if not os.path.exists(global_path + video_name[video_name.rfind('/') + 1:video_name.rfind('.')]):

os.makedirs(global_path + video_name[video_name.rfind('/') + 1:video_name.rfind('.')])

print video_name[video_name.rfind('/') + 1:video_name.rfind('.')]

#main loop

first_detect = True

video = VideoFileClip(video_name)

folder_name = global_path + video_name[video_name.rfind('/') + 1:video_name.rfind('.')] + "/"

print folder_name

mean_r = np.zeros((video.size[1], video.size[0], size))

mean_g = np.zeros((video.size[1], video.size[0], size))

mean_b = np.zeros((video.size[1], video.size[0], size))

for i in range(0, size):

mean_r[:,:,i] = video.get_frame(i * step / video.fps)[:,:,0]

mean_b[:,:,i] = video.get_frame(i * step / video.fps)[:,:,1]

mean_g[:,:,i] = video.get_frame(i * step / video.fps)[:,:,2]

cell_size = 300

#detect = np.zeros((int(round((float(video.size[1]) / cell_size))), int(round(float(video.size[0]) / cell_size))))

#detect = np.zeros((amount_height, amount_width, length_size))

detect = []

start = time.time()

num_frame_in_background = 0

for frame in range(size * step + step, int(video.duration * video.fps), step):

print frame

background = np.dstack((np.median(mean_r, axis = 2), np.median(mean_g, axis = 2), np.median(mean_b, axis = 2)))

cur_image = video.get_frame(frame / video.fps)

cur_image_copy = np.copy(cur_image)

cur_image_with_rectangle = np.copy(cur_image)

cur_image = rgb2gray(abs(cur_image - background))

cur_image = (cur_image > 5) * 255

cur_image = opening(cur_image, disk(2))

image_label = label(cur_image)

i = 0

asdf = 0

for region in regionprops(image_label):

if region.area < 50:

continue

i = i + 1

print i

y, x, y_end, x_end = region.bbox

x = (x + x_end) / 2 - 50

y = (y + y_end) / 2 - 50

x_end = x + 100

y_end = y + 100

if x_end > video.size[0] - 1:

x_end = video.size[0] - 1

x = x_end - 100

if y_end > video.size[1] - 1:

y_end = video.size[1] - 1

y = y_end - 100

if x < 0:

x = 0

x_end = x + 100

if y < 0:

y = 0

y_end = y + 100

frame_info = np.zeros((100, 100, 3, 4), dtype = np.uint8)

mean_r1 = np.zeros((100, 100, 4))

mean_g1 = np.zeros((100, 100, 4))

mean_b1 = np.zeros((100, 100, 4))

temp1 = np.zeros(())

for j in range(0, 4):

temp1 = video.get_frame((frame + j * 5) / float(video.fps))

# imsave(folder_name + str(i) + ".jpg", temp1[y:y_end, x:x_end, :])

frame_info[:,:,:,j] = temp1[y:y_end, x:x_end,:]

mean_r1[:,:,j] = temp1[y:y_end,x:x_end,0]

mean_g1[:,:,j] = temp1[y:y_end,x:x_end,1]

mean_b1[:,:,j] = temp1[y:y_end,x:x_end,2]

temp22 = np.dstack((np.median(mean_r1, axis = 2), np.median(mean_g1, axis = 2), np.median(mean_b1, axis = 2)))

# for j in range(0, 4):

# temp1 = video.get_frame((frame + j * 5) / float(video.fps))

# imsave(folder_name + "%06d" % (50 * asdf + j * 5 + 1) + ".jpg", np.abs(temp1[y:y_end, x:x_end, :] - temp22))

# asdf+=1

for j in range(0, 4):

frame_info[:,:,:,j] = np.abs(frame_info[:,:,:,j] - temp22)

for k in range(0,100):

for m in range(0,100):

print frame_info[k,m,:,j]

print frame_info[0,0,:,j]

frame_info[:,:,:,j] = frame_info[:,:,[2, 1, 0],j].astype(np.uint8)

print frame_info[10,10,:,j]

imsave(global_path + "temp.jpg",frame_info[:,:,:,j])

frame_info[:,:,:,j] = cv.imread(global_path + "temp.jpg",cv.IMREAD_UNCHANGED)

for k in range(0,100):

for m in range(0,100):

print frame_info[k,m,:,j]

exit()

prediction = c3d_classify(

images= frame_info,

image_mean=image_mean,

net=net,

prob_layer='prob'

)

print prediction

res_cnn = int(prediction.argmax())

color = (0, 0, 0)

if (res_cnn == 0):

color = (255, 0, 0)

for i, n in enumerate(detect):

center_x = (x + x_end) / 2

center_y = (y + y_end) / 2

if (n[0][0] - cell_size <= center_y <= n[0][0] + cell_size) and (n[0][1] - cell_size <= center_x <= n[0][1] + cell_size) and n[2] != frame:

n[1][0] = 1

break

else:

detect.append((((y+y_end)/2, (x + x_end) /2), np.array([1, 0, 0, 0]), frame))

cv.rectangle(cur_image_with_rectangle, ((x + x_end) /2 - cell_size, (y+y_end)/2 - cell_size), ((x + x_end) /2 + cell_size, (y+y_end)/2 + cell_size), (0,0, 255), 2)

else:

color = (0, 255, 0)

cv.rectangle(cur_image_with_rectangle, (x, y), (x_end, y_end), color, 2)

print frame

##print detect

#print

for i, n in enumerate(detect):

if (np.sum(n[1]) == 3 or np.sum(n[1]) == 4):

#print frame / video.fps

if first_detect:

# f.write(' %f' % (frame / video.fps))

first_detect = False

cv.rectangle(cur_image_with_rectangle,(n[0][1] - cell_size, n[0][0] - cell_size), (n[0][1] + cell_size, n[0][0] + cell_size), (255, 0, 0), 2)

if (n[1][0] == 0 and n[1][1] == 1 and n[1][2] == 1):

continue

if (n[1][0] == 0 and n[1][1] == 1 and n[1][2] == 0):

del detect[i]

for i, n in enumerate(detect):

n[1][1:] = n[1][0:-1]

n[1][0] = 0

imsave(folder_name + str(frame) + ".png", cur_image_with_rectangle)

mean_r[:,:,num_frame_in_background] = cur_image_copy[:,:,0]

mean_g[:,:,num_frame_in_background] = cur_image_copy[:,:,1]

mean_b[:,:,num_frame_in_background] = cur_image_copy[:,:,2]

num_frame_in_background += 1

num_frame_in_background = num_frame_in_background % size