def run_button(self):
# Sets whatever res you put into the res boxes.
width = self.ui.width_line.text()
height = self.ui.height_line.text()
c.width = int(width)
c.height = int(height)
# For the fps
fps = self.ui.fps_line.text()
c.fps = int(fps)
# Print using this length
length = self.ui.length_line.text()
c.length_frames = int(length)
main_text, title_sheet = fs.get_sheet() # get sheet data and title
if c.flag_create_image:
create_image.create_image(
main_text) # Creates an png Image using string
if c.flag_create_video:
make_video.make_video(
) # way of getting png into a movie sequence. Can me done on FFmpeg
if c.flag_delete_images_when_done: # deleting images
os.listdir('images')
for i in os.listdir('images'):
os.remove(f"images/{i}")
def main():
capture = cv2.VideoCapture('input.mp4')
background_subtractor = cv2.bgsegm.createBackgroundSubtractorMOG()
length = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
bar = Bar('Processing Frames', max=length)
first_iteration_indicator = 1
for i in range(0, length):
ret, frame = capture.read()
# If first frame
if first_iteration_indicator == 1:
first_frame = copy.deepcopy(frame)
height, width = frame.shape[:2]
accum_image = np.zeros((height, width), np.uint8)
first_iteration_indicator = 0
else:
filter = background_subtractor.apply(frame) # remove the background
cv2.imwrite('./frame.jpg', frame)
cv2.imwrite('./diff-bkgnd-frame.jpg', filter)
threshold = 2
maxValue = 2
ret, th1 = cv2.threshold(filter, threshold, maxValue, cv2.THRESH_BINARY)
# add to the accumulated image
accum_image = cv2.add(accum_image, th1)
cv2.imwrite('./mask.jpg', accum_image)
color_image_video = cv2.applyColorMap(accum_image, cv2.COLORMAP_SUMMER)
video_frame = cv2.addWeighted(frame, 0.7, color_image_video, 0.7, 0)
name = "./frames/frame%d.jpg" % i
cv2.imwrite(name, video_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
bar.next()
bar.finish()
make_video('./frames/', './output.avi')
color_image = cv2.applyColorMap(accum_image, cv2.COLORMAP_HOT)
result_overlay = cv2.addWeighted(first_frame, 0.7, color_image, 0.7, 0)
# save the final heatmap
cv2.imwrite('diff-overlay.jpg', result_overlay)
# cleanup
capture.release()
cv2.destroyAllWindows()
def project_structure(text_music_name,text_artist_name,image_type,op_lyric,op_deepDream,deepDream_format):
global current_job
shutil.rmtree('/code/flask/music', ignore_errors=True)
shutil.rmtree('/code/flask/imagens', ignore_errors=True)
json_code = get_lyric_videoLink(text_music_name,text_artist_name)
json_code['MusicPath'] = download_song(json_code['VideoID'])
print '\n\nmusica baixada\n\n'
json_code['Subtitle'] = get_images(json_code['Subtitle'],image_type)
print '\n\nimagens pegadas\n\n'
json_code['Subtitle'] = improve_subtitle(json_code['Subtitle'])
print '\n\ntimestamps modificado\n\n'
if op_deepDream:
if deepDream_format == '1':
json_code['Subtitle'] = dreamImage(json_code['Subtitle'])
if op_lyric:
video_name = make_videoDeep_lyric(json_code,text_music_name,True)
else:
video_name = make_videoDeep(json_code,text_music_name,True)
elif deepDream_format == '5':
json_code['Subtitle'] = dreamImage_5(json_code['Subtitle'])
if op_lyric:
video_name = make_videoDeep_lyric(json_code,text_music_name,False)
else:
video_name = make_videoDeep(json_code,text_music_name,False)
else:
json_code['Subtitle'] = dreamImage_10(json_code['Subtitle'])
if op_lyric:
video_name = make_videoDeep_lyric(json_code,text_music_name,False)
else:
video_name = make_videoDeep(json_code,text_music_name,False)
else:
if op_lyric:
video_name = make_video_lyric(json_code,text_music_name)
else:
video_name = make_video(json_code,text_music_name)
print '\n\nclipe feito\n\n'
return video_name
if __name__ == "__main__":
# Orca
# base = '/usr0/home/dlwong/Projects'
base = '/users/ahjiang'
# base2 = '/usr0/home/dlwong/Dropbox/CMU/Projects/mainstream'
base2 = '/users/ahjiang/src/'
imagelabels_file = base + '/image-data/video/oxford-flowers/imagelabels.mat'
images_dir = base + '/image-data/video/oxford-flowers/images/'
dst_dir = base + '/image-data/video/flowers_video'
metafile_dir = base2 + '/mainstream/log/videos/flowers/'
label_numbers = {"daisy": 49}
'''
possibilities = get_label(49, imagelabels_file, images_dir)
for p in possibilities:
positives = get_positive_ids(imagelabels_file, p)
print p, positives[0]
'''
positives = get_positive_ids(imagelabels_file, label_numbers["daisy"])
negatives = get_negative_ids(imagelabels_file, label_numbers["daisy"])
human_label = "daisy-p3-n7-buffer2500"
human_label = "test"
video_name = make_video.make_video(positives, negatives, 1, 1, 10,
human_label, images_dir, dst_dir,
metafile_dir, False)
perturbed_video_name = make_perturbed_video(dst_dir, video_name)
print video_name, perturbed_video_name
def main():
capture = cv2.VideoCapture(videoname + '.mp4')
#background_subtractor = cv2.createBackgroundSubtractorKNN(detectShadows=False)
background_subtractor = cv2.bgsegm.createBackgroundSubtractorMOG()
length = int(capture.get(cv2.CAP_PROP_FRAME_COUNT)) #获取视频总帧数
warning_threshold = 15 #报警阈值
print("Start processing...")
