + for k, det in enumerate(dets): + self.scale_and_plot_rects(img, det, thresh) + return img diff --git a/object_detection_app.py b/object_detection_app.py new file mode 100644 index 0000000..71df995 --- /dev/null +++ b/object_detection_app.py @@ -0,0 +1,187 @@ +import os +import cv2 +import time +import argparse +import multiprocessing +import numpy as np +import tools.find_mxnet +import mxnet as mx +import sys +from detect.image_detector import ImageDetector +from detect.detector import Detector +from symbol.symbol_factory import get_symbol + +from utils import FPS, WebcamVideoStream +from multiprocessing import Queue, Pool + +network = 'vgg16_reduced' +class_names = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria',\ + 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\ + 'BosniaandHerzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'BurkinaFaso', 'Burundi', 'Cambodia', 'Cameroon', 'Canada',\ + 'CapeVerde', 'CentralAfricanRepublic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'CostaRica', 'Croatia', 'Cuba', 'Cyprus',\ + 'CzechRepublic', 'DemocraticRepublicoftheCongo', 'Denmark', 'Djibouti', 'DominicanRepublic', 'EastTimor', 'Ecuador', 'Egypt',\ + 'ElSalvador', 'EquatorialGuinea', 'Eritrea', 'Estonia', 'Ethiopia', 'FalklandIslands', 'FederatedStatesofMicronesia', 'Fiji',\ + 'Finland', 'France', 'FrenchGuiana', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland', 'Guatemala',\ + 'Guinea', 'GuineaBissau', 'Guyana', 'Haiti', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\ + 'Ireland', 'Israel', 'Italy', 'IvoryCoast', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait',\ + 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg',\ + 'Macedonia', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'MarshallIslands', 'Mauritania',\ + 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\ + 'Nauru', 'Nepal', 'Netherlands', 'NewCaledonia', 'NewZealand', 'Nicaragua', 'Niger', 'Nigeria', 'NorthKorea', 'Norway',\ + 'Oman', 'Pakistan', 'Palau', 'Palestine', 'Panama', 'PapuaNewGuinea', 'Paraguay', 'Peru', 'Philippines', 'Poland',\ + 'Portugal', 'PuertoRico', 'Qatar', 'RepublicofCongo', 'Romania', 'Russia', 'Rwanda', 'Samoa', 'SanMarino',\ + 'SaoTomeandPrincipe', 'SaudiArabia', 'Senegal', 'Serbia', 'Seychelles', 'SierraLeone', 'Singapore', 'Slovakia',\ + 'Slovenia', 'SolomonIslands', 'Somalia', 'SouthAfrica', 'SouthKorea', 'SouthSudan', 'Spain', 'SriLanka', 'Sudan',\ + 'Suriname', 'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Tajikistan', 'Tanzania', 'Thailand', 'Togo', 'Tonga',\ + 'Tunisia', 'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine', 'UnitedArabEmirates', 'UnitedKingdom',\ + 'UnitedStates', 'Uruguay', 'Uzbekistan', 'Vanuatu', 'VaticanCity', 'Venezuela', 'Vietnam', 'WesternSahara',\ + 'Yemen', 'Zambia', 'Zimbabwe'] + +class_names = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria',\ + 'Azerbaijan'] +epoch = 6 +cwd = os.getcwd() +prefix = cwd + '/model/ssd' +data_shape = 300 +color_subtract = (123, 117, 104) +nms_thresh = 0.5 +force_nms = True +show_timer = True +thresh = 0.5 + +def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, class_names, + nms_thresh=0.5, force_nms=True, nms_topk=400): + """ + wrapper for initialize a detector + + Parameters: + ---------- + net : str + test network name + prefix : str + load model prefix + epoch : int + load model epoch + data_shape : int + resize image shape + mean_pixels : tuple (float, float, float) + mean pixel values (R, G, B) + ctx : mx.ctx + running context, mx.cpu() or mx.gpu(?) + num_class : int + number of classes + nms_thresh : float + non-maximum suppression threshold + force_nms : bool + force suppress different categories + """ + if net is not None: + net = get_symbol(net, data_shape, num_classes=len(class_names), nms_thresh=nms_thresh, + force_nms=force_nms, nms_topk=nms_topk) + detector = ImageDetector(net, prefix, epoch, data_shape, mean_pixels, class_names, ctx=ctx) + #detector = Detector(net, prefix, epoch, data_shape, mean_pixels, ctx=ctx) + return detector + +detector = get_detector(network, prefix, epoch, data_shape, color_subtract, mx.gpu(0), + class_names, nms_thresh, force_nms) + +def process_image(image_frame): + # run detection + detected_img = detector.detect_and_visualize_image(image_frame, thresh, show_timer) + #detected_img = detector.detect_and_layover_image(image_frame, thresh, show_timer) + return detected_img + +def worker(input_q, output_q): + fps = FPS().start() + while True: + fps.update() + frame = input_q.get() + #frame = np.transpose(frame, (2, 0, 1)) + #output_q.put(detect_objects(frame, sess, detection_graph)) + detected_img = process_image(frame) + output_q.put(detected_img) + + fps.stop() + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-src', '--source', dest='video_source', type=int, + default=0, help='Device index of the camera.') + parser.add_argument('-wd', '--width', dest='width', type=int, + default=480, help='Width of the frames in the video stream.') + parser.add_argument('-ht', '--height', dest='height', type=int, + default=360, help='Height of the frames in the video stream.') + parser.add_argument('-num-w', '--num-workers', dest='num_workers', type=int, + default=1, help='Number of workers.') + parser.add_argument('-q-size', '--queue-size', dest='queue_size', type=int, + default=5, help='Size of the queue.') + args = parser.parse_args() + + #time.sleep(1) + + #''' + video_capture = WebcamVideoStream(src=args.video_source, + width=args.width, + height=args.height).start() + fps = FPS().start() + + while True: # fps._numFrames < 120 + frame = video_capture.read() + detected_img = process_image(frame) + + t = time.time() + + cv2.imshow('Video', detected_img) + fps.update() + + #print('[INFO] elapsed time: {:.2f}'.format(time.time() - t)) + + if cv2.waitKey(1) & 0xFF == ord('q'): + break + + + fps.stop() + #print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) + #print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) + + video_capture.stop() + cv2.destroyAllWindows() + #''' + + + ''' + logger = multiprocessing.log_to_stderr() + #logger.setLevel(multiprocessing.SUBDEBUG) + + input_q = Queue(maxsize=args.queue_size) + output_q = Queue(maxsize=args.queue_size) + pool = Pool(args.num_workers, worker, (input_q, output_q)) + + video_capture = WebcamVideoStream(src=args.video_source, + width=args.width, + height=args.height).start() + fps = FPS().start() + + while True: # fps._numFrames < 120 + frame = video_capture.read() + input_q.put(frame) + + t = time.time() + + cv2.imshow('Video', output_q.get()) + fps.update() + + #print('[INFO] elapsed time: {:.2f}'.format(time.time() - t)) + + if cv2.waitKey(1) & 0xFF == ord('q'): + break + + + fps.stop() + #print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) + #print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) + + pool.terminate() + video_capture.stop() + cv2.destroyAllWindows() + ''' diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..c1f2a58 --- /dev/null +++ b/utils.py @@ -0,0 +1,73 @@ +# From http://www.pyimagesearch.com/2015/12/21/increasing-webcam-fps-with-python-and-opencv/ + +import cv2 +import datetime +from threading import Thread + +class FPS: + def __init__(self): + # store the start time, end time, and total number of frames + # that were examined between the start and end intervals + self._start = None + self._end = None + self._numFrames = 0 + + def start(self): + # start the timer + self._start = datetime.datetime.now() + return self + + def stop(self): + # stop the timer + self._end = datetime.datetime.now() + + def update(self): + # increment the total number of frames examined during the + # start and end intervals + self._numFrames += 1 + + def elapsed(self): + # return the total number of seconds between the start and + # end interval + return (self._end - self._start).total_seconds() + + def fps(self): + # compute the (approximate) frames per second + return self._numFrames / self.elapsed() + +class WebcamVideoStream: + def __init__(self, src, width, height): + # initialize the video camera stream and read the first frame + # from the stream + src = -1 + self.stream = cv2.VideoCapture(src) + self.stream.set(cv2.CAP_PROP_FRAME_WIDTH, width) + self.stream.set(cv2.CAP_PROP_FRAME_HEIGHT, height) + (self.grabbed, self.frame) = self.stream.read() + + # initialize the variable used to indicate if the thread should + # be stopped + self.stopped = False + + def start(self): + # start the thread to read frames from the video stream + Thread(target=self.update, args=()).start() + return self + + def update(self): + # keep looping infinitely until the thread is stopped + while True: + # if the thread indicator variable is set, stop the thread + if self.stopped: + return + + # otherwise, read the next frame from the stream + (self.grabbed, self.frame) = self.stream.read() + + def read(self): + # return the frame most recently read + return self.frame + + def stop(self): + # indicate that the thread should be stopped + self.stopped = True diff --git a/video_detection.py b/video_detection.py new file mode 100644 index 0000000..6d0b060 --- /dev/null +++ b/video_detection.py @@ -0,0 +1,109 @@ +import os +import cv2 +import time +import argparse +import multiprocessing +import numpy as np +import tools.find_mxnet +import mxnet as mx +import sys +from detect.image_detector import ImageDetector +from detect.detector import Detector +from symbol.symbol_factory import get_symbol + +from utils import FPS, WebcamVideoStream +from multiprocessing import Queue, Pool + +from moviepy.editor import VideoFileClip +from IPython.display import HTML + +IS_CLASSES_30 = True +INPUT_VIDEO_AT = './model/project_video.MOV' +OUTPUT_VIDEO_AT = './model/output_video.mp4' + +network = 'vgg16_reduced' +class_names_30 = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria',\ + 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\ + 'BosniaandHerzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'BurkinaFaso', 'Burundi', 'Cambodia', 'Cameroon', 'Canada'] + +class_names_194 = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria',\ + 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\ + 'BosniaandHerzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'BurkinaFaso', 'Burundi', 'Cambodia', 'Cameroon', 'Canada',\ + 'CapeVerde', 'CentralAfricanRepublic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'CostaRica', 'Croatia', 'Cuba', 'Cyprus',\ + 'CzechRepublic', 'DemocraticRepublicoftheCongo', 'Denmark', 'Djibouti', 'DominicanRepublic', 'EastTimor', 'Ecuador', 'Egypt',\ + 'ElSalvador', 'EquatorialGuinea', 'Eritrea', 'Estonia', 'Ethiopia', 'FalklandIslands', 'FederatedStatesofMicronesia', 'Fiji',\ + 'Finland', 'France', 'FrenchGuiana', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland', 'Guatemala',\ + 'Guinea', 'GuineaBissau', 'Guyana', 'Haiti', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\ + 'Ireland', 'Israel', 'Italy', 'IvoryCoast', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait',\ + 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg',\ + 'Macedonia', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'MarshallIslands', 'Mauritania',\ + 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\ + 'Nauru', 'Nepal', 'Netherlands', 'NewCaledonia', 'NewZealand', 'Nicaragua', 'Niger', 'Nigeria', 'NorthKorea', 'Norway',\ + 'Oman', 'Pakistan', 'Palau', 'Palestine', 'Panama', 'PapuaNewGuinea', 'Paraguay', 'Peru', 'Philippines', 'Poland',\ + 'Portugal', 'PuertoRico', 'Qatar', 'RepublicofCongo', 'Romania', 'Russia', 'Rwanda', 'Samoa', 'SanMarino',\ + 'SaoTomeandPrincipe', 'SaudiArabia', 'Senegal', 'Serbia', 'Seychelles', 'SierraLeone', 'Singapore', 'Slovakia',\ + 'Slovenia', 'SolomonIslands', 'Somalia', 'SouthAfrica', 'SouthKorea', 'SouthSudan', 'Spain', 'SriLanka', 'Sudan',\ + 'Suriname', 'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Tajikistan', 'Tanzania', 'Thailand', 'Togo', 'Tonga',\ + 'Tunisia', 'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine', 'UnitedArabEmirates', 'UnitedKingdom',\ + 'UnitedStates', 'Uruguay', 'Uzbekistan', 'Vanuatu', 'VaticanCity', 'Venezuela', 'Vietnam', 'WesternSahara',\ + 'Yemen', 'Zambia', 'Zimbabwe'] + + +epoch = 4 if IS_CLASSES_30 else 13 +class_names = class_names_30 if IS_CLASSES_30 else class_names_194 +cwd = os.getcwd() +prefix = cwd + '/model/ssd' +data_shape = 300 +color_subtract = (123, 117, 104) +nms_thresh = 0.5 +force_nms = True +show_timer = True +thresh = 0.43 + +def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, class_names, + nms_thresh=0.5, force_nms=True, nms_topk=400): + """ + wrapper for initialize a detector + + Parameters: + ---------- + net : str + test network name + prefix : str + load model prefix + epoch : int + load model epoch + data_shape : int + resize image shape + mean_pixels : tuple (float, float, float) + mean pixel values (R, G, B) + ctx : mx.ctx + running context, mx.cpu() or mx.gpu(?) + num_class : int + number of classes + nms_thresh : float + non-maximum suppression threshold + force_nms : bool + force suppress different categories + """ + if net is not None: + net = get_symbol(net, data_shape, num_classes=len(class_names), nms_thresh=nms_thresh, + force_nms=force_nms, nms_topk=nms_topk) + detector = ImageDetector(net, prefix, epoch, data_shape, mean_pixels, class_names, ctx=ctx) + #detector = Detector(net, prefix, epoch, data_shape, mean_pixels, ctx=ctx) + return detector + +detector = get_detector(network, prefix, epoch, data_shape, color_subtract, mx.gpu(0), + class_names, nms_thresh, force_nms) + +def process_image(image_frame): + # run detection + detected_img = detector.detect_and_layover_image(image_frame, thresh, show_timer) + return detected_img + +if __name__ == '__main__': + output_at = OUTPUT_VIDEO_AT + clip1 = VideoFileClip(INPUT_VIDEO_AT) + white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!! + white_clip.write_videofile(output_at, audio=False) + \ No newline at end of file From 6da153770a961c5d54ae14af9dc576d326af3c72 Mon Sep 17 00:00:00 2001 From: Prasad Date: Mon, 16 Oct 2017 19:28:39 +0530 Subject: [PATCH 02/19] Initial draft of code for flag detection. --- dataset/iterator.py | 103 ++++++++++++++++++++ detect/image_detector.py | 206 +++++++++++++++++++++++++++++++++++++++ object_detection_app.py | 187 +++++++++++++++++++++++++++++++++++ utils.py | 73 ++++++++++++++ video_detection.py | 109 +++++++++++++++++++++ 5 files changed, 678 insertions(+) create mode 100644 detect/image_detector.py create mode 100644 object_detection_app.py create mode 100644 utils.py create mode 100644 video_detection.py diff --git a/dataset/iterator.py b/dataset/iterator.py index f46efe6..992fd1d 100644 --- a/dataset/iterator.py +++ b/dataset/iterator.py @@ -288,3 +288,106 @@ def _data_augmentation(self, data, label): data = data.astype('float32') data = data - self._mean_pixels return data, label + +class DetTestImageIter(mx.io.DataIter): + """ + Detection Iterator, which will feed data and label to network + Optional data augmentation is performed when providing batch + + Parameters: + ---------- + imdb : Imdb + image database + batch_size : int + batch size + data_shape : int or (int, int) + image shape to be resized + mean_pixels : float or float list + [R, G, B], mean pixel values + """ + def __init__(self, test_images, batch_size, data_shape, \ + mean_pixels=[128, 128, 128]): + super(DetTestImageIter, self).