count-ception.py

# coding: utf-8

# This code is for:
# 
# J. P. Cohen, H. Z. Lo, and Y. Bengio, “Count-ception: Counting by Fully Convolutional Redundant Counting,” 2017.
# https://arxiv.org/abs/1703.08710
# 
# 
# Here is a video of the learning in progress:
# [![](http://img.youtube.com/vi/ej5bj0mlQq8/0.jpg)](https://www.youtube.com/watch?v=ej5bj0mlQq8)
# 
# The cell dataset used in this work is available from [VGG](http://www.robots.ox.ac.uk/~vgg/research/counting/cells.zip) and [Academic Torrents](http://academictorrents.com/details/b32305598175bb8e03c5f350e962d772a910641c)
# 

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# In[1]:

import sys,os,time,random
import numpy as np
import matplotlib
matplotlib.use('Agg');
import matplotlib.pyplot as plt
plt.set_cmap('jet');

import theano
import theano.tensor as T 
import lasagne

import skimage
from skimage.io import imread, imsave
import pickle
import scipy

from os import walk
print "theano",theano.version.full_version
print "lasagne",lasagne.__version__


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# In[3]:

import sys
import argparse

if len(sys.argv) == 3 and sys.argv[1] == "-f": #on jupyter
    sys.argv = ['']
    
parser = argparse.ArgumentParser(description='Count-ception')

parser.add_argument('-seed', type=int, nargs='?',default=0, help='random seed for split and init')
parser.add_argument('-nsamples', type=int, nargs='?',default=32, help='Number of samples (N) in train and valid')
parser.add_argument('-stride', type=int, nargs='?',default=1, help='The args.stride at the initial layer')
parser.add_argument('-lr', type=float, nargs='?',default=0.00005, help='This will set the learning rate ')
parser.add_argument('-kern', type=str, nargs='?',default="sq", help='This can be gaus or sq')
parser.add_argument('-cov', type=float, nargs='?',default=1, help='This is the covariance when kern=gaus')
parser.add_argument('-scale', type=int, nargs='?',default=1, help='Scale the input image and labels')
parser.add_argument('-data', type=str, nargs='?',default="cells", help='Dataset folder')
parser.add_argument('-framesize', type=int, nargs='?',default=256, help='Size of the images processed at once')


args = parser.parse_args()


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print args


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job_id = os.environ.get('SLURM_JOB_ID')

if job_id == None:
    job_id = os.environ.get('PBS_JOBID')

print "job_id",job_id


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# In[6]:

patch_size = 32
framesize = int(args.framesize/args.scale)
framesize_h = framesize_w = framesize
noutputs = 1
channels = 3


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# In[7]:

paramfilename = str(args.scale) + "-" + str(patch_size) + "-" + args.data + "-" + args.kern + str(args.cov) + "_params.p"
datasetfilename = str(args.scale) + "-" + str(patch_size) + "-" + str(framesize) + "-" + args.kern + str(args.stride) + "-" + args.data + "-" + str(args.cov) + "-dataset.p"
print paramfilename
print datasetfilename


# In[8]:

random.seed(args.seed)
np.random.seed(args.seed)
lasagne.random.set_rng(np.random.RandomState(args.seed))


# In[9]:

from lasagne.layers.normalization import BatchNormLayer
from lasagne.layers import InputLayer, ConcatLayer, Conv2DLayer

input_var = T.tensor4('inputs')
input_var_ex = T.ivector('input_var_ex')

def ConvFactory(data, num_filter, filter_size, stride=1, pad=0, nonlinearity=lasagne.nonlinearities.leaky_rectify):
    data = lasagne.layers.batch_norm(Conv2DLayer(
        data, num_filters=num_filter,
        filter_size=filter_size,
        stride=stride, pad=pad,
        nonlinearity=nonlinearity,
        W=lasagne.init.GlorotUniform(gain='relu')))
    return data

def SimpleFactory(data, ch_1x1, ch_3x3):
    conv1x1 = ConvFactory(data=data, filter_size=1, pad=0, num_filter=ch_1x1)
    conv3x3 = ConvFactory(data=data, filter_size=3, pad=1, num_filter=ch_3x3) 
    concat = ConcatLayer([conv1x1, conv3x3])
    return concat


input_shape = (None, channels, framesize, framesize)
img = InputLayer(shape=input_shape, input_var=input_var[input_var_ex])
net = img