first_iteration_indicator = 1
threshold = 2
maxValue = 2
for i in tqdm(range(0, length), ncols=100): #进度条总长度为100
ret, frame = capture.read()
frame = cv2.flip(frame, -1)
# If first frame
if first_iteration_indicator == 1:
first_frame = copy.deepcopy(frame)
global height, width #图片的高度和宽度
height, width = frame.shape[:2]
global height_size, width_size
height_size = height // 10 #分割成的小区域的宽度和高度
width_size = width // 10
accum_image = np.zeros((height, width), np.uint8)
global size2
size2 = 2 * height_size * width_size
first_iteration_indicator = 0
else:
filter = background_subtractor.apply(
frame) # remove the background
cv2.imwrite(filename + '-frame_test_dining.jpg', frame)
cv2.imwrite(filename + '-diff-bkgnd-frame_test_dining.jpg', filter)
ret, th1 = cv2.threshold(filter, threshold, maxValue,
cv2.THRESH_BINARY) #黑白二值化
# add to the accumulated image
accum_image = cv2.add(accum_image, th1)
cv2.imwrite(filename + '-mask_test_dining.jpg', accum_image)
color_image_video = cv2.applyColorMap(accum_image,
cv2.COLORMAP_SUMMER) #伪彩色
video_frame = cv2.addWeighted(frame, 0.7, color_image_video, 0.5,
0) #按权重叠加
#cv2.imwrite(name, video_frame)
if i % 10 == 1:
number, max_i, max_j = dense_max(accum_image)
text = "Cumulative traffic:" + str(number) # 待添加的文字
text_color = (0, 255, 0)
if number > warning_threshold:
text = text + ' danger!'
text_color = (0, 0, 255)
warning_area = np.zeros((height, width, 3), np.uint8)
for x in range(max_i * width_size,
(max_i + 1) * width_size):
for y in range(max_j * height_size,
(max_j + 1) * height_size):
pixel_value = accum_image[y, x]
warning_area[y, x] = (pixel_value, 0, 0)
video_frame = cv2.add(video_frame, warning_area)
text_position = (int(
(max_i + 0.5) * width_size), int((max_j + 0.5) * height_size))
cv2.putText(video_frame, text, text_position,
cv2.FONT_HERSHEY_TRIPLEX, 0.8, text_color, 2,
cv2.LINE_AA)
name = "./frames_test_dining/frame%d.jpg" % i
cv2.imwrite(name, video_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
make_video('./frames_test_dining/', filename + '-output.avi')
np.savetxt(filename + '.txt', accum_image, fmt='%d') #将数组保存到文件中，方便后续调试
color_image = cv2.applyColorMap(accum_image, cv2.COLORMAP_HOT)
result_overlay = cv2.addWeighted(first_frame, 0.7, color_image, 0.7, 0)
# save the final heatmap
cv2.imwrite(filename + '-diff-overlay_test.jpg', result_overlay)
dense_every(accum_image)
# cleanup
capture.release()
cv2.destroyAllWindows()
cv2.THRESH_BINARY)
# add to the accumulated image
accum_image = cv2.add(accum_image, th1)
cv2.imwrite('./mask.jpg', accum_image)
color_image_video = cv2.applyColorMap(accum_image, cv2.COLORMAP_HOT)
video_frame = cv2.addWeighted(frame, 0.7, color_image_video, 0.7, 0)
name = "./frames/frame%d.jpg" % i
cv2.imwrite(name, video_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
bar.next()
bar.finish()
make_video('./frames/', './output.avi')
color_image = cv2.applyColorMap(accum_image, cv2.COLORMAP_HOT)
result_overlay = cv2.addWeighted(first_frame, 0.7, color_image, 0.7, 0)
# save the final heatmap
cv2.imwrite('diff-overlay.jpg', result_overlay)
# cleanup
capture.release()
cv2.destroyAllWindows()