__init__() + + self.test_images = test_images + self.batch_size = batch_size + if isinstance(data_shape, int): + data_shape = (data_shape, data_shape) + self._data_shape = data_shape + self._mean_pixels = mx.nd.array(mean_pixels).reshape((3,1,1)) + + self._current = 0 + self._size = len(test_images) + self._index = np.arange(self._size) + + self._data = None + self._label = None + self._get_batch() + self.resized_data = None + + @property + def provide_data(self): + return [(k, v.shape) for k, v in self._data.items()] + + @property + def provide_label(self): + return [] + + def reset(self): + self._current = 0 + + def iter_next(self): + return self._current < self._size + + def next(self): + if self.iter_next(): + self._get_batch() + data_batch = mx.io.DataBatch(data=list(self._data.values()), + label=list(self._label.values()), + pad=self.getpad(), index=self.getindex()) + self._current += self.batch_size + return data_batch + else: + raise StopIteration + + def getindex(self): + return self._current // self.batch_size + + def getpad(self): + pad = self._current + self.batch_size - self._size + return 0 if pad < 0 else pad + + def _get_batch(self): + """ + Load data/label from dataset + """ + batch_data = mx.nd.zeros((self.batch_size, 3, self._data_shape[0], self._data_shape[1])) + for i in range(self.batch_size): + if (self._current + i) >= self._size: + continue + else: + index = self._index[self._current + i] + img_content = self.test_images[index] + #img = mx.img.imdecode(img_content) + img = mx.nd.array(img_content) + data = self._data_augmentation(img) + batch_data[i] = data + + self._data = {'data': batch_data} + self._label = {'label': None} + + def _data_augmentation(self, data): + """ + perform data augmentations: crop, mirror, resize, sub mean, swap channels... + """ + interp_methods = [cv2.INTER_LINEAR] + interp_method = interp_methods[int(np.random.uniform(0, 1) * len(interp_methods))] + data = mx.img.imresize(data, self._data_shape[1], self._data_shape[0], interp_method) + self.resized_data = data + data = mx.nd.transpose(data, (2,0,1)) + data = data.astype('float32') + data = data - self._mean_pixels + return data + + def current_data(self): + return self.resized_data diff --git a/detect/image_detector.py b/detect/image_detector.py new file mode 100644 index 0000000..7efd863 --- /dev/null +++ b/detect/image_detector.py @@ -0,0 +1,206 @@ +from __future__ import print_function +import mxnet as mx +import numpy as np +from timeit import default_timer as timer +from dataset.iterator import DetTestImageIter +import cv2 + +class ImageDetector(object): + """ + SSD detector which hold a detection network and wraps detection API + + Parameters: + ---------- + symbol : mx.Symbol + detection network Symbol + model_prefix : str + name prefix of trained model + epoch : int + load epoch of trained model + data_shape : int + input data resize shape + mean_pixels : tuple of float + (mean_r, mean_g, mean_b) + batch_size : int + run detection with batch size + ctx : mx.ctx + device to use, if None, use mx.cpu() as default context + """ + def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \ + classes, batch_size=1, ctx=None): + self.ctx = ctx + if self.ctx is None: + self.ctx = mx.cpu() + load_symbol, args, auxs = mx.model.load_checkpoint(model_prefix, epoch) + if symbol is None: + symbol = load_symbol + self.mod = mx.mod.Module(symbol, label_names=None, context=ctx) + self.data_shape = data_shape + self.mod.bind(data_shapes=[('data', (batch_size, 3, data_shape, data_shape))]) + self.mod.set_params(args, auxs) + self.data_shape = data_shape + self.mean_pixels = mean_pixels + self.classes = classes + self.colors = [] + self.fill_random_colors() + + def fill_random_colors(self): + import random + for i in range(len(self.classes)): + self.colors.append((random.random(), random.random(), random.random())) + + print(self.colors) + + def fill_random_colors_int(self): + import random + for i in range(len(self.classes)): + self.colors.append((random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))) + + print(self.colors) + + + def detect(self, det_iter, show_timer=False): + """ + detect all images in iterator + + Parameters: + ---------- + det_iter : DetIter + iterator for all testing images + show_timer : Boolean + whether to print out detection exec time + + Returns: + ---------- + list of detection results + """ + num_images = det_iter._size + result = [] + detections = [] + #if not isinstance(det_iter, mx.io.PrefetchingIter): + # det_iter = mx.io.PrefetchingIter(det_iter) + start = timer() + for pred, _, _ in self.mod.iter_predict(det_iter): + detections.append(pred[0].asnumpy()) + time_elapsed = timer() - start + if show_timer and False: + print("Detection time for {} images: {:.4f} sec".format(num_images, time_elapsed)) + for output in detections: + for i in range(output.shape[0]): + det = output[i, :, :] + res = det[np.where(det[:, 0] >= 0)[0]] + result.append(res) + resized_img = det_iter.current_data() + return result, resized_img + + def im_detect(self, img, show_timer=False): + """ + wrapper for detecting multiple images + + Parameters: + ---------- + im_list : list of str + image path or list of image paths + root_dir : str + directory of input images, optional if image path already + has full directory information + extension : str + image extension, eg. ".jpg", optional + + Returns: + ---------- + list of detection results in format [det0, det1...], det is in + format np.array([id, score, xmin, ymin, xmax, ymax]...) + """ + im_list = [img] + test_iter = DetTestImageIter(im_list, 1, self.data_shape, self.mean_pixels) + return self.detect(test_iter, show_timer) + + def plot_rects(self, img, dets, thresh=0.6): + img_shape = img.shape + for i in range(dets.shape[0]): + cls_id = int(dets[i, 0]) + if cls_id >= 0: + score = dets[i, 1] + #print('Score is {}, class {}'.format(score, cls_id)) + if score > thresh: + xmin = int(dets[i, 2] * img_shape[1]) + ymin = int(dets[i, 3] * img_shape[0]) + xmax = int(dets[i, 4] * img_shape[1]) + ymax = int(dets[i, 5] * img_shape[0]) + + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 4) + + class_name = self.classes[cls_id] + cv2.putText(img, str(cls_id), (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 4) + print('Class id = {}, Score = {}, Country = {}, rect = ({}, {}, {}, {})'.format(cls_id, score, class_name, xmin, ymin, xmax, ymax)) + + def detect_and_visualize_image(self, img, thresh=0.6, show_timer=False): + """ + wrapper for im_detect and visualize_detection + + Parameters: + ---------- + im_list : list of str or str + image path or list of image paths + root_dir : str or None + directory of input images, optional if image path already + has full directory information + extension : str or None + image extension, eg. ".jpg", optional + + Returns: + ---------- + + """ + dets, resized_img = self.im_detect(img, show_timer=show_timer) + resized_img = resized_img.asnumpy() + resized_img /= 255.0 + for k, det in enumerate(dets): + self.plot_rects(resized_img, det, thresh) + return resized_img + + def scale_and_plot_rects(self, img, dets, thresh=0.6): + img_shape = img.shape + for i in range(dets.shape[0]): + cls_id = int(dets[i, 0]) + if cls_id >= 0: + score = dets[i, 1] + #print('Score is {}, class {}'.format(score, cls_id)) + if score > thresh: + xmin = int(dets[i, 2] * img_shape[1]) + ymin = int(dets[i, 3] * img_shape[0]) + xmax = int(dets[i, 4] * img_shape[1]) + ymax = int(dets[i, 5] * img_shape[0]) + + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 8) + + class_name = self.classes[cls_id] + cv2.putText(img, str(cls_id), (xmin, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 4, (0, 0, 255), 8) + score_color = (0, 255, 0) if score > 0.5 else (255, 0, 0) + cv2.putText(img, '{:.3f}'.format(score), (xmax - 140, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 2, score_color, 4) + if score < 0.5: + print('Class id = {}, Score = {}, Thresh = {}'.format(cls_id, score, thresh)) + + def detect_and_layover_image(self, img, thresh=0.6, show_timer=False): + """ + wrapper for im_detect and visualize_detection + + Parameters: + ---------- + im_list : list of str or str + image path or list of image paths + root_dir : str or None + directory of input images, optional if image path already + has full directory information + extension : str or None + image extension, eg. ".jpg", optional + + Returns: + ---------- + + """ + dets, _ = self.im_detect(img, show_timer=show_timer) + for k, det in enumerate(dets): + self.scale_and_plot_rects(img, det, thresh) + return img diff --git a/object_detection_app.py b/object_detection_app.py new file mode 100644 index 0000000..71df995 --- /dev/null +++ b/object_detection_app.py @@ -0,0 +1,187 @@ +import os +import cv2 +import time +import argparse +import multiprocessing +import numpy as np +import tools.find_mxnet +import mxnet as mx +import sys +from detect.image_detector import ImageDetector +from detect.detector import Detector +from symbol.symbol_factory import get_symbol + +from utils import FPS, WebcamVideoStream +from multiprocessing import Queue, Pool + +network = 'vgg16_reduced' +class_names = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria',\ + 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\ + 'BosniaandHerzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'BurkinaFaso', 'Burundi', 'Cambodia', 'Cameroon', 'Canada',\ + 'CapeVerde', 'CentralAfricanRepublic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'CostaRica', 'Croatia', 'Cuba', 'Cyprus',\ + 'CzechRepublic', 'DemocraticRepublicoftheCongo', 'Denmark', 'Djibouti', 'DominicanRepublic', 'EastTimor', 'Ecuador', 'Egypt',\ + 'ElSalvador', 'EquatorialGuinea', 'Eritrea', 'Estonia', 'Ethiopia', 'FalklandIslands', 'FederatedStatesofMicronesia', 'Fiji',\ + 'Finland', 'France', 'FrenchGuiana', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland', 'Guatemala',\ + 'Guinea', 'GuineaBissau', 'Guyana', 'Haiti', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\ + 'Ireland', 'Israel', 'Italy', 'IvoryCoast', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait',\ + 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg',\ + 'Macedonia', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'MarshallIslands', 'Mauritania',\ + 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\ + 'Nauru', 'Nepal', 'Netherlands', 'NewCaledonia', 'NewZealand', 'Nicaragua', 'Niger', 'Nigeria', 'NorthKorea', 'Norway',\ + 'Oman', 'Pakistan', 'Palau', 'Palestine', 'Panama', 'PapuaNewGuinea', 'Paraguay', 'Peru', 'Philippines', 'Poland',\ + 'Portugal', 'PuertoRico', 'Qatar', 'RepublicofCongo', 'Romania', 'Russia', 'Rwanda', 'Samoa', 'SanMarino',\ + 'SaoTomeandPrincipe', 'SaudiArabia', 'Senegal', 'Serbia', 'Seychelles', 'SierraLeone', 'Singapore', 'Slovakia',\ + 'Slovenia', 'SolomonIslands', 'Somalia', 'SouthAfrica', 'SouthKorea', 'SouthSudan', 'Spain', 'SriLanka', 'Sudan',\ + 'Suriname', 'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Tajikistan', 'Tanzania', 'Thailand', 'Togo', 'Tonga',\ + 'Tunisia', 'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine', 'UnitedArabEmirates', 'UnitedKingdom',\ + 'UnitedStates', 'Uruguay', 'Uzbekistan', 'Vanuatu', 'VaticanCity', 'Venezuela', 'Vietnam', 'WesternSahara',\ + 'Yemen', 'Zambia', 'Zimbabwe'] + +class_names = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria',\ + 'Azerbaijan'] +epoch = 6 +cwd = os.getcwd() +prefix = cwd + '/model/ssd' +data_shape = 300 +color_subtract = (123, 117, 104) +nms_thresh = 0.5 +force_nms = True +show_timer = True +thresh = 0.5 + +def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, class_names, + nms_thresh=0.5, force_nms=True, nms_topk=400): + """ + wrapper for initialize a detector + + Parameters: + ---------- + net : str + test network name + prefix : str + load model prefix + epoch : int + load model epoch + data_shape : int + resize image shape + mean_pixels : tuple (float, float, float) + mean pixel values (R, G, B) + ctx : mx.ctx + running context, mx.cpu() or mx.gpu(?) + num_class : int + number of classes + nms_thresh : float + non-maximum suppression threshold + force_nms : bool + force suppress different categories + """ + if net is not None: + net = get_symbol(net, data_shape, num_classes=len(class_names), nms_thresh=nms_thresh, + force_nms=force_nms, nms_topk=nms_topk) + detector = ImageDetector(net, prefix, epoch, data_shape, mean_pixels, class_names, ctx=ctx) + #detector = Detector(net, prefix, epoch, data_shape, mean_pixels, ctx=ctx) + return detector + +detector = get_detector(network, prefix, epoch, data_shape, color_subtract, mx.gpu(0), + class_names, nms_thresh, force_nms) + +def process_image(image_frame): + # run detection + detected_img = detector.detect_and_visualize_image(image_frame, thresh, show_timer) + #detected_img = detector.detect_and_layover_image(image_frame, thresh, show_timer) + return detected_img + +def worker(input_q, output_q): + fps = FPS().start() + while True: + fps.update() + frame = input_q.get() + #frame = np.transpose(frame, (2, 0, 1)) + #output_q.put(detect_objects(frame, sess, detection_graph)) + detected_img = process_image(frame) + output_q.put(detected_img) + + fps.stop() + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-src', '--source', dest='video_source', type=int, + default=0, help='Device index of the camera.') + parser.add_argument('-wd', '--width', dest='width', type=int, + default=480, help='Width of the frames in the video stream.') + parser.add_argument('-ht', '--height', dest='height', type=int, + default=360, help='Height of the frames in the video stream.') + parser.add_argument('-num-w', '--num-workers', dest='num_workers', type=int, + default=1, help='Number of workers.') + parser.add_argument('-q-size', '--queue-size', dest='queue_size', type=int, + default=5, help='Size of the queue.') + args = parser.parse_args() + + #time.sleep(1) + + #''' + video_capture = WebcamVideoStream(src=args.video_source, + width=args.width, + height=args.height).start() + fps = FPS().start() + + while True: # fps._numFrames < 120 + frame = video_capture.read() + detected_img = process_image(frame) + + t = time.time() + + cv2.imshow('Video', detected_img) + fps.update() + + #print('[INFO] elapsed time: {:.2f}'.format(time.time() - t)) + + if cv2.waitKey(1) & 0xFF == ord('q'): + break + + + fps.stop() + #print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) + #print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) + + video_capture.stop() + cv2.destroyAllWindows() + #''' + + + ''' + logger = multiprocessing.log_to_stderr() + #logger.setLevel(multiprocessing.SUBDEBUG) + + input_q = Queue(maxsize=args.queue_size) + output_q = Queue(maxsize=args.queue_size) + pool = Pool(args.num_workers, worker, (input_q, output_q)) + + video_capture = WebcamVideoStream(src=args.video_source, + width=args.width, + height=args.height).start() + fps = FPS().start() + + while True: # fps._numFrames < 120 + frame = video_capture.read() + input_q.put(frame) + + t = time.time() + + cv2.imshow('Video', output_q.get()) + fps.update() + + #print('[INFO] elapsed time: {:.2f}'.format(time.time() - t)) + + if