net = ConvFactory(net, filter_size=3, num_filter=64, pad=patch_size)
print net.output_shape
net = SimpleFactory(net, 16, 16)
print net.output_shape
net = SimpleFactory(net, 16, 32)
print net.output_shape
net = ConvFactory(net, filter_size=14, num_filter=16) 
print net.output_shape
net = SimpleFactory(net, 112, 48)
print net.output_shape
net = SimpleFactory(net, 64, 32)
print net.output_shape
net = SimpleFactory(net, 40, 40)
print net.output_shape
net = SimpleFactory(net, 32, 96)
print net.output_shape
net = ConvFactory(net, filter_size=18, num_filter=32) 
print net.output_shape
net = ConvFactory(net, filter_size=1, pad=0, num_filter=64)
print net.output_shape
net = ConvFactory(net, filter_size=1, pad=0, num_filter=64)
print net.output_shape
net = ConvFactory(net, filter_size=1, num_filter=1, stride=args.stride)
print net.output_shape


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output_shape = lasagne.layers.get_output_shape(net)
real_input_shape = (None, input_shape[1], input_shape[2]+2*patch_size, input_shape[3]+2*patch_size)
print "real_input_shape:",real_input_shape,"-> output_shape:",output_shape


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print "network output size should be",(input_shape[2]+2*patch_size)-(patch_size)


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if (args.kern == "sq"):
    ef = ((patch_size/args.stride)**2.0)
elif (args.kern == "gaus"):
    ef = 1.0
print "ef", ef

prediction = lasagne.layers.get_output(net, deterministic=True)
prediction_count = (prediction/ef).sum(axis=(2,3))

classify = theano.function([input_var, input_var_ex], prediction)


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train_start_time = time.time()
print classify(np.zeros((1,channels,framesize,framesize), dtype=theano.config.floatX), [0]).shape
print time.time() - train_start_time, "sec"

train_start_time = time.time()
print classify(np.zeros((1,channels,framesize,framesize), dtype=theano.config.floatX), [0]).shape
print time.time() - train_start_time, "sec"


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def genGausImage(framesize, mx, my, cov=1):
    x, y = np.mgrid[0:framesize, 0:framesize]
    pos = np.dstack((x, y))
    mean = [mx, my]
    cov = [[cov, 0], [0, cov]]
    rv = scipy.stats.multivariate_normal(mean, cov).pdf(pos)
    return rv/rv.sum()

def getDensity(width, markers):
    gaus_img = np.zeros((width,width))
    for k in range(width):
        for l in range(width):
            if (markers[k,l] > 0.5):
                gaus_img += genGausImage(len(markers),k-patch_size/2,l-patch_size/2,cov)
    return gaus_img

def getMarkersCells(labelPath, scale, size):  
    labs = imread(labelPath)
    if len(labs.shape) == 2:
        lab = labs[:,:]/255
    elif len(labs.shape) == 3:
        lab = labs[:,:,0]/255
    else:
        print "unknown label format"
    
    binsize = [scale,scale]
    out = np.zeros(size)
    for i in xrange(binsize[0]):
        for j in xrange(binsize[1]):
            out = np.maximum(lab[i::binsize[0], j::binsize[1]], out)
        
    print lab.sum(),out.sum()
    assert np.allclose(lab.sum(),out.sum(), 1)
    
    return out

def getCellCountCells(markers,(x,y,h,w)):
    types = [0] * noutputs
    for i in range(noutputs):
        types[i] = (markers[y:y+h,x:x+w] == 1).sum()
        #types[i] = (markers[y:y+h,x:x+w] != -1).sum()
    return types

def getLabelsCells(markers, img_pad, base_x, base_y, stride, scale):
    
    height = ((img_pad.shape[0])/args.stride)
    width = ((img_pad.shape[1])/args.stride)
    print "label size: ", height, width
    labels = np.zeros((noutputs, height, width))
    if (args.kern == "sq"):
        for y in range(0,height):
            for x in range(0,width):
                count = getCellCountCells(markers,(x*args.stride,y*args.stride,patch_size,patch_size))  
                for i in range(0,noutputs):
                    labels[i][y][x] = count[i]

    
    elif (args.kern == "gaus"):
        for i in range(0,noutputs):
            labels[i] = getDensity(width, markers[base_y:base_y+width,base_x:base_x+width])
    

    count_total = getCellCountCells(markers,(0,0,framesize_h+patch_size,framesize_w+patch_size))
    return labels, count_total

def getTrainingExampleCells(img_raw, framesize_w, framesize_h, labelPath, base_x,  base_y, stride, scale):
    
    img = img_raw[base_y:base_y+framesize_h, base_x:base_x+framesize_w]
    img_pad = np.pad(img[:,:,0],(patch_size)/2, "constant")
    
    markers = getMarkersCells(labelPath, scale, img_raw.shape[0:2])
    markers = markers[base_y:base_y+framesize_h, base_x:base_x+framesize_w]
    markers = np.pad(markers, patch_size, "constant", constant_values=-1)
    
    labels, count  = getLabelsCells(markers, img_pad, base_x, base_y, args.stride, scale)
    return img, labels, count


