def main():
video = VideoCapture(video_sources.video_2)
workArea = WorkAreaView(video_sources.video_2_work_area_markers)
vc = VideoController(10, 'pause')
(video_wnd, bin_diff_wnd, gray_diff_wnd, colorDiffWnd,
learned_BG_wnd) = Wnd.create('video', 'binary diff', 'gray diff',
'color diff', 'Learned BG')
colorAbsDiffWnd = Wnd('color_abs_diff')
segmentedWnd = Wnd('segmented')
segmenter = Segmenter()
frames_iter = workArea.skip_non_area(video.frames())
motionDetector = MotionDetector(next(frames_iter)[0], 3)
backgroundModel = BackgroundModel(15)
prevBackground = None
for frame, _ in frames_iter:
motionDetector.detect(frame)
if motionDetector.motionEnded():
# calc fgMask
mask, gray_diff, color_diff, colorAbsDiff = calcForegroundMask(
prevBackground, frame)
# bin_diff_wnd.imshow(resize(mask, 0.5))
bin_diff_wnd.imshow(cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR))
# gray_diff_wnd.imshow(resize(gray_diff, .5))
# colorDiffWnd.imshow(resize(color_diff, .5))
# colorAbsDiffWnd.imshow(resize(colorAbsDiff, .5))
markers, objectsCount = segmenter.segment(mask)
segmentedWnd.imshow(
resize(Segmenter.markersToDisplayImage(markers, objectsCount),
.5))
backgroundModel = BackgroundModel(15)
if motionDetector.isSilence():
backgroundModel.learn(frame, foregroundMask=None)
learned_BG_wnd.imshow(resize(backgroundModel.learned, .5))
if motionDetector.motionStarted():
prevBackground = backgroundModel.learned
backgroundModel = None
learned_BG_wnd.imshow(resize(prevBackground, .5))
# VIS
vis_img = motionDetector.indicateCurrentState(frame.copy())
vis_img = utils.put_frame_pos(vis_img, video.frame_pos(), xy=(2, 55))
video_wnd.imshow(vis_img)
# bin_diff_wnd.imshow(resize(motionDetector.bin_diff, .5))
# gray_diff_wnd.imshow(resize(motionDetector.gray_diff, .5))
# VIS END
if vc.wait_key() == 27: break
video.release()
def main():
video = VideoCapture(video_sources.video_6)
work_area = WorkAreaView(video_sources.video_6_work_area_markers)
vc = VideoController(10, 'pause')
(video_wnd, bin_diff_wnd, gray_diff_wnd, frame0_diff_wnd,
learned_BG_wnd) = Wnd.create('video', 'binary diff', 'gray diff',
'diff with frame0', 'Learned BG')
frames_iter = work_area.skip_non_area(video.frames())
motion_detector = MotionDetector(next(frames_iter)[0], 3)
background = BackgroundModel(motion_detector, 15)
for frame, _ in frames_iter:
motion_detector.detect(frame)
if not background.done:
background.learn()
else:
if motion_detector.motion_ended():
frame0_diff = cv2.absdiff(background.learned, frame)
gray_of_color_diff = Helper.to_gray(frame0_diff)
frame0_diff_wnd.imshow(
resize(
np.hstack(
(frame0_diff, Helper.to_bgr(gray_of_color_diff))),
.5))
_, binary = cv2.threshold(gray_of_color_diff, 35, 255,
cv2.THRESH_BINARY)
cv2.imshow('1 binary', resize(binary, .5))
# VIS
if background.done:
learned_BG_wnd.imshow(resize(background.learned, 1))
vis_img = motion_detector.put_current_state(frame.copy())
vis_img = utils.put_frame_pos(vis_img, video.frame_pos(), xy=(2, 55))
video_wnd.imshow(vis_img)
# bin_diff_wnd.imshow(resize(motion_detector.bin_diff, .5))
# gray_diff_wnd.imshow(resize(motion_detector.gray_diff, .5))
# VIS END
if vc.wait_key() == 27: break
video.release()
def readbtn(self, obj):
Window.screenshot(name='digit.png')
image = resizer.resize(f"digit0001.png")
image = 255. - np.mean(image, axis=2).reshape(1, -1)
prediction = model.predict_number(image)
print(prediction)
self.resultlbl.text = f"[color=3333ff][b]result: {prediction}[/b][/color]"
removeFiles()
def small_app_icon_path(self):
app_icon_path = self.app_icon_path()
small_icon_path = os.path.join(os.path.dirname(app_icon_path), "small_icon.png")
image_resizer.resize(app_icon_path, small_icon_path, 114, 114)
return small_icon_path
from colorthief import ColorThief
import time
import image_resizer
def get_dominant_colors(img_path):
color_thief = ColorThief(img_path)
# get the dominant color
dominant_color = color_thief.get_color(quality=1)
palette = color_thief.get_palette(color_count=5)
print(palette)
file_name = "/bighdd/Downloads/vannaya.txt"
with open(file_name) as f:
images = f.readlines()
start_time = time.time()
for image in images:
try:
image_resizer.resize(image.rstrip(), 100)
print("--- %s resized ---" % (image))
except:
print("Не смогли сресайзить")
print("--- %s seconds ---" % (time.time() - start_time))