cv2.waitKey(1) & 0xFF == ord('q'): + break + + + fps.stop() + #print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) + #print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) + + pool.terminate() + video_capture.stop() + cv2.destroyAllWindows() + ''' diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..c1f2a58 --- /dev/null +++ b/utils.py @@ -0,0 +1,73 @@ +# From http://www.pyimagesearch.com/2015/12/21/increasing-webcam-fps-with-python-and-opencv/ + +import cv2 +import datetime +from threading import Thread + +class FPS: + def __init__(self): + # store the start time, end time, and total number of frames + # that were examined between the start and end intervals + self._start = None + self._end = None + self._numFrames = 0 + + def start(self): + # start the timer + self._start = datetime.datetime.now() + return self + + def stop(self): + # stop the timer + self._end = datetime.datetime.now() + + def update(self): + # increment the total number of frames examined during the + # start and end intervals + self._numFrames += 1 + + def elapsed(self): + # return the total number of seconds between the start and + # end interval + return (self._end - self._start).total_seconds() + + def fps(self): + # compute the (approximate) frames per second + return self._numFrames / self.elapsed() + +class WebcamVideoStream: + def __init__(self, src, width, height): + # initialize the video camera stream and read the first frame + # from the stream + src = -1 + self.stream = cv2.VideoCapture(src) + self.stream.set(cv2.CAP_PROP_FRAME_WIDTH, width) + self.stream.set(cv2.CAP_PROP_FRAME_HEIGHT, height) + (self.grabbed, self.frame) = self.stream.read() + + # initialize the variable used to indicate if the thread should + # be stopped + self.stopped = False + + def start(self): + # start the thread to read frames from the video stream + Thread(target=self.update, args=()).start() + return self + + def update(self): + # keep looping infinitely until the thread is stopped + while True: + # if the thread indicator variable is set, stop the thread + if self.stopped: + return + + # otherwise, read the next frame from the stream + (self.grabbed, self.frame) = self.stream.read() + + def read(self): + # return the frame most recently read + return self.frame + + def stop(self): + # indicate that the thread should be stopped + self.stopped = True diff --git a/video_detection.py b/video_detection.py new file mode 100644 index 0000000..6d0b060 --- /dev/null +++ b/video_detection.py @@ -0,0 +1,109 @@ +import os +import cv2 +import time +import argparse +import multiprocessing +import numpy as np +import tools.find_mxnet +import mxnet as mx +import sys +from detect.image_detector import ImageDetector +from detect.detector import Detector +from symbol.symbol_factory import get_symbol + +from utils import FPS, WebcamVideoStream +from multiprocessing import Queue, Pool + +from moviepy.editor import VideoFileClip +from IPython.display import HTML + +IS_CLASSES_30 = True +INPUT_VIDEO_AT = './model/project_video.MOV' +OUTPUT_VIDEO_AT = './model/output_video.mp4' + +network = 'vgg16_reduced' +class_names_30 = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria',\ + 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\ + 'BosniaandHerzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'BurkinaFaso', 'Burundi', 'Cambodia', 'Cameroon', 'Canada'] + +class_names_194 = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria',\ + 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\ + 'BosniaandHerzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'BurkinaFaso', 'Burundi', 'Cambodia', 'Cameroon', 'Canada',\ + 'CapeVerde', 'CentralAfricanRepublic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'CostaRica', 'Croatia', 'Cuba', 'Cyprus',\ + 'CzechRepublic', 'DemocraticRepublicoftheCongo', 'Denmark', 'Djibouti', 'DominicanRepublic', 'EastTimor', 'Ecuador', 'Egypt',\ + 'ElSalvador', 'EquatorialGuinea', 'Eritrea', 'Estonia', 'Ethiopia', 'FalklandIslands', 'FederatedStatesofMicronesia', 'Fiji',\ + 'Finland', 'France', 'FrenchGuiana', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland', 'Guatemala',\ + 'Guinea', 'GuineaBissau', 'Guyana', 'Haiti', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\ + 'Ireland', 'Israel', 'Italy', 'IvoryCoast', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait',\ + 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg',\ + 'Macedonia', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'MarshallIslands', 'Mauritania',\ + 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\ + 'Nauru', 'Nepal', 'Netherlands', 'NewCaledonia', 'NewZealand', 'Nicaragua', 'Niger', 'Nigeria', 'NorthKorea', 'Norway',\ + 'Oman', 'Pakistan', 'Palau', 'Palestine', 'Panama', 'PapuaNewGuinea', 'Paraguay', 'Peru', 'Philippines', 'Poland',\ + 'Portugal', 'PuertoRico', 'Qatar', 'RepublicofCongo', 'Romania', 'Russia', 'Rwanda', 'Samoa', 'SanMarino',\ + 'SaoTomeandPrincipe', 'SaudiArabia', 'Senegal', 'Serbia', 'Seychelles', 'SierraLeone', 'Singapore', 'Slovakia',\ + 'Slovenia', 'SolomonIslands', 'Somalia', 'SouthAfrica', 'SouthKorea', 'SouthSudan', 'Spain', 'SriLanka', 'Sudan',\ + 'Suriname', 'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Tajikistan', 'Tanzania', 'Thailand', 'Togo', 'Tonga',\ + 'Tunisia', 'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine', 'UnitedArabEmirates', 'UnitedKingdom',\ + 'UnitedStates', 'Uruguay', 'Uzbekistan', 'Vanuatu', 'VaticanCity', 'Venezuela', 'Vietnam', 'WesternSahara',\ + 'Yemen', 'Zambia', 'Zimbabwe'] + + +epoch = 4 if IS_CLASSES_30 else 13 +class_names = class_names_30 if IS_CLASSES_30 else class_names_194 +cwd = os.getcwd() +prefix = cwd + '/model/ssd' +data_shape = 300 +color_subtract = (123, 117, 104) +nms_thresh = 0.5 +force_nms = True +show_timer = True +thresh = 0.43 + +def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, class_names, + nms_thresh=0.5, force_nms=True, nms_topk=400): + """ + wrapper for initialize a detector + + Parameters: + ---------- + net : str + test network name + prefix : str + load model prefix + epoch : int + load model epoch + data_shape : int + resize image shape + mean_pixels : tuple (float, float, float) + mean pixel values (R, G, B) + ctx : mx.ctx + running context, mx.cpu() or mx.gpu(?) + num_class : int + number of classes + nms_thresh : float + non-maximum suppression threshold + force_nms : bool + force suppress different categories + """ + if net is not None: + net = get_symbol(net, data_shape, num_classes=len(class_names), nms_thresh=nms_thresh, + force_nms=force_nms, nms_topk=nms_topk) + detector = ImageDetector(net, prefix, epoch, data_shape, mean_pixels, class_names, ctx=ctx) + #detector = Detector(net, prefix, epoch, data_shape, mean_pixels, ctx=ctx) + return detector + +detector = get_detector(network, prefix, epoch, data_shape, color_subtract, mx.gpu(0), + class_names, nms_thresh, force_nms) + +def process_image(image_frame): + # run detection + detected_img = detector.detect_and_layover_image(image_frame, thresh, show_timer) + return detected_img + +if __name__ == '__main__': + output_at = OUTPUT_VIDEO_AT + clip1 = VideoFileClip(INPUT_VIDEO_AT) + white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!! + white_clip.write_videofile(output_at, audio=False) + \ No newline at end of file From 5a160b0b13b9d9a926b21defa172db71d5fc4e94 Mon Sep 17 00:00:00 2001 From: Prasad Date: Sat, 4 Nov 2017 15:24:29 +0530 Subject: [PATCH 03/19] Coded to generate images, XML file anad label file. --- flags/data_utils/folder_names.py | 6 + flags/data_utils/operations.py | 227 +++++++++++++++++++++++++++++ flags/data_utils/preprocess.py | 73 ++++++++++ flags/data_utils/write_xml_file.py | 42 ++++++ flags/generate_data.py | 147 +++++++++++++++++++ 5 files changed, 495 insertions(+) create mode 100644 flags/data_utils/folder_names.py create mode 100644 flags/data_utils/operations.py create mode 100644 flags/data_utils/preprocess.py create mode 100644 flags/data_utils/write_xml_file.py create mode 100644 flags/generate_data.py diff --git a/flags/data_utils/folder_names.py b/flags/data_utils/folder_names.py new file mode 100644 index 0000000..c2349dd --- /dev/null +++ b/flags/data_utils/folder_names.py @@ -0,0 +1,6 @@ +XML_FOLDER = 'Annotations' +GENERATED_DATA = 'GeneratedData' +TRAIN_FOLDER = 'Train' +VAL_FOLDER = 'Validation' +TEST_FOLDER = 'Test' +LABEL = 'Label' \ No newline at end of file diff --git a/flags/data_utils/operations.py b/flags/data_utils/operations.py new file mode 100644 index 0000000..dae6569 --- /dev/null +++ b/flags/data_utils/operations.py @@ -0,0 +1,227 @@ +import tensorflow as tf +import numpy as np +import matplotlib.image as mpimg +import cv2 +import os +import random +from math import ceil, floor, pi, sqrt + +from data_utils.write_xml_file import write_xml_file +from data_utils.folder_names import LABEL + +IMAGE_SIZE = 224 + +def tf_resize_images_with_white_bg(img, image_width, image_height, white_area_percent): + box_area = image_width * image_height + resized_area = (100 - white_area_percent) * box_area / 100.0 + resized_width = ceil(image_width * sqrt(resized_area / box_area)) + resized_height = ceil(image_height * sqrt(resized_area / box_area)) + width_start = ceil((image_width - resized_width) / 2.0) + height_start = ceil((image_height - resized_height) / 2.0) + + tf.reset_default_graph() + tf_img = tf.image.resize_images(img, (resized_height, resized_width), + tf.image.ResizeMethod.NEAREST_NEIGHBOR) + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + resized_img = sess.run(tf_img) + + return_img = np.ones((len(resized_img), image_height, image_width, 3), dtype = np.float32) + for index, r_img in enumerate(resized_img): + return_img[index, height_start : height_start + resized_height, + width_start : width_start + resized_width, :] = r_img[:, :, :] + return return_img + +def tf_rotate_images(img, angle_at): + radian = angle_at * pi / 180 + tf.reset_default_graph() + tf_rotate_img = tf.contrib.image.rotate(img, radian) + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + rotate_img = sess.run(tf_rotate_img) + return rotate_img + +def save_img_as_png(X_file_data, file_name, folder_name): + file_name_comp = file_name.split('.') + new_file_name = '{}/{}.png'.format(folder_name, '_'.join(file_name_comp[:-1])) + mpimg.imsave(new_file_name, X_file_data) + +def fetch_image_files(label_list): + image_array = [] + for file_path in label_list: + img = mpimg.imread(file_path)[:, :, :3] + image_array.append(img) + image_array = np.array(image_array) + return image_array + +def add_salt_pepper_noise(image): + row,col,ch = image.shape + s_vs_p = 0.2 + amount = 0.004 + # Salt mode + num_salt = np.ceil(amount * image.size * s_vs_p) + coords = [np.random.randint(0, i - 1, int(num_salt)) for i in image.shape] + image[coords[0], coords[1], :] = 1 + + # Pepper mode + num_pepper = np.ceil(amount* image.size * (1. - s_vs_p)) + coords = [np.random.randint(0, i - 1, int(num_pepper)) for i in image.shape] + image[coords[0], coords[1], :] = 0 + return image + +def add_gaussian_noise(image): + row, col, ch = image.shape + mean = 0 + var = 0.1 + sigma = var ** 0.5 + gauss = np.random.random((row, col, 1)).astype(np.float32) + gauss = np.concatenate((gauss, gauss, gauss), axis = 2) + noisy = cv2.addWeighted(image, 0.8, 0.2 * gauss, 0.2, 0) + return noisy + +def tf_generate_images(flag_file_names, flag_labels, bg_img_folder, save_img_folder, save_xml_folder, parameter_dict): + scale_at = parameter_dict['scale_at'] + angle_at = parameter_dict['angle_at'] + celeb_index_at = parameter_dict['celeb_index_at'] + save_index = parameter_dict['save_index'] + raw_flag_size = parameter_dict['raw_flag_size'] + no_of_files_array = parameter_dict['no_of_files_array'] + border_area = parameter_dict['border_area'] + bg_total_files = parameter_dict['bg_total_files'] + + flag_files = fetch_image_files(flag_file_names) + flag_height, flag_width = raw_flag_size + flag_size = (ceil(scale_at * flag_height), ceil(scale_at * flag_width)) + flag_files_op = tf_resize_images_with_white_bg(flag_files, flag_size[1], flag_size[0], border_area) + # Angle rotation is not giving any benefits. Hence disable it at the moment. + #flag_files_op = tf_rotate_images(flag_files_op, angle_at) + flag_file_shape = flag_files_op[0].shape + + file_index_at = 0 + current_celeb_index = celeb_index_at + current_save_index = save_index + cwd = os.getcwd() + flag_index_at = 0 + for i in range(len(no_of_files_array)): + current_celeb_index += 1 + if current_celeb_index >= bg_total_files: + current_celeb_index = 1 + celeb_file = '{}/{:06d}.png'.format(bg_img_folder, current_celeb_index) + celeb_img = mpimg.imread(celeb_file)[:, :, :3] + + no_of_files = no_of_files_array[i] + is_difficult_array, is_truncated_array, is_occluded_array = [], [], [] + boxes, image_flag_labels = [], [] + for file_index in range(no_of_files): + # Currently this supports only two images + if no_of_files == 1: + end_index_x, end_index_y = IMAGE_SIZE, IMAGE_SIZE + start_index_x, start_index_y = 0, 0 + elif file_index == 0: + end_index_x, end_index_y = floor(IMAGE_SIZE / 2.0), IMAGE_SIZE + start_index_x, start_index_y = 0, 0 + elif file_index == 1: + end_index_x, end_index_y = IMAGE_SIZE, IMAGE_SIZE + start_index_x, start_index_y = ceil(IMAGE_SIZE / 2.0), 0 + + # Truncation + should_truncate_choice = random.choice([True, False]) + # Avoiding Truncation in this case always + should_truncate_choice = False + if should_truncate_choice: + truncation_in = random.randint(0, 2) + # For x-axis + if truncation_in in [0, 2]: + truncate_percent_x = random.randint(10, 35) + is_truncate_left = random.choice([True, False]) + if is_truncate_left: + flag_start_x = floor(flag_file_shape[0] * truncate_percent_x / 100.0) + flag_end_x = flag_file_shape[0] + celeb_start_x = 0 + start_index_x + celeb_end_x = flag_end_x - flag_start_x + start_index_x + else: + flag_start_x = 0 + flag_end_x = ceil(flag_file_shape[0] * (100.0 - truncate_percent_x) / 100.0) + celeb_start_x = end_index_x - (flag_end_x - flag_start_x) + celeb_end_x = end_index_x + else: + flag_start_x, flag_end_x = 0, flag_file_shape[0] + celeb_start_x = random.randint(start_index_x, end_index_x - flag_file_shape[0]) + celeb_end_x = celeb_start_x + flag_file_shape[0] + truncate_percent_x = 0 + + # For y-axis + if truncation_in in [1, 2]: + truncate_percent_y = random.randint(10, 35) + is_truncate_top = random.choice([True, False]) + if is_truncate_top: + flag_start_y = floor(flag_file_shape[1] * truncate_percent_y / 100.0) + flag_end_y = flag_file_shape[1] + celeb_start_y = 0 + start_index_y + celeb_end_y = flag_end_y - flag_start_y + start_index_y + else: + flag_start_y = 0 + flag_end_y = ceil(flag_file_shape[1] * (100.0 - truncate_percent_y) / 100.0) + celeb_start_y = end_index_y - (flag_end_y - flag_start_y) + celeb_end_y = end_index_y + else: + flag_start_y, flag_end_y = 0, flag_file_shape[1] + celeb_start_y = random.randint(start_index_y, end_index_y - flag_file_shape[1]) + celeb_end_y = celeb_start_y + flag_file_shape[1] + truncate_percent_y = 0 + + is_difficult = (truncate_percent_x + truncate_percent_y) > 40 + is_occluded = (truncate_percent_x + truncate_percent_y) > 55 + is_truncated = True + else: + flag_start_x, flag_end_x = 0, flag_file_shape[0] + celeb_start_x = random.randint(start_index_x, end_index_x - flag_file_shape[0]) + celeb_end_x = celeb_start_x + flag_file_shape[0] + + flag_start_y, flag_end_y = 0, flag_file_shape[1] + celeb_start_y = random.randint(start_index_y, end_index_y - flag_file_shape[1]) + celeb_end_y = celeb_start_y + flag_file_shape[1] + + is_difficult = False + is_truncated = False + is_occluded = False + + celeb_img[celeb_start_x: celeb_end_x, celeb_start_y: celeb_end_y, :] = flag_files_op[flag_index_at,\ + flag_start_x : flag_end_x, flag_start_y : flag_end_y, :] + boxes.append((celeb_start_y, celeb_start_x, celeb_end_y, celeb_end_x)) + image_flag_labels.append(flag_labels[flag_index_at]) + is_difficult_array.append(is_difficult) + is_truncated_array.append(is_truncated) + is_occluded_array.append(is_occluded) + + flag_index_at += 1 + + noise_type = random.randint(0, 2) # 0: None, 1: Gaussian, 2: Pepper + if noise_type == 1: + celeb_img = add_gaussian_noise(celeb_img) + elif noise_type == 2: + celeb_img = add_salt_pepper_noise(celeb_img) + + save_location = '{}/{:06d}.png'.format(save_img_folder, current_save_index) + mpimg.imsave(save_location, celeb_img) + + img_full_path = os.path.join(cwd, save_location) + write_xml_file(boxes, image_flag_labels, celeb_img.shape, img_full_path, save_xml_folder, + is_truncated_array, is_difficult_array, is_occluded_array) + + current_save_index += 1 + return current_celeb_index, no_of_files_array, flag_labels + +def write_label_file_entries(label_entries, no_of_entries, save_folder, folder_type): + file_path = '{}/{}_{}.txt'.format(save_folder, LABEL, folder_type) + label_file = [] + label_index = 0 + with open(file_path, 'a') as file: + for entries in no_of_entries: + for entry_no in range(entries): + file.write('{}'.format(label_entries[label_index])) + if entry_no == entries - 1: + file.write('\n') + else: + file.write(',') + label_index += 1 \ No newline at end of file diff --git a/flags/data_utils/preprocess.py b/flags/data_utils/preprocess.py new file mode 100644 index 0000000..f35dede --- /dev/null +++ b/flags/data_utils/preprocess.py @@ -0,0 +1,73 @@ +import matplotlib.image as mpimg +import tensorflow as tf +import numpy as np +import os +import argparse +import shutil + +from write_xml_file import write_xml_file + +FLAG_HEIGHT = 144 +FLAG_WIDTH = 224 + +def tf_resize_images(img, image_width, image_height): + tf.reset_default_graph() + tf_img = tf.image.resize_images(img, (image_height, image_width), + tf.image.ResizeMethod.NEAREST_NEIGHBOR) + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + resized_img = sess.run(tf_img) + return resized_img + + +def base_resize_images(src_folder, dest_folder): + shutil.rmtree(dest_folder, ignore_errors = True) + os.mkdir(dest_folder) + + flag_files = os.listdir(src_folder) + resize_batch_size = 32 + for offset in range(0, len(flag_files), resize_batch_size): + batch_file_names = flag_files[offset: offset + resize_batch_size] + batch_files = [] + for file_name in batch_file_names: + file_path = os.path.join(src_folder, file_name) + img = mpimg.imread(file_path)[:, :, :3] + batch_files.append(img) + batch_files = np.array(batch_files) + + batch_files = tf_resize_images(batch_files, FLAG_WIDTH, FLAG_HEIGHT) + for file_name, file in zip(batch_file_names, batch_files): + file_path = os.path.join(dest_folder, file_name) + mpimg.imsave(file_path, file) + +def create_label_file(dest_folder): + dest_path = os.path.abspath(dest_folder) + country_name_file = os.path.join(dest_path, '..', 'class_names.txt') + + files = os.listdir(dest_path) + files = ['{}\n'.format(file.split('.')[0]) for file in files] + files.sort() + + with open(country_name_file, mode = 'w') as label_file: + for country_name in files: + label_file.write(country_name) + +def parse_args(): + # Make sure the file names are the country names preferrably without any spaces. + parser = argparse.ArgumentParser(description = 'Resize data and prepare labels') + parser.add_argument('src_folder', help = 'Where raw sized flags are present', type = str) + parser.add_argument('dest_folder', help = 'Where to store resized flags', type = str) + + parser.add_argument('--create-label-file', dest = 'create_label', + help = 'Should create label file(0=No, 1=Yes)', + default = 1, type = int) + args = parser.parse_args() + return args + +if __name__ == "__main__": + args = parse_args() + + base_resize_images(args.src_folder, args.dest_folder) + + if args.create_label == 1: + create_label_file(args.dest_folder) diff --git a/flags/data_utils/write_xml_file.py b/flags/data_utils/write_xml_file.py new file mode 100644 index 0000000..217ee8f --- /dev/null +++ b/flags/data_utils/write_xml_file.py @@ -0,0 +1,42 @@ +import os + +def write_xml_file(boxes, labels, file_size, file_path, save_folder, truncated_array, difficult_array, occluded_array): + file_name = file_path.split('/')[-1] + base_file_name = file_name.split('.')[0] + xml_file_name = '{}.xml'.format(base_file_name) + with open(os.path.join(save_folder, xml_file_name), 'w') as xml_file: + xml_file.write('\n') + xml_file.write('\t{}\n'.format(save_folder)) + xml_file.write('\t{}.png\n'.format(base_file_name)) + xml_file.write('\t{}\n'.format(file_path)) + + xml_file.write('\t\n') + xml_file.write('\t\tUnknown\n') + xml_file.write('\t\n') + + xml_file.write('\t\n') + xml_file.write('\t\t{}\n'.format(file_size[1])) + xml_file.write('\t\t{}\n'.format(file_size[0])) + xml_file.write('\t\t{}\n'.format(file_size[2])) + xml_file.write('\t\n') + xml_file.write('\t0\n') + + for box, label, truncated, difficult, occlusion in zip(boxes, labels, truncated_array, + difficult_array, occluded_array): + xml_file.write('\t\n') + xml_file.write('\t\t{}\n'.format(label)) + xml_file.write('\t\tFrontal\n') + xml_file.write('\t\t{}\n'.format('1' if truncated else '0')) + xml_file.write('\t\t{}\n'.format('1' if difficult else '0')) + xml_file.write('\t\t{}\n'.format('1' if occlusion else '0')) + + xml_file.write('\t\t\n') + xml_file.write('\t\t\t{}\n'.format(box[0])) + xml_file.write('\t\t\t{}\n'.format(box[2])) + xml_file.write('\t\t\t{}\n'.format(box[1])) + xml_file.write('\t\t\t{}\n'.format(box[3])) + xml_file.write('\t\t\n') + + xml_file.write('\t\n') + + xml_file.write('') \ No newline at end of file diff --git a/flags/generate_data.py b/flags/generate_data.py new file mode 100644 index 0000000..17d4f28 --- /dev/null +++ b/flags/generate_data.py @@ -0,0 +1,147 @@ +import argparse +from math import ceil, floor, pi, sqrt +from tqdm import tqdm +import numpy as np +import os +from sklearn.utils import shuffle +import shutil +import random + +from data_utils.operations import tf_generate_images, write_label_file_entries +from data_utils.folder_names import XML_FOLDER, GENERATED_DATA, TRAIN_FOLDER, VAL_FOLDER, TEST_FOLDER, LABEL + +# There are 202599 images in my CelebA dataset. Give this value appropriately. +CELEBA_TOTAL_FILES = 202599 # Directly hardcoded to save memory + +BATCH_SIZE = 16 + +MIN_FLAGS = 1 +MAX_FLAGS = 2 # Currently supports upto 2 Maximum flags in one image. + +BORDER_WHITE_AREA = 40 # How much percent of card should be covered with white area. + +# Dimensions of the raw flag height and width +FLAG_HEIGHT = 144 +FLAG_WIDTH = 224 + +def get_filenames_and_labels(flag_path): + flag_file_names = os.listdir(flag_path) + flag_file_names.sort() + flag_file_names = ['{}/{}'.format(flag_path, flag_file) for flag_file in flag_file_names] + + labels = list(range(len(flag_file_names))) + return flag_file_names, labels + +def generate_image_pipeline(X_files, y_data, save_folder, folder_type, bg_img_folder, + start_celeb_index, total_base_images, + scales = [0.40, 0.43, 0.46, 0.48, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85], + angles = [0], angle_repeat_ratio = [1]): + # Folder for saving generated images. + save_img_folder = '{}/{}_{}'.format(save_folder, GENERATED_DATA, folder_type) + shutil.rmtree(save_img_folder, ignore_errors = True) + os.mkdir(save_img_folder) + # Folder for saving annotation XML files. + save_xml_folder = '{}/{}_{}'.format(save_folder, XML_FOLDER, folder_type) + shutil.rmtree(save_xml_folder, ignore_errors = True) + os.mkdir(save_xml_folder) + # File for saving labels. + file_name = '{}/{}_{}.txt'.format(save_folder, LABEL, folder_type) + if os.path.exists(file_name): + os.unlink(file_name) + + # Counter indexes. + save_index = 0 # Index for maintaining saved file number of newly generated file. + celeb_index = start_celeb_index # Index for maintaining at which file index of bg_img is at currently. Loops after finishing. + scale_index = 0 # Index for at which scale position of flag image. Loops after finishing. + data_index = 0 # Index for maintaining at which flag image is currently at. Loops after finishing. + + data_samples = len(y_data) + # Calculate the number of images to generate for each angle. + angle_images = [ceil(total_base_images * ratio) for ratio in angle_repeat_ratio] + total_images = sum(angle_images) + with tqdm(total = total_images) as pbar: + # Generate total images needed at each angle. + for angle_at, images_at_angle in zip(angles, angle_images): + save_image_at = 0 + while save_image_at < images_at_angle: + # Get the scale index. + if scale_index == len(scales): + scale_index = 0 + scale_at = scales[scale_index] + scale_index += 1 + + if data_index >= data_samples: + data_index = 0 + + no_of_files_array = [] + # Keep the ability of putting multiple flag files in one image only if scaling is below 0.5. + if scale_at <= 0.5: + for batch_counter in range(min(images_at_angle - save_image_at, BATCH_SIZE)): + files_to_pick = random.randint(MIN_FLAGS, MAX_FLAGS) + no_of_files_array.append(files_to_pick) + else: + no_of_files_array = [MIN_FLAGS] * min(images_at_angle - save_image_at, BATCH_SIZE) + no_of_files = sum(no_of_files_array) + + # Collect the needed number of flag files. + if data_index + no_of_files > data_samples: + # This condition deals with in case the looping of flag files array has to be done. + batch_X_files = X_files[data_index: ] + batch_y_data = y_data[data_index: ] + data_index = no_of_files - len(batch_y_data) + batch_X_files.extend(X_files[: data_index]) + batch_y_data = np.concatenate((batch_y_data, y_data[: data_index])) + # If the data is not filled still. + if len(batch_y_data) != no_of_files: + data_index = no_of_files - len(batch_y_data) + batch_X_files.extend(X_files[: data_index]) + batch_y_data = np.concatenate((batch_y_data, y_data[: data_index])) + else: + batch_X_files = X_files[data_index: data_index + no_of_files] + batch_y_data = y_data[data_index: data_index + no_of_files] + data_index += no_of_files + + # Some check to see if required number of flags files are collected. + # Ideally, the assert condition should never fail. + assert no_of_files == len(batch_X_files), 'Length mismatch in data files' + assert no_of_files == len(batch_y_data), 'Length mismatch in label array' + + # As there are large number of parameters to pass, pass it in a dictionary. + parameter_dict = {'scale_at': scale_at, + 'angle_at': angle_at, + 'celeb_index_at': celeb_index, + 'save_index': save_index, + 'raw_flag_size': (FLAG_HEIGHT, FLAG_WIDTH), + 'no_of_files_array': no_of_files_array, + 'border_area': BORDER_WHITE_AREA, + 'bg_total_files': CELEBA_TOTAL_FILES} + + # Generate the batch of images. + celeb_index, no_of_files_array, batch_y_data = tf_generate_images(batch_X_files, batch_y_data, + bg_img_folder, save_img_folder, + save_xml_folder, parameter_dict) + + write_label_file_entries(batch_y_data, no_of_files_array, save_folder, folder_type) + save_index += len(no_of_files_array) + save_image_at += len(no_of_files_array) + pbar.update(len(no_of_files_array)) + + +def parse_args(): + parser = argparse.ArgumentParser(description = 'Resize data and prepare labels') + parser.add_argument('flag_folder', help = 'Where flags are present', type = str) + parser.add_argument('bg_img_folder', help = 'Where background images are present', type = str) + parser.add_argument('save_folder', help = 'Where the generated files are to be stored', type = str) + args = parser.parse_args() + return args + +if __name__ == "__main__": + args = parse_args() + + label_file_names, labels = get_filenames_and_labels(args.flag_folder) + label_file_names, labels = shuffle(label_file_names, labels) + + generate_image_pipeline(label_file_names, labels, args.save_folder, TRAIN_FOLDER, args.bg_img_folder, + 1, total_base_images = 120000) + generate_image_pipeline(label_file_names, labels, args.save_folder, TEST_FOLDER, args.bg_img_folder, + 190000, total_base_images = 10000) \ No newline at end of file From 7d1feadcea02f7a2407e902e7f6e7805e1c873e1 Mon Sep 17 00:00:00 2001 From: Prasad Date: Sat, 4 Nov 2017 15:32:57 +0530 Subject: [PATCH 04/19] Made some comments --- flags/data_utils/operations.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/flags/data_utils/operations.py b/flags/data_utils/operations.py index dae6569..ae7f3fe 100644 --- a/flags/data_utils/operations.py +++ b/flags/data_utils/operations.py @@ -9,9 +9,10 @@ from data_utils.write_xml_file import write_xml_file from data_utils.folder_names import LABEL -IMAGE_SIZE = 224 +IMAGE_SIZE = 224 # Input image size def tf_resize_images_with_white_bg(img, image_width, image_height, white_area_percent): + # Estimate coordinates of the image inside white border based percentage area of white border. box_area = image_width * image_height resized_area = (100 - white_area_percent) * box_area / 100.0 resized_width = ceil(image_width * sqrt(resized_area / box_area)) @@ -32,6 +33,7 @@ def tf_resize_images_with_white_bg(img, image_width, image_height, white_area_pe width_start : width_start + resized_width, :] = r_img[:, :, :] return return_img +# Rotation is not being used currently. Use this method if it shows improvement in results. def tf_rotate_images(img, angle_at): radian = angle_at * pi / 180 tf.reset_default_graph() @@ -49,14 +51,14 @@ def save_img_as_png(X_file_data, file_name, folder_name): def fetch_image_files(label_list): image_array = [] for file_path in label_list: - img = mpimg.imread(file_path)[:, :, :3] + img = mpimg.imread(file_path)[:, :, :3] # Don't include alpha channel. image_array.append(img) image_array = np.array(image_array) return image_array def add_salt_pepper_noise(image): row,col,ch = image.shape - s_vs_p = 0.2 + s_vs_p = 0.25 amount = 0.004 # Salt mode num_salt = np.ceil(amount * image.size * s_vs_p) @@ -80,6 +82,7 @@ def add_gaussian_noise(image): return noisy def tf_generate_images(flag_file_names, flag_labels, bg_img_folder, save_img_folder, save_xml_folder, parameter_dict): + # Extract the parameter values from the dictionary. scale_at = parameter_dict['scale_at'] angle_at = parameter_dict['angle_at'] celeb_index_at = parameter_dict['celeb_index_at'] @@ -89,6 +92,7 @@ def tf_generate_images(flag_file_names, flag_labels, bg_img_folder, save_img_fol border_area = parameter_dict['border_area'] bg_total_files = parameter_dict['bg_total_files'] + # Fetch the flag images and resize them. flag_files = fetch_image_files(flag_file_names) flag_height, flag_width = raw_flag_size flag_size = (ceil(scale_at * flag_height), ceil(scale_at * flag_width)) @@ -97,11 +101,13 @@ def tf_generate_images(flag_file_names, flag_labels, bg_img_folder, save_img_fol #flag_files_op = tf_rotate_images(flag_files_op, angle_at) flag_file_shape = flag_files_op[0].shape + # Various index counters file_index_at = 0 current_celeb_index = celeb_index_at current_save_index = save_index - cwd = os.getcwd() flag_index_at = 0 + + cwd = os.getcwd() for i in range(len(no_of_files_array)): current_celeb_index += 1 if current_celeb_index >= bg_total_files: From dea49c400d3895495356435d7140795207a02e42 Mon Sep 17 00:00:00 2001 From: Prasad Date: Sat, 4 Nov 2017 17:12:02 +0530 Subject: [PATCH 05/19] Coded for converting images to record and list file --- flags/data_utils/folder_names.py | 2 +- flags/generate_data.py | 2 +- flags/generate_rec.py | 55 ++++++++++ flags/rec_data_utils/flags_celeba.py | 148 +++++++++++++++++++++++++++ mxnet | 2 +- 5 files changed, 206 insertions(+), 3 deletions(-) create mode 100644 flags/generate_rec.py create mode 100644 flags/rec_data_utils/flags_celeba.py diff --git a/flags/data_utils/folder_names.py b/flags/data_utils/folder_names.py index c2349dd..8220a45 100644 --- a/flags/data_utils/folder_names.py +++ b/flags/data_utils/folder_names.py @@ -1,6 +1,6 @@ XML_FOLDER = 'Annotations' GENERATED_DATA = 'GeneratedData' TRAIN_FOLDER = 'Train' -VAL_FOLDER = 'Validation' +VAL_FOLDER = 'Val' TEST_FOLDER = 'Test' LABEL = 'Label' \ No newline at end of file diff --git a/flags/generate_data.py b/flags/generate_data.py index 17d4f28..4a08a1a 100644 --- a/flags/generate_data.py +++ b/flags/generate_data.py @@ -143,5 +143,5 @@ def parse_args(): generate_image_pipeline(label_file_names, labels, args.save_folder, TRAIN_FOLDER, args.bg_img_folder, 1, total_base_images = 120000) - generate_image_pipeline(label_file_names, labels, args.save_folder, TEST_FOLDER, args.bg_img_folder, + generate_image_pipeline(label_file_names, labels, args.save_folder, VAL_FOLDER, args.bg_img_folder, 190000, total_base_images = 10000) \ No newline at end of file diff --git a/flags/generate_rec.py b/flags/generate_rec.py new file mode 100644 index 0000000..cb30926 --- /dev/null +++ b/flags/generate_rec.py @@ -0,0 +1,55 @@ +from __future__ import print_function +import sys, os +import argparse +import subprocess + +from data_utils.folder_names import TRAIN_FOLDER, VAL_FOLDER, GENERATED_DATA, XML_FOLDER +from rec_data_utils.flags_celeba import FlagsCeleba + +def load_flags(image_path, annotation_path, class_name_path, shuffle = True): + flags_celeba = FlagsCeleba(image_path, annotation_path, class_name_path) + return flags_celeba + +def parse_args(): + parser = argparse.ArgumentParser(description='Prepare lists for dataset') + parser.add_argument('data_path', help = 'Give path where your image folders are present', type = str) + # Ensure that this path has GeneratedData_ folder, Annotations_ and class_names.txt + #parser.add_argument('--dataset', dest = 'dataset', help = '1 for Train, 2 for Validation', default = 1, type = int) + args = parser.parse_args() + return args + +def get_paths(dataset): + image_path, annotation_path, list_save_name = '', '', '' + # For train dataset + if dataset == 0: + image_path = os.path.join(base_path, '{}_{}'.format(GENERATED_DATA, TRAIN_FOLDER)) + annotation_path = os.path.join(base_path, '{}_{}'.format(XML_FOLDER, TRAIN_FOLDER)) + list_save_name = 'train.lst' + # For Validation dataset + elif dataset == 1: + image_path = os.path.join(base_path, '{}_{}'.format(GENERATED_DATA, VAL_FOLDER)) + annotation_path = os.path.join(base_path, '{}_{}'.format(XML_FOLDER, VAL_FOLDER)) + list_save_name = 'val.lst' + return image_path, annotation_path, list_save_name + +if __name__ == "__main__": + args = parse_args() + base_path = args.data_path + for dataset in range(2): + image_path, annotation_path, list_save_name = get_paths(dataset) + + class_name_path = os.path.join(base_path, 'class_names.txt') + lst_path = base_path + '/' + list_save_name + + db = load_flags(image_path, annotation_path, class_name_path) + db.save_imglist(lst_path, image_path) + + print("List file {} generated...".format(lst_path)) + + curr_path = os.path.abspath(os.path.dirname(__file__)) + subprocess.check_call(["python", + os.path.join(curr_path, "../mxnet/tools/im2rec.py"), + os.path.abspath(lst_path), os.path.abspath(image_path), + "--shuffle", str(1), "--pack-label", "1"]) + + print("Record file {} generated...".format(list_save_name.split('.')[0] + '.rec')) diff --git a/flags/rec_data_utils/flags_celeba.py b/flags/rec_data_utils/flags_celeba.py new file mode 100644 index 0000000..e9bdd96 --- /dev/null +++ b/flags/rec_data_utils/flags_celeba.py @@ -0,0 +1,148 @@ +from __future__ import print_function +import os +import numpy as np +import xml.etree.ElementTree as ET +import cv2 +from sklearn.utils import shuffle + +class FlagsCeleba(object): + def __init__(self, image_path, annotation_path, class_name_path, shuffle = True): + self.image_path = image_path + self.annotation_path = annotation_path + + self.classes = self.load_class_names(class_name_path) + self.num_classes = len(self.classes) + self.image_set_index = self.load_image_set_indexes(self.annotation_path, shuffle) + self.num_images = len(self.image_set_index) + self.labels = self._load_image_labels() + + @property + def cache_path(self): + """ + make a directory to store all caches + + Returns: + --------- + cache path + """ + cache_path = os.path.join(os.path.dirname(__file__), '..', 'cache') + if not os.path.exists(cache_path): + os.mkdir(cache_path) + return cache_path + + def load_class_names(self, class_name_path): + with open(class_name_path, 'r') as file: + class_names = file.readlines() + class_names = [class_name.strip() for class_name in class_names] + return class_names + + def load_image_set_indexes(self, annotation_path, should_shuffle): + index_set = os.listdir(annotation_path) + index_set = [name[:6] for name in index_set] + if should_shuffle: + index_set = shuffle(index_set) + return index_set + + def _load_image_labels(self): + """ + preprocess all ground-truths + + Returns: + ---------- + labels packed in [num_images x max_num_objects x 5] tensor + """ + temp = [] + + # load ground-truth from xml annotations + for idx in self.image_set_index: + label_file = self._label_path_from_index(idx) + tree = ET.parse(label_file) + root = tree.getroot() + size = root.find('size') + width = float(size.find('width').text) + height = float(size.find('height').text) + label = [] + + for obj in root.iter('object'): + difficult = int(obj.find('difficult').text) + cls_id = obj.find('name').text + xml_box = obj.find('bndbox') + xmin = float(xml_box.find('xmin').text) / width + ymin = float(xml_box.find('ymin').text) / height + xmax = float(xml_box.find('xmax').text) / width + ymax = float(xml_box.find('ymax').text) / height + label.append([cls_id, xmin, ymin, xmax, ymax, difficult]) + temp.append(np.array(label, dtype = np.float32)) + if len(temp) % 4000 == 0: + print("Reading at {}".format(len(temp))) + return temp + + def image_path_from_index(self, index): + """ + load image full path given specified index + pascal_voc import PascalVoc + Parameters: + ---------- + index : int + index of image requested in dataset + + Returns: + ---------- + full path of specified image + """ + image_name = self.image_set_index[index] + assert self.image_set_index is not None, "Dataset not initialized" + image_file = os.path.join(self.image_path, image_name + '.png') + assert os.path.exists(image_file), 'Path does not exist: {}'.format(image_file) + return image_file + + def label_from_index(self, index): + assert self.labels is not None, "Labels not processed" + return self.labels[index] + + def _label_path_from_index(self, index): + """ + load ground-truth of image given specified index + + Parameters: + ---------- + index : int + index of image requested in dataset + + Returns: + ---------- + object ground-truths, in format + numpy.array([id, xmin, ymin, xmax, ymax]...) + """ + label_file = os.path.join(self.annotation_path, index + '.xml') + assert os.path.exists(label_file), 'Path does not exist: {}'.format(label_file) + return label_file + + def save_imglist(self, fname=None, root=None, shuffle=False): + """ + save imglist to disk + + Parameters: + ---------- + fname : str + saved filename + """ + str_list = [] + for index in range(self.num_images): + label = self.label_from_index(index) + path = self.image_path_from_index(index) + if root: + path = os.path.relpath(path, root) + str_list.append('\t'.join([str(index), str(2), str(label.shape[1])] \ + + ["{0:.4f}".format(x) for x in label.ravel()] + [path,]) + '\n') + if str_list: + if shuffle: + import random + random.shuffle(str_list) + if not fname: + fname = self.name + '.lst' + with open(fname, 'w') as f: + for line in str_list: + f.write(line) + else: + raise RuntimeError("No image in imdb") \ No newline at end of file diff --git a/mxnet b/mxnet index 100eb88..63ac793 160000 --- a/mxnet +++ b/mxnet @@ -1 +1 @@ -Subproject commit 100eb88add1c5a18185226eebde0664cc313f557 +Subproject commit 63ac7932688a3e479006cbb0eafed59d4c6ad137 From 7045cc63fb26006ffb0446da2d6f8f5c22b0724d Mon Sep 17 00:00:00 2001 From: Prasad Date: Sat, 4 Nov 2017 17:34:28 +0530 Subject: [PATCH 06/19] Added class names and simplified input entries. --- flags/generate_data.py | 13 +++++++------ flags/generate_rec.py | 4 ++-- flags/input_data/class_names.txt | 25 +++++++++++++++++++++++++ 3 files changed, 34 insertions(+), 8 deletions(-) create mode 100644 flags/input_data/class_names.txt diff --git a/flags/generate_data.py b/flags/generate_data.py index 4a08a1a..465daa4 100644 --- a/flags/generate_data.py +++ b/flags/generate_data.py @@ -128,12 +128,13 @@ def generate_image_pipeline(X_files, y_data, save_folder, folder_type, bg_img_fo def parse_args(): - parser = argparse.ArgumentParser(description = 'Resize data and prepare labels') - parser.add_argument('flag_folder', help = 'Where flags are present', type = str) - parser.add_argument('bg_img_folder', help = 'Where background images are present', type = str) - parser.add_argument('save_folder', help = 'Where the generated files are to be stored', type = str) - args = parser.parse_args() - return args + parser = argparse.ArgumentParser(description = 'Resize data and prepare labels') + parser.add_argument('flag_folder', help = 'Where flags are present', type = str) + parser.add_argument('bg_img_folder', help = 'Where background images are present', type = str) + parser.add_argument('--save-folder', dest = 'save_folder', help = 'Where the generated files are to be stored', + default = os.path.join(os.getcwd(), 'input_data'), type = str) + args = parser.parse_args() + return args if __name__ == "__main__": args = parse_args() diff --git a/flags/generate_rec.py b/flags/generate_rec.py index cb30926..6d303b9 100644 --- a/flags/generate_rec.py +++ b/flags/generate_rec.py @@ -12,9 +12,9 @@ def load_flags(image_path, annotation_path, class_name_path, shuffle = True): def parse_args(): parser = argparse.ArgumentParser(description='Prepare lists for dataset') - parser.add_argument('data_path', help = 'Give path where your image folders are present', type = str) + parser.add_argument('--data-path', dest = 'data_path', help = 'Give path where your image folders are present', + default = os.path.join(os.getcwd(), 'input_data'), type = str) # Ensure that this path has GeneratedData_ folder, Annotations_ and class_names.txt - #parser.add_argument('--dataset', dest = 'dataset', help = '1 for Train, 2 for Validation', default = 1, type = int) args = parser.parse_args() return args diff --git a/flags/input_data/class_names.txt b/flags/input_data/class_names.txt new file mode 100644 index 0000000..5921730 --- /dev/null +++ b/flags/input_data/class_names.txt @@ -0,0 +1,25 @@ +Argentina +Australia +Bhutan +Brazil +Canada +China +Cuba +France +Germany +Greece +India +Kenya +Mexico +Norway +Portugal +SaudiArabia +SouthAfrica +SriLanka +Sweden +Thailand +Turkey +Ukraine +UnitedArabEmirates +UnitedKingdom +UnitedStates \ No newline at end of file From c39d66baad8d0a2df1f7796d20bf3658a633e105 Mon Sep 17 00:00:00 2001 From: Prasad Date: Sat, 4 Nov 2017 17:58:48 +0530 Subject: [PATCH 07/19] Fixed indentation error --- train/train_net.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/train/train_net.py b/train/train_net.py index 8419587..3c37603 100644 --- a/train/train_net.py +++ b/train/train_net.py @@ -203,15 +203,15 @@ def train_net(net, train_path, num_classes, batch_size, # check what layers mismatch with the loaded parameters exe = net.simple_bind(mx.cpu(), data=(1, 3, 300, 300), label=(1, 1, 5), grad_req='null') arg_dict = exe.arg_dict - fixed_param_names = [] + fixed_param_names = [] for k, v in arg_dict.items(): if k in args: if v.shape != args[k].shape: del args[k] logging.info("Removed %s" % k) else: - if not 'pred' in k: - fixed_param_names.append(k) + if not 'pred' in k: + fixed_param_names.append(k) elif pretrained: logger.info("Start training with {} from pretrained model {}" .format(ctx_str, pretrained)) From aa4d2030395c301e9af0ed5803201659e5970fec Mon Sep 17 00:00:00 2001 From: Prasad Date: Sun, 5 Nov 2017 15:21:24 +0530 Subject: [PATCH 08/19] Filtered code for Video detection and training --- detect/image_detector.py | 10 ++-- flags/generate_rec.py | 20 +++++-- train.py | 10 ++-- video_detection.py | 123 ++++++++++++++++----------------------- 4 files changed, 75 insertions(+), 88 deletions(-) diff --git a/detect/image_detector.py b/detect/image_detector.py index 7efd863..62f0313 100644 --- a/detect/image_detector.py +++ b/detect/image_detector.py @@ -83,7 +83,7 @@ def detect(self, det_iter, show_timer=False): for pred, _, _ in self.mod.iter_predict(det_iter): detections.append(pred[0].asnumpy()) time_elapsed = timer() - start - if show_timer and False: + if show_timer: print("Detection time for {} images: {:.4f} sec".format(num_images, time_elapsed)) for output in detections: for i in range(output.shape[0]): @@ -132,7 +132,7 @@ def plot_rects(self, img, dets, thresh=0.6): cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 4) class_name = self.classes[cls_id] - cv2.putText(img, str(cls_id), (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 4) + cv2.putText(img, class_name, (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 4) print('Class id = {}, Score = {}, Country = {}, rect = ({}, {}, {}, {})'.format(cls_id, score, class_name, xmin, ymin, xmax, ymax)) def detect_and_visualize_image(self, img, thresh=0.6, show_timer=False): @@ -173,12 +173,12 @@ def scale_and_plot_rects(self, img, dets, thresh=0.6): xmax = int(dets[i, 4] * img_shape[1]) ymax = int(dets[i, 5] * img_shape[0]) - cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 8) + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 4) class_name = self.classes[cls_id] - cv2.putText(img, str(cls_id), (xmin, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 4, (0, 0, 255), 8) + cv2.putText(img, class_name, (xmin, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 4) score_color = (0, 255, 0) if score > 0.5 else (255, 0, 0) - cv2.putText(img, '{:.3f}'.format(score), (xmax - 140, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 2, score_color, 4) + cv2.putText(img, '{:.3f}'.format(score), (xmax - 60, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 1, score_color, 1) if score < 0.5: print('Class id = {}, Score = {}, Thresh = {}'.format(cls_id, score, thresh)) diff --git a/flags/generate_rec.py b/flags/generate_rec.py index 6d303b9..8ca0851 100644 --- a/flags/generate_rec.py +++ b/flags/generate_rec.py @@ -18,7 +18,7 @@ def parse_args(): args = parser.parse_args() return args -def get_paths(dataset): +def get_paths(dataset, base_path): image_path, annotation_path, list_save_name = '', '', '' # For train dataset if dataset == 0: @@ -34,12 +34,12 @@ def get_paths(dataset): if __name__ == "__main__": args = parse_args() - base_path = args.data_path + input_data_path = args.data_path for dataset in range(2): - image_path, annotation_path, list_save_name = get_paths(dataset) + image_path, annotation_path, list_save_name = get_paths(dataset, input_data_path) - class_name_path = os.path.join(base_path, 'class_names.txt') - lst_path = base_path + '/' + list_save_name + class_name_path = os.path.join(input_data_path, 'class_names.txt') + lst_path = input_data_path + '/' + list_save_name db = load_flags(image_path, annotation_path, class_name_path) db.save_imglist(lst_path, image_path) @@ -52,4 +52,12 @@ def get_paths(dataset): os.path.abspath(lst_path), os.path.abspath(image_path), "--shuffle", str(1), "--pack-label", "1"]) - print("Record file {} generated...".format(list_save_name.split('.')