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# In[15]:

import glob
imgs = []
for filename in glob.iglob(args.data + "/*dots.png"):
    imgg = filename.replace("dots","cell")
    imgs.append([imgg,filename])
    
if len(imgs) == 0:
    print "Issue with dataset"
    sys.exit()


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#print imgs


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# In[17]:

## code to debug data generation

plt.rcParams['figure.figsize'] = (18, 9)
imgPath,labelPath,x,y = imgs[9][0],imgs[9][1], 0, 0
#imgPath,labelPath,x,y = imgs[0][0], imgs[0][1], 100,200

print imgPath, labelPath

im = imread(imgPath)
img_raw_raw = im #grayscale

img_raw = scipy.misc.imresize(img_raw_raw, (int(img_raw_raw.shape[0]/args.scale), int(img_raw_raw.shape[1]/args.scale)))
print img_raw_raw.shape," ->>>>", img_raw.shape

print "img_raw",img_raw.shape
img, lab, count = getTrainingExampleCells(img_raw, framesize_w, framesize_h, labelPath, x, y, args.stride, args.scale)
print "count", count

markers = getMarkersCells(labelPath, args.scale, img_raw.shape[0:2])
markers = markers[y:y+framesize_h, x:x+framesize_w]
count = getCellCountCells(markers, (0, 0, framesize_w,framesize_h))
print "count", count, 'markers max', markers.max()

pcount = classify([img.transpose((2,0,1))], [0])[0]

lab_est = [(l.sum()/ef).astype(np.int) for l in lab]
pred_est = [(l.sum()/ef).astype(np.int) for l in pcount]

print "img shape",  img.shape
print "label shape", lab.shape
print "label est ",lab_est," --> predicted est ",pred_est


# In[18]:

fig = plt.Figure(figsize=(18, 9), dpi=160)
gcf = plt.gcf()
gcf.set_size_inches(18, 15)
fig.set_canvas(gcf.canvas)

ax2 = plt.subplot2grid((2,4), (0, 0), colspan=2)
ax3 = plt.subplot2grid((2,4), (0, 2), colspan=3)
ax4 = plt.subplot2grid((2,4), (1, 2), colspan=3)
ax5 = plt.subplot2grid((2,4), (1, 0), rowspan=1)
ax6 = plt.subplot2grid((2,4), (1, 1), rowspan=1)

ax2.set_title("Input Image")
ax2.imshow(img, interpolation='none',cmap='Greys_r');
ax3.set_title("Regression target, {}x{} sliding window.".format(patch_size, patch_size))
ax3.imshow(np.concatenate((lab),axis=1), interpolation='none');
#ax3.imshow(lab[0], interpolation='none')

ax4.set_title("Predicted counts")
ax4.imshow(np.concatenate((pcount),axis=1), interpolation='none');

ax5.set_title("Real " + str(lab_est))
ax5.set_ylim((0, np.max(lab_est)*2))
ax5.set_xticks(np.arange(0, noutputs, 1.0))
ax5.bar(range(noutputs),lab_est, align='center')
ax6.set_title("Pred " + str(pred_est))
ax6.set_ylim((0, np.max(lab_est)*2))
ax6.set_xticks(np.arange(0, noutputs, 1.0))
ax6.bar(range(noutputs),pred_est, align='center')


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# In[19]:

img_pad = np.asarray([np.pad(img[:,:,i],(patch_size-1)/2, "constant", constant_values=255) for i in range(img[0,0].shape[0])])
img_pad = img_pad.transpose((1,2,0))
plt.imshow(img_pad);
plt.imshow(lab[0], alpha=0.5);


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for path in imgs: 
    if (not os.path.isfile(path[0])):
        print path, "bad", path[0]
    if (not os.path.isfile(path[1])):
        print path, "bad", path[1]


