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cv1.py
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cv1.py
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import os
import cv2
import numpy as np
from numpy import array
from scipy.signal import argrelextrema
import matplotlib.pyplot as plt
from sklearn.neighbors.kde import KernelDensity
from pandas import Series
videos_src_path = "/home/eastward/ug thesis/opencv project/"
video_formats = [".MP4", ".MOV"]
frames_save_path = "/home/eastward/ug thesis/opencv project/cv5/"
width = 1920
height = 1080
time_interval = 1
def threshold_cluster(Data_set,threshold):
stand_array=np.asarray(Data_set).ravel('C')
stand_Data=Series(stand_array)
index_list,class_k=[],[]
while stand_Data.any():
if len(stand_Data)==1:
index_list.append(list(stand_Data.index))
class_k.append(list(stand_Data))
stand_Data=stand_Data.drop(stand_Data.index)
else:
class_data_index=stand_Data.index[0]
class_data=stand_Data[class_data_index]
stand_Data=stand_Data.drop(class_data_index)
if (abs(stand_Data-class_data)<=threshold).any():
args_data=stand_Data[abs(stand_Data-class_data)<=threshold]
stand_Data=stand_Data.drop(args_data.index)
index_list.append([class_data_index]+list(args_data.index))
class_k.append([class_data]+list(args_data))
else:
index_list.append([class_data_index])
class_k.append([class_data])
return index_list,class_k
def video2frame(video_src_path, formats, frame_save_path, frame_width, frame_height, interval):
"""
将视频按固定间隔读取写入图片
:param video_src_path: 视频存放路径
:param formats: 包含的所有视频格式
:param frame_save_path: 保存路径
:param frame_width: 保存帧宽
:param frame_height: 保存帧高
:param interval: 保存帧间隔
:return: 帧图片
"""
videos = os.listdir(video_src_path)
def filter_format(x, all_formats):
if x[-4:] in all_formats:
return True
else:
return False
videos = filter(lambda x: filter_format(x, formats), videos)
for each_video in videos:
print ("正在读取视频："), each_video
each_video_name = each_video[:-4]
os.mkdir(frame_save_path + each_video_name)
each_video_save_full_path = os.path.join(frame_save_path, each_video_name) + "/"
os.mkdir(each_video_save_full_path + each_video_name)
each_video_save_full = os.path.join(frame_save_path) + "/"
each_video_full_path = os.path.join(video_src_path, each_video)
each_video_save_final_path = os.path.join(each_video_save_full_path, each_video_name) + "/"
cap = cv2.VideoCapture(each_video_full_path)
frame_index = 0
frame_count = 0
if cap.isOpened():
success = True
else:
success = False
print("读取失败!")
while(success):
success, frame = cap.read()
if frame_index % interval == 0:
resize_frame = cv2.resize(frame, (frame_width, frame_height), interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(resize_frame,cv2.COLOR_BGR2GRAY)
# cv2.imwrite(each_video_save_full_path + each_video_name + "_%d.jpg" % frame_index, resize_frame)
cv2.imwrite(each_video_save_full + "%d.pgm" % frame_count, gray)
cv2.imwrite(each_video_save_full + "%d.jpg" % frame_count, resize_frame)
prossed1_frame = os.system("./elsd" + " " + "%d.pgm" % frame_count)
path = each_video_save_full_path+"%d.pgm" % frame_count
path1 = each_video_save_full + "%d.jpg" % frame_count
img = cv2.imread(path)
imag = cv2.imread(path1)
data = np.loadtxt("ellipses.txt")
f= open("test1.txt","w+")
f1= open("test2.txt","w+")
a = data.shape[0]
data1=data.reshape(a,1,4)
data2=data1.astype(np.int)
data3=np.empty([0,1,4])
for i in range(0,data1.shape[0]):
for x1,y1,x2,y2 in data1[i]:
d = ((x1-x2)*(x1-x2)+(y1-y2)*(y1-y2))**0.5
print(d,file=f)
f.close
oriPath = "test1.txt"
oriPath1 = "test2.txt"
def get_data(lines):
sizeArry=[]
for line in lines:
line = line.replace("\n","")
line = float(line)
sizeArry.append(line)
return array(sizeArry)
f=open(oriPath)
Lenths = get_data(f.readlines())
hist, bin_edges = np.histogram(Lenths,10)
for i in range(0,data2.shape[0]):
for x1,y1,x2,y2 in data2[i]:
d = ((x1-x2)*(x1-x2)+(y1-y2)*(y1-y2))**0.5
if d > (bin_edges[1]+bin_edges[2])/2 and (y2-y1)/(x2-x1)>0:
print(x1,file=f1)
data3 = np.append(data3,[[[x1,y1,x2,y2]]], axis = 0)
f1.close
f1=open(oriPath1)
Lenths1 = get_data(f1.readlines())
Lenths1=sorted(Lenths1)
index_list,class_k=threshold_cluster(Lenths1,450)
a=len(class_k)
td1=class_k[int(a)-1]
td2=class_k[int(a)-2]
if np.var(td1)<np.var(td2):
Lenths1 = td2
else:
Lenths1 = td1
hist, bin_edges = np.histogram(Lenths1,bins=2)
break_flag=False
for i in range(0,data3.shape[0]):
for x1,y1,x2,y2 in data3[i]:
if x1>=bin_edges[len(bin_edges)-1] and x1 in Lenths1:
break_flag=True
break
if break_flag==True:
break
k = (y2-y1)/(x2-x1)
b = y1 - k*x1
cv2.line(imag,(int((0-b)/k),0),(int((1080-b)/k),1080),(0,0,255),3)
break_flag=False
for i in range(0,data3.shape[0]):
for x1,y1,x2,y2 in data3[i]:
if x1>bin_edges[len(bin_edges)-1]-500 and x1<bin_edges[len(bin_edges)-1]-150 and x1 in Lenths1:
break_flag=True
break
if break_flag==True:
break
k = (y2-y1)/(x2-x1)
b = y1 - k*x1
cv2.line(imag,(int((0-b)/k),0),(int((1080-b)/k),1080),(0,0,255),3)
cv2.imwrite(each_video_save_final_path+"%d.jpg" % frame_count,imag)
frame_count += 1
frame_index += 1
cap.release()
if __name__ == '__main__':
video2frame(videos_src_path, video_formats, frames_save_path, width, height, time_interval)