[0] + '.rec')) + file_name = list_save_name.split('.')[0] + print("Record file {} generated...".format(file_name + '.rec')) + + base_path_name = os.path.join(input_data_path, file_name) + target_path = curr_path + '/../data/' + file_name + os.rename(base_path_name + '.rec', target_path + '.rec') + os.rename(base_path_name + '.idx', target_path + '.idx') + os.rename(base_path_name + '.lst', target_path + '.lst') + print("Record file moved to {}".format(target_path)) diff --git a/train.py b/train.py index 06449be..504a0d4 100644 --- a/train.py +++ b/train.py @@ -5,6 +5,10 @@ import sys from train.train_net import train_net +country_names = 'Argentina, Australia, Bhutan, Brazil, Canada, China, Cuba, France, Germany, Greece, India,\ + Kenya, Mexico, Norway, Portugal, Saudi Arabia, South Africa, Sri Lanka, Sweden, Thailand, \ + Turkey, Ukraine, United Arab Emirates, United Kingdom, United States' + def parse_args(): parser = argparse.ArgumentParser(description='Train a Single-shot detection network') parser.add_argument('--train-path', dest='train_path', help='train record to use', @@ -65,14 +69,12 @@ def parse_args(): help='log network parameters every N iters if larger than 0') parser.add_argument('--pattern', dest='monitor_pattern', type=str, default=".*", help='monitor parameter pattern, as regex') - parser.add_argument('--num-class', dest='num_class', type=int, default=20, + parser.add_argument('--num-class', dest='num_class', type=int, default=25, help='number of classes') parser.add_argument('--num-example', dest='num_example', type=int, default=16551, help='number of image examples') parser.add_argument('--class-names', dest='class_names', type=str, - default='aeroplane, bicycle, bird, boat, bottle, bus, \ - car, cat, chair, cow, diningtable, dog, horse, motorbike, \ - person, pottedplant, sheep, sofa, train, tvmonitor', + default = country_names, help='string of comma separated names, or text filename') parser.add_argument('--nms', dest='nms_thresh', type=float, default=0.45, help='non-maximum suppression threshold') diff --git a/video_detection.py b/video_detection.py index 6d0b060..f89eb5b 100644 --- a/video_detection.py +++ b/video_detection.py @@ -15,95 +15,72 @@ from multiprocessing import Queue, Pool from moviepy.editor import VideoFileClip -from IPython.display import HTML -IS_CLASSES_30 = True -INPUT_VIDEO_AT = './model/project_video.MOV' -OUTPUT_VIDEO_AT = './model/output_video.mp4' - -network = 'vgg16_reduced' -class_names_30 = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria',\ - 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\ - 'BosniaandHerzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'BurkinaFaso', 'Burundi', 'Cambodia', 'Cameroon', 'Canada'] - -class_names_194 = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria',\ - 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\ - 'BosniaandHerzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'BurkinaFaso', 'Burundi', 'Cambodia', 'Cameroon', 'Canada',\ - 'CapeVerde', 'CentralAfricanRepublic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'CostaRica', 'Croatia', 'Cuba', 'Cyprus',\ - 'CzechRepublic', 'DemocraticRepublicoftheCongo', 'Denmark', 'Djibouti', 'DominicanRepublic', 'EastTimor', 'Ecuador', 'Egypt',\ - 'ElSalvador', 'EquatorialGuinea', 'Eritrea', 'Estonia', 'Ethiopia', 'FalklandIslands', 'FederatedStatesofMicronesia', 'Fiji',\ - 'Finland', 'France', 'FrenchGuiana', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland', 'Guatemala',\ - 'Guinea', 'GuineaBissau', 'Guyana', 'Haiti', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\ - 'Ireland', 'Israel', 'Italy', 'IvoryCoast', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait',\ - 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg',\ - 'Macedonia', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'MarshallIslands', 'Mauritania',\ - 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\ - 'Nauru', 'Nepal', 'Netherlands', 'NewCaledonia', 'NewZealand', 'Nicaragua', 'Niger', 'Nigeria', 'NorthKorea', 'Norway',\ - 'Oman', 'Pakistan', 'Palau', 'Palestine', 'Panama', 'PapuaNewGuinea', 'Paraguay', 'Peru', 'Philippines', 'Poland',\ - 'Portugal', 'PuertoRico', 'Qatar', 'RepublicofCongo', 'Romania', 'Russia', 'Rwanda', 'Samoa', 'SanMarino',\ - 'SaoTomeandPrincipe', 'SaudiArabia', 'Senegal', 'Serbia', 'Seychelles', 'SierraLeone', 'Singapore', 'Slovakia',\ - 'Slovenia', 'SolomonIslands', 'Somalia', 'SouthAfrica', 'SouthKorea', 'SouthSudan', 'Spain', 'SriLanka', 'Sudan',\ - 'Suriname', 'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Tajikistan', 'Tanzania', 'Thailand', 'Togo', 'Tonga',\ - 'Tunisia', 'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine', 'UnitedArabEmirates', 'UnitedKingdom',\ - 'UnitedStates', 'Uruguay', 'Uzbekistan', 'Vanuatu', 'VaticanCity', 'Venezuela', 'Vietnam', 'WesternSahara',\ - 'Yemen', 'Zambia', 'Zimbabwe'] - - -epoch = 4 if IS_CLASSES_30 else 13 -class_names = class_names_30 if IS_CLASSES_30 else class_names_194 -cwd = os.getcwd() -prefix = cwd + '/model/ssd' -data_shape = 300 -color_subtract = (123, 117, 104) -nms_thresh = 0.5 -force_nms = True -show_timer = True -thresh = 0.43 +class_names = 'Argentina, Australia, Bhutan, Brazil, Canada, China, Cuba, France, Germany, Greece, India, \ + Kenya, Mexico, Norway, Portugal, Saudi Arabia, South Africa, Sri Lanka, Sweden, Thailand, \ + Turkey, Ukraine, U.A.E., U.K., U.S.A.' +detector = None +thresh = None def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, class_names, nms_thresh=0.5, force_nms=True, nms_topk=400): - """ - wrapper for initialize a detector - Parameters: - ---------- - net : str - test network name - prefix : str - load model prefix - epoch : int - load model epoch - data_shape : int - resize image shape - mean_pixels : tuple (float, float, float) - mean pixel values (R, G, B) - ctx : mx.ctx - running context, mx.cpu() or mx.gpu(?) - num_class : int - number of classes - nms_thresh : float - non-maximum suppression threshold - force_nms : bool - force suppress different categories - """ if net is not None: net = get_symbol(net, data_shape, num_classes=len(class_names), nms_thresh=nms_thresh, force_nms=force_nms, nms_topk=nms_topk) detector = ImageDetector(net, prefix, epoch, data_shape, mean_pixels, class_names, ctx=ctx) - #detector = Detector(net, prefix, epoch, data_shape, mean_pixels, ctx=ctx) return detector -detector = get_detector(network, prefix, epoch, data_shape, color_subtract, mx.gpu(0), - class_names, nms_thresh, force_nms) - def process_image(image_frame): # run detection - detected_img = detector.detect_and_layover_image(image_frame, thresh, show_timer) + detected_img = detector.detect_and_layover_image(image_frame, thresh, False) return detected_img +def parse_args(): + parser = argparse.ArgumentParser(description='Detect objects in the video') + parser.add_argument('video_path', help = 'Where video is present', type = str) + parser.add_argument('--network', dest='network', type=str, default='vgg16_reduced', + help='which network to use') + parser.add_argument('--epoch', dest='epoch', help='epoch of pretrained model', + default=1, type=int) + parser.add_argument('--prefix', dest='prefix', help='Trained model prefix', + default=os.path.join(os.getcwd(), 'model', 'ssd'), type=str) + parser.add_argument('--thresh', dest='thresh', help='Threshold of confidence level', + default=0.43, type=float) + parser.add_argument('--nms', dest='nms_thresh', type=float, default=0.45, + help='non-maximum suppression threshold') + parser.add_argument('--mean-r', dest='mean_r', type=float, default=123, + help='red mean value') + parser.add_argument('--mean-g', dest='mean_g', type=float, default=117, + help='green mean value') + parser.add_argument('--mean-b', dest='mean_b', type=float, default=104, + help='blue mean value') + parser.add_argument('--data-shape', dest='data_shape', type=int, default=300, + help='set image shape') + parser.add_argument('--class-names', dest='class_names', type=str, + default = class_names, help='string of comma separated names') + parser.add_argument('--force', dest='force_nms', type=bool, default=True, + help='force non-maximum suppression on different class') + parser.add_argument('--has-gpu', dest='gpu', help='GPU device 1 if present else 0', + default=1, type=int) + args = parser.parse_args() + return args + if __name__ == '__main__': - output_at = OUTPUT_VIDEO_AT - clip1 = VideoFileClip(INPUT_VIDEO_AT) + global detector, thresh + args = parse_args() + + color_subtract = (args.mean_r, args.mean_g, args.mean_b) + ctx = mx.gpu(0) if args.gpu == 1 else mx.cpu(0) + class_names = [class_name.strip() for class_name in args.class_names.split(',')] + detector = get_detector(args.network, args.prefix, args.epoch, args.data_shape, color_subtract, ctx, + class_names, args.nms_thresh, args.force_nms) + thresh = args.thresh + + video_path_comp = args.video_path.split('.') + output_at = video_path_comp[0] + '_output.' + video_path_comp[1] + clip1 = VideoFileClip(args.video_path) + white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!! white_clip.write_videofile(output_at, audio=False) \ No newline at end of file From 3749db848f1a2112916889840f27dcf043f86048 Mon Sep 17 00:00:00 2001 From: Prasad Date: Sun, 5 Nov 2017 18:44:26 +0530 Subject: [PATCH 09/19] Streamlined code for object detection app and objection detectio in images --- detect/image_detector.py | 38 ++--- video_detection.py => object_detection.py | 52 +++--- object_detection_app.py | 186 ++++++---------------- utils.py | 32 ---- 4 files changed, 101 insertions(+), 207 deletions(-) rename video_detection.py => object_detection.py (66%) diff --git a/detect/image_detector.py b/detect/image_detector.py index 62f0313..f7194f8 100644 --- a/detect/image_detector.py +++ b/detect/image_detector.py @@ -27,7 +27,7 @@ class ImageDetector(object): device to use, if None, use mx.cpu() as default context """ def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \ - classes, batch_size=1, ctx=None): + classes, thresh = 0.6, plot_confidence = True, batch_size=1, ctx=None): self.ctx = ctx if self.ctx is None: self.ctx = mx.cpu() @@ -42,21 +42,23 @@ def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \ self.mean_pixels = mean_pixels self.classes = classes self.colors = [] - self.fill_random_colors() + self.fill_random_colors_int() + self.thresh = thresh + self.plot_confidence = plot_confidence def fill_random_colors(self): import random for i in range(len(self.classes)): self.colors.append((random.random(), random.random(), random.random())) - print(self.colors) + #print(self.colors) def fill_random_colors_int(self): import random for i in range(len(self.classes)): self.colors.append((random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))) - print(self.colors) + #print(self.colors) def detect(self, det_iter, show_timer=False): @@ -116,14 +118,14 @@ def im_detect(self, img, show_timer=False): test_iter = DetTestImageIter(im_list, 1, self.data_shape, self.mean_pixels) return self.detect(test_iter, show_timer) - def plot_rects(self, img, dets, thresh=0.6): + def plot_rects(self, img, dets): img_shape = img.shape for i in range(dets.shape[0]): cls_id = int(dets[i, 0]) if cls_id >= 0: score = dets[i, 1] #print('Score is {}, class {}'.format(score, cls_id)) - if score > thresh: + if score > self.thresh: xmin = int(dets[i, 2] * img_shape[1]) ymin = int(dets[i, 3] * img_shape[0]) xmax = int(dets[i, 4] * img_shape[1]) @@ -133,9 +135,9 @@ def plot_rects(self, img, dets, thresh=0.6): class_name = self.classes[cls_id] cv2.putText(img, class_name, (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 4) - print('Class id = {}, Score = {}, Country = {}, rect = ({}, {}, {}, {})'.format(cls_id, score, class_name, xmin, ymin, xmax, ymax)) + #print('Class id = {}, Score = {}, Country = {}, rect = ({}, {}, {}, {})'.format(cls_id, score, class_name, xmin, ymin, xmax, ymax)) - def detect_and_visualize_image(self, img, thresh=0.6, show_timer=False): + def detect_and_visualize_image(self, img, show_timer=False): """ wrapper for im_detect and visualize_detection @@ -157,17 +159,17 @@ def detect_and_visualize_image(self, img, thresh=0.6, show_timer=False): resized_img = resized_img.asnumpy() resized_img /= 255.0 for k, det in enumerate(dets): - self.plot_rects(resized_img, det, thresh) + self.plot_rects(resized_img, det) return resized_img - def scale_and_plot_rects(self, img, dets, thresh=0.6): + def scale_and_plot_rects(self, img, dets): img_shape = img.shape for i in range(dets.shape[0]): cls_id = int(dets[i, 0]) if cls_id >= 0: score = dets[i, 1] #print('Score is {}, class {}'.format(score, cls_id)) - if score > thresh: + if score > self.thresh: xmin = int(dets[i, 2] * img_shape[1]) ymin = int(dets[i, 3] * img_shape[0]) xmax = int(dets[i, 4] * img_shape[1]) @@ -176,13 +178,13 @@ def scale_and_plot_rects(self, img, dets, thresh=0.6): cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 4) class_name = self.classes[cls_id] - cv2.putText(img, class_name, (xmin, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 4) - score_color = (0, 255, 0) if score > 0.5 else (255, 0, 0) - cv2.putText(img, '{:.3f}'.format(score), (xmax - 60, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 1, score_color, 1) - if score < 0.5: - print('Class id = {}, Score = {}, Thresh = {}'.format(cls_id, score, thresh)) + cv2.putText(img, class_name, (xmin, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 255), 3) + if self.plot_confidence: + score_color = (0, 255, 0) if score > 0.5 else (255, 0, 0) + cv2.putText(img, '{:.3f}'.format(score), (xmax - 60, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 1, score_color, 1) + - def detect_and_layover_image(self, img, thresh=0.6, show_timer=False): + def detect_and_layover_image(self, img, show_timer=False): """ wrapper for im_detect and visualize_detection @@ -202,5 +204,5 @@ def detect_and_layover_image(self, img, thresh=0.6, show_timer=False): """ dets, _ = self.im_detect(img, show_timer=show_timer) for