# In[21]:

dataset = []
if (os.path.isfile(datasetfilename)):
    print "reading", datasetfilename
    dataset = pickle.load(open(datasetfilename, "rb" ))
else:
    dataset_x = []
    dataset_y = []
    dataset_c = []
    print len(imgs)
    for path in imgs: 

        imgPath = path[0]
        print imgPath

        im = imread(imgPath)
        img_raw_raw = im
        
        img_raw = scipy.misc.imresize(img_raw_raw, (int(img_raw_raw.shape[0]/args.scale),int(img_raw_raw.shape[1]/args.scale)))
        print img_raw_raw.shape," ->>>>", img_raw.shape

        labelPath = path[1]
        for base_x in range(0,img_raw.shape[0],framesize_h):
            for base_y in range(0,img_raw.shape[1],framesize_w):
                
                if (img_raw.shape[1] - base_y < framesize_w) or (img_raw.shape[0] - base_x < framesize_h):
                    print "!!!! Not adding image because size is" , img_raw.shape[1] - base_y, img_raw.shape[0] - base_x
                    continue
                    
                img, lab, count = getTrainingExampleCells(img_raw, framesize_w, framesize_h, labelPath, base_y, base_x, args.stride, args.scale)
                print "count ", count
                    
                if img.shape[0:2] != (framesize_w,framesize_h):
                    print "!!!! Not adding image because size is" , img.shape[0:2]
                    
                else :   
                    lab_est = [(l.sum()/ef).astype(np.int) for l in lab]
                
                    assert np.allclose(count,lab_est, 0)
                
                    dataset.append((img,lab,count))
                    
                    print "lab_est", lab_est, "img shape", img.shape, "label shape", lab.shape
                    sys.stdout.flush()
                    
        print "dataset size", len(dataset)
                    
    print "writing", datasetfilename
    out = open(datasetfilename, "wb",0)
    pickle.dump(dataset, out)
    out.close()
print "DONE"


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# In[22]:

# %matplotlib inline
# plt.rcParams['figure.figsize'] = (18, 9)
# plt.imshow(lab[0])


# In[23]:

#np_dataset = np.asarray(dataset)

np.random.shuffle(dataset)

np_dataset_x = np.asarray([d[0] for d in dataset], dtype=theano.config.floatX)
np_dataset_y = np.asarray([d[1] for d in dataset], dtype=theano.config.floatX)
np_dataset_c = np.asarray([d[2] for d in dataset], dtype=theano.config.floatX)


np_dataset_x = np_dataset_x.transpose((0,3,1,2))

print "np_dataset_x", np_dataset_x.shape
print "np_dataset_y", np_dataset_y.shape
print "np_dataset_c", np_dataset_c.shape


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# In[24]:

length = len(np_dataset_x)

n = args.nsamples

np_dataset_x_train = np_dataset_x[0:n]
np_dataset_y_train = np_dataset_y[0:n]
np_dataset_c_train = np_dataset_c[0:n]
print "np_dataset_x_train", len(np_dataset_x_train)

np_dataset_x_valid = np_dataset_x[n:2*n]
np_dataset_y_valid = np_dataset_y[n:2*n]
np_dataset_c_valid = np_dataset_c[n:2*n]
print "np_dataset_x_valid", len(np_dataset_x_valid)

np_dataset_x_test = np_dataset_x[-100:]
np_dataset_y_test = np_dataset_y[-100:]
np_dataset_c_test = np_dataset_c[-100:]
print "np_dataset_x_test", len(np_dataset_x_test)


# In[25]:

print "number of counts total ", np_dataset_c.sum()
print "number of counts on average ", np_dataset_c.mean(), "+-", np_dataset_c.std()
print "counts min:", np_dataset_c.min(), "max:", np_dataset_c.max()


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plt.rcParams['figure.figsize'] = (15, 5)
plt.title("Example images")
plt.imshow(np.concatenate(np_dataset_x_train[:5].astype(np.uint8).transpose((0,2,3,1)),axis=1), interpolation='none');


# In[27]:

plt.title("Example images")
plt.imshow(np.concatenate(np_dataset_y_train[:5,0],axis=1), interpolation='none');


# In[28]:

plt.rcParams['figure.figsize'] = (15, 5)
plt.title("Counts in each image")
plt.bar(range(len(np_dataset_c_train)),np_dataset_c_train);


# In[29]:

print "Total cells in training", np.sum(np_dataset_c_train[0:], axis=0)
print "Total cells in validation", np.sum(np_dataset_c_valid[0:], axis=0)
print "Total cells in testing", np.sum(np_dataset_c_test[0:], axis=0)


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# In[30]:

#to make video: ffmpeg -i images-cell/image-0-%d-cell.png -vcodec libx264 aout.mp4
def processImages(name, i):
    fig = plt.Figure(figsize=(18, 9), dpi=160)
    gcf = plt.gcf()
    gcf.set_size_inches(18, 15)
    fig.set_canvas(gcf.canvas)
    
    (img, lab, count) = dataset[i]
    
    #print str(i),count
    pcount = classify([img.transpose((2,0,1))], [0])[0]
    
    lab_est = [(l.sum()/(ef)).astype(np.int) for l in lab]
    
    #print lab_est
    
    pred_est = [(l.sum()/(ef)).astype(np.int) for l in pcount]
    
    print str(i),"label est ",lab_est," --> predicted est ",pred_est

    ax2 = plt.subplot2grid((2,6), (0, 0), colspan=2)
    ax3 = plt.subplot2grid((2,6), (0, 2), colspan=5)
    ax4 = plt.subplot2grid((2,6), (1, 2), colspan=5)
    ax5 = plt.subplot2grid((2,6), (1, 0), rowspan=1)
    ax6 = plt.subplot2grid((2,6), (1, 1), rowspan=1)

    ax2.set_title("Input Image")
    ax2.imshow(img, interpolation='none', cmap='Greys_r')
    ax3.set_title("Regression target, {}x{} sliding window.".format(patch_size, patch_size))
    ax3.imshow(np.concatenate((lab),axis=1), interpolation='none')
    ax4.set_title("Predicted counts")
    ax4.imshow(np.concatenate((pcount),axis=1), interpolation='none')

    ax5.set_title("Real " + str(lab_est))
    ax5.set_ylim((0, np.max(lab_est)*2))
    ax5.set_xticks(np.arange(0, noutputs, 1.0))
    ax5.bar(range(noutputs),lab_est, align='center')
    ax6.set_title("Pred " + str(pred_est))
    ax6.set_ylim((0, np.max(lab_est)*2))
    ax6.set_xticks(np.arange(0, noutputs, 1.0))
    ax6.bar(range(noutputs),pred_est, align='center')
    if not os.path.exists('images-cell'): 
        os.mkdir('images-cell')
    fig.savefig('images-cell/image-' + str(i) + "-" + name + '.png', bbox_inches='tight', pad_inches=0)


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# In[31]:

import pickle, os

directory = "network-temp/"
ext = "countception.p"

if not os.path.exists(directory):
    os.makedirs(directory)
    
def save_network(net,name):
    pkl_params = lasagne.layers.get_all_param_values(net, trainable=True)
    out = open(directory + str(name) + ext, "w", 0) #bufsize=0
    pickle.dump(pkl_params, out)
    out.close()

def load_network(net,name):
    all_param_values = pickle.load(open(directory + str(name) + ext, "r" ))
    lasagne.layers.set_all_param_values(net, all_param_values, trainable=True)


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# In[32]:

#test accuracy
def test_perf(dataset_x, dataset_y, dataset_c):

    testpixelerrors = []
    testerrors = []
    bs = 1
    for i in range(0,len(dataset_x), bs):

        pcount = classify(dataset_x,range(i,i+bs))
        pixelerr = np.abs(pcount - dataset_y[i:i+bs]).mean(axis=(2,3))
        testpixelerrors.append(pixelerr)
        
        pred_est = (pcount/(ef)).sum(axis=(1,2,3))
        err = np.abs(dataset_c[i:i+bs].flatten()-pred_est)
        
        testerrors.append(err)
    
    return np.abs(testpixelerrors).mean(), np.abs(testerrors).mean()


# In[33]:

print "Random performance"
print test_perf(np_dataset_x_train, np_dataset_y_train, np_dataset_c_train)
print test_perf(np_dataset_x_valid, np_dataset_y_valid, np_dataset_c_valid)
print test_perf(np_dataset_x_test, np_dataset_y_test, np_dataset_c_test)


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# In[34]:

target_var = T.tensor4('target')
lr = theano.shared(np.array(0.0, dtype=theano.config.floatX))

#Mean Absolute Error is computed between each count of the count map
l1_loss = T.abs_(prediction - target_var[input_var_ex])