k, det in enumerate(dets): - self.scale_and_plot_rects(img, det, thresh) + self.scale_and_plot_rects(img, det) return img diff --git a/video_detection.py b/object_detection.py similarity index 66% rename from video_detection.py rename to object_detection.py index f89eb5b..adf4a78 100644 --- a/video_detection.py +++ b/object_detection.py @@ -7,38 +7,36 @@ import tools.find_mxnet import mxnet as mx import sys + from detect.image_detector import ImageDetector -from detect.detector import Detector from symbol.symbol_factory import get_symbol -from utils import FPS, WebcamVideoStream -from multiprocessing import Queue, Pool - from moviepy.editor import VideoFileClip +from tqdm import tqdm class_names = 'Argentina, Australia, Bhutan, Brazil, Canada, China, Cuba, France, Germany, Greece, India, \ Kenya, Mexico, Norway, Portugal, Saudi Arabia, South Africa, Sri Lanka, Sweden, Thailand, \ Turkey, Ukraine, U.A.E., U.K., U.S.A.' detector = None -thresh = None -def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, class_names, +def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, class_names, thresh, plot_confidence, nms_thresh=0.5, force_nms=True, nms_topk=400): if net is not None: net = get_symbol(net, data_shape, num_classes=len(class_names), nms_thresh=nms_thresh, force_nms=force_nms, nms_topk=nms_topk) - detector = ImageDetector(net, prefix, epoch, data_shape, mean_pixels, class_names, ctx=ctx) + detector = ImageDetector(net, prefix, epoch, data_shape, mean_pixels, class_names, thresh,\ + plot_confidence, ctx=ctx) return detector def process_image(image_frame): # run detection - detected_img = detector.detect_and_layover_image(image_frame, thresh, False) + detected_img = detector.detect_and_layover_image(image_frame, False) return detected_img def parse_args(): - parser = argparse.ArgumentParser(description='Detect objects in the video') - parser.add_argument('video_path', help = 'Where video is present', type = str) + parser = argparse.ArgumentParser(description='Detect objects in the video or still images') + parser.add_argument('data_path', help = 'Path of video or folder containing images', type = str) parser.add_argument('--network', dest='network', type=str, default='vgg16_reduced', help='which network to use') parser.add_argument('--epoch', dest='epoch', help='epoch of pretrained model', @@ -47,6 +45,8 @@ def parse_args(): default=os.path.join(os.getcwd(), 'model', 'ssd'), type=str) parser.add_argument('--thresh', dest='thresh', help='Threshold of confidence level', default=0.43, type=float) + parser.add_argument('--plot-prob', dest='plot_prob', help='Should probabilities be printed. (1 = Yes, 0 = No)', + default=1, type=int) parser.add_argument('--nms', dest='nms_thresh', type=float, default=0.45, help='non-maximum suppression threshold') parser.add_argument('--mean-r', dest='mean_r', type=float, default=123, @@ -67,20 +67,34 @@ def parse_args(): return args if __name__ == '__main__': - global detector, thresh args = parse_args() color_subtract = (args.mean_r, args.mean_g, args.mean_b) ctx = mx.gpu(0) if args.gpu == 1 else mx.cpu(0) class_names = [class_name.strip() for class_name in args.class_names.split(',')] detector = get_detector(args.network, args.prefix, args.epoch, args.data_shape, color_subtract, ctx, - class_names, args.nms_thresh, args.force_nms) - thresh = args.thresh + class_names, args.thresh, args.plot_prob, args.nms_thresh, args.force_nms) + + data_path = os.path.abspath(args.data_path) + # For image processing + if os.path.isdir(data_path): + file_paths = os.listdir(data_path) + file_paths = [os.path.join(data_path, file_path) for file_path in file_paths] + + with tqdm(total = len(file_paths)) as pbar: + for file_path in file_paths: + image = cv2.imread(file_path, cv2.COLOR_BGR2RGB) + detected_image = process_image(image) + file_path_comp = file_path.split('.') + cv2.imwrite('{}_output.{}'.format(file_path_comp[0], file_path_comp[1]), detected_image) - video_path_comp = args.video_path.split('.') - output_at = video_path_comp[0] + '_output.' + video_path_comp[1] - clip1 = VideoFileClip(args.video_path) + pbar.update(1) - white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!! - white_clip.write_videofile(output_at, audio=False) - \ No newline at end of file + # For video processing + else: + video_path_comp = data_path.split('.') + output_at = video_path_comp[0] + '_output.' + video_path_comp[1] + clip1 = VideoFileClip(data_path) + + white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!! + white_clip.write_videofile(output_at, audio=False) \ No newline at end of file diff --git a/object_detection_app.py b/object_detection_app.py index 71df995..4dc6ba4 100644 --- a/object_detection_app.py +++ b/object_detection_app.py @@ -11,177 +11,87 @@ from detect.detector import Detector from symbol.symbol_factory import get_symbol -from utils import FPS, WebcamVideoStream -from multiprocessing import Queue, Pool +from utils import WebcamVideoStream -network = 'vgg16_reduced' -class_names = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria',\ - 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia',\ - 'BosniaandHerzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'BurkinaFaso', 'Burundi', 'Cambodia', 'Cameroon', 'Canada',\ - 'CapeVerde', 'CentralAfricanRepublic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'CostaRica', 'Croatia', 'Cuba', 'Cyprus',\ - 'CzechRepublic', 'DemocraticRepublicoftheCongo', 'Denmark', 'Djibouti', 'DominicanRepublic', 'EastTimor', 'Ecuador', 'Egypt',\ - 'ElSalvador', 'EquatorialGuinea', 'Eritrea', 'Estonia', 'Ethiopia', 'FalklandIslands', 'FederatedStatesofMicronesia', 'Fiji',\ - 'Finland', 'France', 'FrenchGuiana', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland', 'Guatemala',\ - 'Guinea', 'GuineaBissau', 'Guyana', 'Haiti', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq',\ - 'Ireland', 'Israel', 'Italy', 'IvoryCoast', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati', 'Kuwait',\ - 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg',\ - 'Macedonia', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'MarshallIslands', 'Mauritania',\ - 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\ - 'Nauru', 'Nepal', 'Netherlands', 'NewCaledonia', 'NewZealand', 'Nicaragua', 'Niger', 'Nigeria', 'NorthKorea', 'Norway',\ - 'Oman', 'Pakistan', 'Palau', 'Palestine', 'Panama', 'PapuaNewGuinea', 'Paraguay', 'Peru', 'Philippines', 'Poland',\ - 'Portugal', 'PuertoRico', 'Qatar', 'RepublicofCongo', 'Romania', 'Russia', 'Rwanda', 'Samoa', 'SanMarino',\ - 'SaoTomeandPrincipe', 'SaudiArabia', 'Senegal', 'Serbia', 'Seychelles', 'SierraLeone', 'Singapore', 'Slovakia',\ - 'Slovenia', 'SolomonIslands', 'Somalia', 'SouthAfrica', 'SouthKorea', 'SouthSudan', 'Spain', 'SriLanka', 'Sudan',\ - 'Suriname', 'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Tajikistan', 'Tanzania', 'Thailand', 'Togo', 'Tonga',\ - 'Tunisia', 'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine', 'UnitedArabEmirates', 'UnitedKingdom',\ - 'UnitedStates', 'Uruguay', 'Uzbekistan', 'Vanuatu', 'VaticanCity', 'Venezuela', 'Vietnam', 'WesternSahara',\ - 'Yemen', 'Zambia', 'Zimbabwe'] +class_names = 'Argentina, Australia, Bhutan, Brazil, Canada, China, Cuba, France, Germany, Greece, India, \ + Kenya, Mexico, Norway, Portugal, Saudi Arabia, South Africa, Sri Lanka, Sweden, Thailand, \ + Turkey, Ukraine, U.A.E., U.K., U.S.A.' +detector = None -class_names = ['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Argentina', 'Armenia', 'Australia', 'Austria',\ - 'Azerbaijan'] -epoch = 6 -cwd = os.getcwd() -prefix = cwd + '/model/ssd' -data_shape = 300 -color_subtract = (123, 117, 104) -nms_thresh = 0.5 -force_nms = True -show_timer = True -thresh = 0.5 - -def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, class_names, +def get_detector(net, prefix, epoch, data_shape, mean_pixels, ctx, class_names, thresh, plot_confidence, nms_thresh=0.5, force_nms=True, nms_topk=400): - """ - wrapper for initialize a detector - Parameters: - ---------- - net : str - test network name - prefix : str - load model prefix - epoch : int - load model epoch - data_shape : int - resize image shape - mean_pixels : tuple (float, float, float) - mean pixel values (R, G, B) - ctx : mx.ctx - running context, mx.cpu() or mx.gpu(?) - num_class : int - number of classes - nms_thresh : float - non-maximum suppression threshold - force_nms : bool - force suppress different categories - """ if net is not None: net = get_symbol(net, data_shape, num_classes=len(class_names), nms_thresh=nms_thresh, force_nms=force_nms, nms_topk=nms_topk) - detector = ImageDetector(net, prefix, epoch, data_shape, mean_pixels, class_names, ctx=ctx) - #detector = Detector(net, prefix, epoch, data_shape, mean_pixels, ctx=ctx) + detector = ImageDetector(net, prefix, epoch, data_shape, mean_pixels, class_names, thresh,\ + plot_confidence, ctx=ctx) return detector -detector = get_detector(network, prefix, epoch, data_shape, color_subtract, mx.gpu(0), - class_names, nms_thresh, force_nms) - def process_image(image_frame): # run detection - detected_img = detector.detect_and_visualize_image(image_frame, thresh, show_timer) - #detected_img = detector.detect_and_layover_image(image_frame, thresh, show_timer) + detected_img = detector.detect_and_layover_image(image_frame, False) return detected_img -def worker(input_q, output_q): - fps = FPS().start() - while True: - fps.update() - frame = input_q.get() - #frame = np.transpose(frame, (2, 0, 1)) - #output_q.put(detect_objects(frame, sess, detection_graph)) - detected_img = process_image(frame) - output_q.put(detected_img) - - fps.stop() +def parse_args(): + parser = argparse.ArgumentParser(description='Detect objects in the live video') + parser.add_argument('--network', dest='network', type=str, default='vgg16_reduced', + help='which network to use') + parser.add_argument('--epoch', dest='epoch', help='epoch of pretrained model', + default=1, type=int) + parser.add_argument('--prefix', dest='prefix', help='Trained model prefix', + default=os.path.join(os.getcwd(), 'model', 'ssd'), type=str) + parser.add_argument('--thresh', dest='thresh', help='Threshold of confidence level', + default=0.43, type=float) + parser.add_argument('--plot-prob', dest='plot_prob', help='Should probabilities be printed. (1 = Yes, 0 = No)', + default=1, type=int) + parser.add_argument('--nms', dest='nms_thresh', type=float, default=0.45, + help='non-maximum suppression threshold') + parser.add_argument('--mean-r', dest='mean_r', type=float, default=123, + help='red mean value') + parser.add_argument('--mean-g', dest='mean_g', type=float, default=117, + help='green mean value') + parser.add_argument('--mean-b', dest='mean_b', type=float, default=104, + help='blue mean value') + parser.add_argument('--data-shape', dest='data_shape', type=int, default=300, + help='set image shape') + parser.add_argument('--class-names', dest='class_names', type=str, + default = class_names, help='string of comma separated names') + parser.add_argument('--force', dest='force_nms', type=bool, default=True, + help='force non-maximum suppression on different class') + parser.add_argument('--has-gpu', dest='gpu', help='GPU device 1 if present else 0', + default=1, type=int) -if __name__ == '__main__': - parser = argparse.ArgumentParser() parser.add_argument('-src', '--source', dest='video_source', type=int, default=0, help='Device index of the camera.') parser.add_argument('-wd', '--width', dest='width', type=int, default=480, help='Width of the frames in the video stream.') parser.add_argument('-ht', '--height', dest='height', type=int, - default=360, help='Height of the frames in the video stream.') - parser.add_argument('-num-w', '--num-workers', dest='num_workers', type=int, - default=1, help='Number of workers.') - parser.add_argument('-q-size', '--queue-size', dest='queue_size', type=int, - default=5, help='Size of the queue.') + default=640, help='Height of the frames in the video stream.') args = parser.parse_args() + return args + +if __name__ == '__main__': + args = parse_args() - #time.sleep(1) + color_subtract = (args.mean_r, args.mean_g, args.mean_b) + ctx = mx.gpu(0) if args.gpu == 1 else mx.cpu(0) + class_names = [class_name.strip() for class_name in args.class_names.split(',')] + detector = get_detector(args.network, args.prefix, args.epoch, args.data_shape, color_subtract, ctx, + class_names, args.thresh, args.plot_prob, args.nms_thresh, args.force_nms) - #''' video_capture = WebcamVideoStream(src=args.video_source, width=args.width, height=args.height).start() - fps = FPS().start() - while True: # fps._numFrames < 120 + while True: frame = video_capture.read() detected_img = process_image(frame) - - t = time.time() - cv2.imshow('Video', detected_img) - fps.update() - - #print('[INFO] elapsed time: {:.2f}'.format(time.time() - t)) if cv2.waitKey(1) & 0xFF == ord('q'): break - - - fps.stop() - #print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) - #print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) video_capture.stop() cv2.destroyAllWindows() - #''' - - ''' - logger = multiprocessing.log_to_stderr() - #logger.setLevel(multiprocessing.SUBDEBUG) - - input_q = Queue(maxsize=args.queue_size) - output_q = Queue(maxsize=args.queue_size) - pool = Pool(args.num_workers, worker, (input_q, output_q)) - - video_capture = WebcamVideoStream(src=args.video_source, - width=args.width, - height=args.height).start() - fps = FPS().start() - - while True: # fps._numFrames < 120 - frame = video_capture.read() - input_q.put(frame) - - t = time.time() - - cv2.imshow('Video', output_q.get()) - fps.update() - - #print('[INFO] elapsed time: {:.2f}'.format(time.time() - t)) - - if cv2.waitKey(1) & 0xFF == ord('q'): - break - - - fps.stop() - #print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed())) - #print('[INFO] approx. FPS: {:.2f}'.format(fps.fps())) - - pool.terminate() - video_capture.stop() - cv2.destroyAllWindows() - ''' diff --git a/utils.py b/utils.py index c1f2a58..8814772 100644 --- a/utils.py +++ b/utils.py @@ -1,40 +1,8 @@ # From http://www.pyimagesearch.com/2015/12/21/increasing-webcam-fps-with-python-and-opencv/ import cv2 -import datetime from threading import Thread -class FPS: - def __init__(self): - # store the start time, end time, and total number of frames - # that were examined between the start and end intervals - self._start = None - self._end = None - self._numFrames = 0 - - def start(self): - # start the timer - self._start = datetime.datetime.now() - return self - - def stop(self): - # stop the timer - self._end = datetime.datetime.now() - - def update(self): - # increment the total number of frames examined during the - # start and end intervals - self._numFrames += 1 - - def elapsed(self): - # return the total number of seconds between the start and - # end interval - return (self._end - self._start).total_seconds() - - def fps(self): - # compute the (approximate) frames per second - return self._numFrames / self.elapsed() - class WebcamVideoStream: def __init__(self, src, width, height): # initialize the video camera stream and read the first frame From f8744b152975aa09cf1fbf09652d0438f8ddbf2b Mon Sep 17 00:00:00 2001 From: Prasad Date: Mon, 6 Nov 2017 20:45:02 +0530 Subject: [PATCH 10/19] Updated ReadMe and added demo images, video. --- README.md | 191 +++++++++---------------------- flags/demo_data/images/demo1.jpg | Bin 0 -> 46473 bytes flags/demo_data/images/demo2.jpg | Bin 0 -> 52020 bytes flags/demo_data/images/demo3.jpg | Bin 0 -> 45921 bytes flags/demo_data/video/demo.mp4 | Bin 0 -> 1644685 bytes 5 files changed, 55 insertions(+), 136 deletions(-) create mode 100644 flags/demo_data/images/demo1.jpg create mode 100644 flags/demo_data/images/demo2.jpg create mode 100644 flags/demo_data/images/demo3.jpg create mode 100644 flags/demo_data/video/demo.mp4 diff --git a/README.md b/README.md index ff7485d..7468b8d 100644 --- a/README.md +++ b/README.md @@ -1,60 +1,16 @@ -# SSD: Single Shot MultiBox Object Detector - -SSD is an unified framework for object detection with a single network. - -You can use the code to train/evaluate/test for object detection task. - -### Disclaimer -This is a re-implementation of original SSD which is based on caffe. The official -repository is available [here](https://github.com/weiliu89/caffe/tree/ssd). -The arXiv paper is available [here](http://arxiv.org/abs/1512.02325). - -This example is intended for reproducing the nice detector while fully utilize the -remarkable traits of MXNet. -* The model is fully compatible with caffe version. -* Model [converter](#convert-caffemodel) from caffe is available now! -* The result is almost identical to the original version. However, due to different implementation details, the results might differ slightly. - -### What's new -* Now this repo is internally synchronized up to data with offical mxnet backend. `pip install mxnet` will work for this repo as well in most cases. -* MobileNet pretrained model now provided. -* Added multiple trained models. -* Added a much simpler way to compose network from mainstream classification networks (resnet, inception...) and [Guide](symbol/README.md). -* Update to the latest version according to caffe version, with 5% mAP increase. -* Use C++ record iterator based on back-end multi-thread engine to achieve huge speed up on multi-gpu environments. -* Monitor validation mAP during training. -* More network symbols under development and test. -* Extra operators are now in `mxnet/src/operator/contrib`, symbols are modified. Please use [Release-v0.2-beta](https://github.com/zhreshold/mxnet-ssd/releases/tag/v0.2-beta) for old models. - -### Demo results -![demo1](https://cloud.githubusercontent.com/assets/3307514/19171057/8e1a0cc4-8be0-11e6-9d8f-088c25353b40.png) -![demo2](https://cloud.githubusercontent.com/assets/3307514/19171063/91ec2792-8be0-11e6-983c-773bd6868fa8.png) -![demo3](https://cloud.githubusercontent.com/assets/3307514/19171086/a9346842-8be0-11e6-8011-c17716b22ad3.png) - -### mAP -| Model | Training data | Test data | mAP | Note | -|:-----------------:|:----------------:|:---------:|:----:|:-----| -| [VGG16_reduced 300x300](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.5-beta/vgg16_ssd_300_voc0712_trainval.zip) | VOC07+12 trainval| VOC07 test| 77.8| fast | -| [VGG16_reduced 512x512](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.5-beta/vgg16_ssd_512_voc0712_trainval.zip) | VOC07+12 trainval | VOC07 test| 79.9| slow | -| [Inception-v3 512x512](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.6/inceptionv3_ssd_512_voc0712_trainval.zip) | VOC07+12 trainval| VOC07 test| 78.9 | fastest | -| [Resnet-50 512x512](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.6/resnet50_ssd_512_voc0712_trainval.zip) | VOC07+12 trainval| VOC07 test| 78.9 | fast | -| [MobileNet 512x512](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.7-alpha/mobilenet-ssd-512.zip) | VOC07+12 trainval| VOC07 test| 72.5 | super fast | -| [MobileNet 608x608](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.7-alpha/mobilenet-ssd-608.zip) | VOC07+12 trainval| VOC07 test| 74.7 | super fast | - - -*More to be added* - -### Speed -| Model | GPU | CUDNN | Batch-size | FPS* | -|:---------------------:|:----------------:|:-----:|:----------:|:----:| -| VGG16_reduced 300x300 | TITAN X(Maxwell) | v5.1 | 16 | 95 | -| VGG16_reduced 300x300 | TITAN X(Maxwell) | v5.1 | 8 | 95 | -| VGG16_reduced 300x300 | TITAN X(Maxwell) | v5.1 | 1 | 64 | -| VGG16_reduced 300x300 | TITAN X(Maxwell) | N/A | 8 | 36 | -| VGG16_reduced 300x300 | TITAN X(Maxwell) | N/A | 1 | 28 | - -*Forward time only, data loading and drawing excluded.* +# SSD based Object Detection for Country Flags +This is an example of Object Detection done for country flags making use of SSD Network in MXNet Framework. + +You can read the detailed post about the approach used in this project in my [Medium post](--------------). + +This repository is the forked version of [Zhreshold's MXNet-SSD](https://github.com/zhreshold/mxnet-ssd) which is a generic version of MXNet-SSD and some of the instructions of installation are copied from there. + +The trained network at present runs for detecting 25 country flags printed on rectangular placards. The countries' flags used are of Argentina, Australia, Bhutan, Brazil, Canada, China, Cuba, France, Germany, Greece, India, Kenya, Mexico, Norway, Portugal, Saudi Arabia, South Africa, Sri Lanka, Sweden, Thailand, Turkey, Ukraine, United Arab Emirates, United Kingdom and United States of America. The evaluation of the trained network can be done on still images or a pre-recorded video or even a live video feed. This is the result of the network trained using VGG model. + +![Detected Result](https://user-images.githubusercontent.com/13696749/32447111-13a44b6a-c331-11e7-9968-9c10343d3e31.png) + +## Instructions to run the code ### Getting started * You will need python modules: `cv2`, `matplotlib` and `numpy`. If you use mxnet-python api, you probably have already got them. @@ -68,7 +24,7 @@ sudo apt-get install python-opencv python-matplotlib python-numpy sudo apt-get install git # cd where you would like to clone this repo cd ~ -git clone --recursive https://github.com/zhreshold/mxnet-ssd.git +git clone --recursive https://github.com/Prasad9/mxnet-ssd.git # make sure you clone this with --recursive # if not done correctly or you are using downloaded repo, pull them all via: # git submodule update --recursive --init @@ -80,102 +36,65 @@ cd mxnet-ssd/mxnet cp make/config.mk ./config.mk # modify it if necessary ``` -Remember to enable CUDA if you want to be able to train, since CPU training is -insanely slow. Using CUDNN is optional, but highly recommanded. - -### Try the demo -* Download the pretrained model: [`ssd_resnet50_0712.zip`](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.6/resnet50_ssd_512_voc0712_trainval.zip), and extract to `model/` directory. -* Run -``` -# cd /path/to/mxnet-ssd -python demo.py --gpu 0 -# play with examples: -python demo.py --epoch 0 --images ./data/demo/dog.jpg --thresh 0.5 -python demo.py --cpu --network resnet50 --data-shape 512 -# wait for library to load for the first time + +### Switch to `Flag25` branch +The code has been written in `Flag25` branch. So please checkout that branch using: ``` -* Check `python demo.py --help` for more options. +git checkout -b Flag25 +git pull origin Flag25 +``` +The code to generate the specific type of data needed to produce results as shown in introduction above is present in `flags` folder. -### Train the model -This example only covers training on Pascal VOC dataset. Other datasets should -be easily supported by adding subclass derived from class `Imdb` in `dataset/imdb.py`. -See example of `dataset/pascal_voc.py` for details. -* Download the converted pretrained `vgg16_reduced` model [here](https://github.com/zhreshold/mxnet-ssd/releases/download/v0.2-beta/vgg16_reduced.zip), unzip `.param` and `.json` files -into `model/` directory by default. -* Download the PASCAL VOC dataset, skip this step if you already have one. -``` -cd /path/to/where_you_store_datasets/ -wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar -wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar -wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar -# Extract the data. -tar -xvf VOCtrainval_11-May-2012.tar -tar -xvf VOCtrainval_06-Nov-2007.tar -tar -xvf VOCtest_06-Nov-2007.tar -``` -* We are going to use `trainval` set in VOC2007/2012 as a common strategy. -The suggested directory structure is to store `VOC2007` and `VOC2012` directories -in the same `VOCdevkit` folder. -* Then link `VOCdevkit` folder to `data/VOCdevkit` by default: +### Trying the demo +The example output image was run on VGG network for only 4 epochs. You will have to download this network's pretrained weight and symbol files from this [dropbox link](https://www.dropbox.com/sh/xv686xmciow5ns5/AADKFYVPHnTkpph7Lb-JdsdVa?dl=0). Paste the two files (without changing names) in `model` folder present in root directory of this repository. After that you can try the demo in three formats: + +* ### Pre-recorded Video +To try out the pre-recorded video, run the following command: ``` -ln -s /path/to/VOCdevkit /path/to/this_example/data/VOCdevkit +python object_detection.py ./flags/demo_data/video/demo.mp4 --epoch=4 ``` -Use hard link instead of copy could save us a bit disk space. -* Create packed binary file for faster training: +The above command will create another file named as `demo_output.mp4` present in same folder as input. You can test the output run on all the 25 flags in this [youtube video](https://www.youtube.com/watch?v=QC3GULk9ngU). I encourage you to go through [other options](https://github.com/Prasad9/mxnet-ssd/blob/Flag25/object_detection.py#L37) present in the command. + +* ### Still Images +Place all the images which you wish to run the network upon in a common folder containing no other files. Then run the following command: ``` -# cd /path/to/mxnet-ssd -bash tools/prepare_pascal.sh -# or if you are using windows -python tools/prepare_dataset.py --dataset pascal --year 2007,2012 --set trainval --target ./data/train.lst -python tools/prepare_dataset.py --dataset pascal --year 2007 --set test --target ./data/val.lst --shuffle False +python object_detection.py ./flags/demo_data/images --epoch=4 --thresh=0.6 --plot-prob=0 ``` -* Start training: +For each of the image present in the folder, it will create an `_output` file containing the predictions. I encourage you to go through [other options](https://github.com/Prasad9/mxnet-ssd/blob/Flag25/object_detection.py#L37) present in the command. + +* ### Live Feed Video +At present, the live feed video isn't working properly. This is a work in progress at the moment but you are free to test the video feed directly. To try it, run the following command: ``` -python train.py +python object_detection_app.py ``` -* By default, this example will use `batch-size=32` and `learning_rate=0.004`. -You might need to change the parameters a bit if you have different configurations. -Check `python train.py --help` for more training options. For example, if you have 4 GPUs, use: + +### Training your own network +To train your own network, collect the 25 country flags you are interested in (and name the files with country names if you wish to generate label file as well) and place them in a common folder. Then run the following command. ``` -# note that a perfect training parameter set is yet to be discovered for multi-gpu -python train.py --gpus 0,1,2,3 --batch-size 128 --lr 0.001 +cd flags +python data_utils/preprocess.py --create-label-file=1 ``` -* Memory usage: MXNet is very memory efficient, training on `VGG16_reduced` model with `batch-size` 32 takes around 4684MB without CUDNN(conv1_x and conv2_x fixed). +The output folder will contain all the resized images at dimension of 224x144 pixels. Update the contents of your label names in this [class_names.txt file](https://github.com/Prasad9/Detect-Flags-SSD/blob/Flag25/flags/input_data/class_names.txt). -### Evalute trained model -Use: +Next you will have to download a dataset which mimics the best background situation you will have while you put your model for testing. I have used [CelebA dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) which comprises of 2 lakh plus images of celebrity faces. Remember, you can't use CelebA dataset for commericial purposes. + +Next, we are going to superimpose the flag files on these background images and add some random noise. You will get an output something like this after this step. ``` -# cd /path/to/mxnet-ssd -python evaluate.py --gpus 0,1 --batch-size 128 --epoch 0 +python generate_data.py ``` -### Convert model to deploy mode -This simply removes all loss layers, and attach a layer for merging results and non-maximum suppression. -Useful when loading python symbol is not available. +![Sample Dataset](https://user-images.githubusercontent.com/13696749/32447203-5f1137ac-c331-11e7-9dfd-30276afa83a6.png) + +Next, we have to generate the record file for training and validation. Run the following command. ``` -# cd /path/to/mxnet-ssd -python deploy.py --num-class 20 -# then you can run demo with new model without loading python symbol -python demo.py --prefix model/ssd_300_deploy --epoch 0 --deploy +python generate_rec.py ``` -### Convert caffemodel -Converter from caffe is available at `/path/to/mxnet-ssd/tools/caffe_converter` +Now with the data ready, we can start the training procedure. Our base model can comprise of any model like VGG, Resnet, Inception etc. Whichever model you choose, download the weight and symbol file of that model trained on ImageNet from this MXNet models [website](http://data.mxnet.io/models/imagenet/). Place the downloaded files in `model` folder present in root directory. -This is specifically modified to handle custom layer in caffe-ssd. Usage: +Lastly, train your model. ``` -cd /path/to/mxnet-ssd/tools/caffe_converter -make -python convert_model.py deploy.prototxt name_of_pretrained_caffe_model.caffemodel ssd_converted -# you will use this model in deploy mode without loading from python symbol -python demo.py --prefix ssd_converted --epoch 1 --deploy +cd .. +python train.py ``` -There is no guarantee that conversion will always work, but at least it's good for now. +Depending upon your network, epoch no, batch size etc, you may very well like to add extra options while training your network. Hence, I encourage you to look into the [various options](https://github.com/Prasad9/mxnet-ssd/blob/Flag25/train.py#L12) present while training. -### Legacy models -Since the new interface for composing network is introduced, the old models have inconsistent names for weights. -You can still load the previous model by rename the symbol to `legacy_xxx.py` -and call with `python train/demo.py --network legacy_xxx ` -For example: -``` -python demo.py --network 'legacy_vgg16_ssd_300.py' --prefix model/ssd_300 --epoch 0 -``` diff --git a/flags/demo_data/images/demo1.jpg b/flags/demo_data/images/demo1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..06cd86673b0335721b664616df66373cfb0a6189 GIT binary patch literal 46473 zcmeFZcT`hd*Dsn7dM^R#z4sCzG^GfH4xtLDKmZ9HLPw;dAiXFAgdl_tf>K2kkg5bi zC?WzPL3)v@V!L^s?|t9%eRq84j&tt$?~HdbcCxZ&+3PpwntRW+_S*be__GRNHZ?La z0sw&k0Fd+t`11+SXBdU_1^`S3985|LFJVI%8E+r zno3HVimHN2P)$WeO+^g=2mr{XBApZfP*he?1m*t6DJuWciTOvnlA_{YKmR-FKcDD9 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