#Mean Absolute Error is computed for the overall image prediction
prediction_count2 =(prediction/ef).sum(axis=(2,3))
mae_loss = T.abs_(prediction_count2 - (target_var[input_var_ex]/ef).sum(axis=(2,3))) 

loss = l1_loss.mean()

params = lasagne.layers.get_all_params(net, trainable=True)
updates = lasagne.updates.adam(loss, params, learning_rate=lr)

train_fn = theano.function([input_var_ex], [loss,mae_loss], updates=updates,
                         givens={input_var:np_dataset_x_train, target_var:np_dataset_y_train})

print "DONE compiling theano functons"


# In[36]:

lr.set_value(args.lr)
best_valid_err = 99999999
best_test_err = 99999999
epoch = 0


# In[37]:

batch_size = 2

print "batch_size", batch_size
print "lr", lr.eval()

datasetlength = len(np_dataset_x_train)
print "datasetlength",datasetlength

for epoch in range(epoch, 1000):
    start_time = time.time()

    epoch_err_pix = []
    epoch_err_pred = []
    todo = range(datasetlength)    
    
    for i in range(0,datasetlength, batch_size):
        ex = todo[i:i+batch_size]

        train_start_time = time.time()
        err_pix,err_pred = train_fn(ex)
        train_elapsed_time = time.time() - train_start_time

        epoch_err_pix.append(err_pix)
        epoch_err_pred.append(err_pred)

    valid_pix_err, valid_err = test_perf(np_dataset_x_valid, np_dataset_y_valid, np_dataset_c_valid)

    # a threshold is used to reduce processing when we are far from the goal
    if (valid_err < 10 and valid_err < best_valid_err):
        best_valid_err = valid_err
        best_test_err = test_perf(np_dataset_x_test, np_dataset_y_test,np_dataset_c_test)
        print "OOO best test (err_pix, err_pred)", best_test_err, ",epoch",epoch
        save_network(net,"best_valid_err" + job_id)


    elapsed_time = time.time() - start_time
    err = np.mean(epoch_err_pix)
    acc = np.mean(np.concatenate(epoch_err_pred))
    
    if epoch % 5 == 0:
        print "#" + str(epoch) + "#(err_pix:" + str(np.around(err,3)) + ",err_pred:" +  str(np.around(acc,3)) + "),valid(err_pix:" + str(np.around(valid_pix_err,3)) + ",err_pred:" + str(np.around(valid_err,3)) +"),(time:" + str(np.around(elapsed_time,3)) + "sec)"

    #visualize training
    #processImages(str(epoch) + '-cell',0)

print "#####", "best_test_acc", best_test_err, args


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print "Done"


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#load best network
load_network(net,"best_valid_err" + job_id)


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def compute_counts(dataset_x):

    bs = 1
    ests = []
    for i in range(0,len(dataset_x), bs):
        pcount = classify(dataset_x,range(i,i+bs))
        pred_est = (pcount/(ef)).sum(axis=(1,2,3))        
        ests.append(pred_est)
    return ests


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plt.rcParams['figure.figsize'] = (15, 5)
plt.title("Training Data")

pcounts = compute_counts(np_dataset_x_train)
plt.bar(np.arange(len(np_dataset_c_train))-0.1,np_dataset_c_train, width=0.5, label="Real Count");
plt.bar(np.arange(len(np_dataset_c_train))+0.1,pcounts, width=0.5,label="Predicted Count");
plt.tight_layout()
plt.legend()


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plt.rcParams['figure.figsize'] = (15, 5)
plt.title("Valid Data")

pcounts = compute_counts(np_dataset_x_valid)
plt.bar(np.arange(len(np_dataset_c_valid))-0.1,np_dataset_c_valid, width=0.5, label="Real Count");
plt.bar(np.arange(len(np_dataset_c_valid))+0.1,pcounts, width=0.5,label="Predicted Count");
plt.tight_layout()
plt.legend()


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plt.rcParams['figure.figsize'] = (15, 5)
plt.title("Test Data")

pcounts = compute_counts(np_dataset_x_test)
plt.bar(np.arange(len(np_dataset_c_test))-0.1,np_dataset_c_test, width=0.5, label="Real Count");
plt.bar(np.arange(len(np_dataset_c_test))+0.1,pcounts, width=0.5,label="Predicted Count");
plt.tight_layout()
plt.legend()


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processImages('test',0)


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processImages('test',1)


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processImages('test',2)


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processImages('test',3)


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processImages('test',4)


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processImages('test',5)


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processImages('test',10)


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