def video_extract_features(vid_path, frame_number, end_frame):
# FASTER video reading (decode in separate thread)
fvs = FileVideoStream(vid_path, start_frame=frame_number).start()
time.sleep(1.0)
current_frame = frame_number - 1
frameCount = end_frame - frame_number + 1
roi = [0.25, 0.55, 0.35, 0.65]
frame_width = int(fvs.get_width())
frame_height = int(fvs.get_height())
result_width = round(roi[3] * frame_width) - round(roi[2] * frame_width)
result_height = round(roi[1] * frame_height) - round(roi[0] * frame_height)
buf = np.empty((frameCount, result_height, result_width), np.dtype('uint8'))
buf_canny = np.empty((frameCount, result_height, result_width), np.dtype('uint8'))
hist_buf = np.empty((frameCount, 16))
print_iter = 0
while fvs.more():
print_iter += 1
# Capture frame-by-frame
frame = fvs.read()
current_frame += 1
frame_roi = get_ROI(frame, roi)
harris_result = get_harris_feature(frame_roi)
hist_result = extract_frame_histogram(frame_roi)
# canny_result = get_canny_feature(frame_roi)
buf[current_frame - frame_number] = harris_result
hist_buf[current_frame - frame_number] = hist_result
# buf_canny[current_frame - frame_number] = canny_result
# cv2.imshow('Frame', harris_result)
if divmod(print_iter, 60)[1] == 0:
print(f'Progress: {100*(current_frame-frame_number)/(end_frame-frame_number)}%')
# Press Q on keyboard to exit
if cv2.waitKey(10) & 0xFF == ord('q'):
print(f'current_frame: {current_frame}')
break
if current_frame == end_frame:
fvs.stop()
break
#save numpy matrix of feature frames
np.save('library_match_1_segment_harris.npy', buf)
np.save('library_match_1_segment_histogram.npy', hist_buf)
def video_extract_harris_features(vid_path,
frame_number,
end_frame,
filename='harris_features'):
# FASTER video reading (decode in separate thread)
fvs = FileVideoStream(vid_path, start_frame=frame_number).start()
time.sleep(1.0)
current_frame = frame_number - 1
frameCount = end_frame - frame_number + 1
roi = [0.35, 0.65, 0.35, 0.65]
frame_width = int(fvs.get_width())
frame_height = int(fvs.get_height())
result_width = round(roi[3] * frame_width) - round(roi[2] * frame_width)
result_height = round(roi[1] * frame_height) - round(roi[0] * frame_height)
buf = np.empty((frameCount, result_height, result_width),
np.dtype('uint8'))
print_iter = 0
while fvs.more():
print_iter += 1
# Capture frame-by-frame
frame = fvs.read()
current_frame += 1
# extract features from the frame
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
gray = gray[round(roi[0] * frame_height):round(roi[1] * frame_height),
round(roi[2] * frame_width):round(roi[3] * frame_width)]
# frame = frame[round(roi[0]*size_1):round(roi[1]*size_1), round(roi[2]*size_2):round(roi[3]*size_2)]
dst = cv2.cornerHarris(gray, 4, 3, 0.04)
# result is dilated for marking the corners, not important
dst = cv2.dilate(dst, None)
# Threshold for an optimal value, it may vary depending on the image.
# frame[dst > 0.01 * dst.max()] = [0, 0, 255]
harris_result = np.uint8(255 * dst / dst.max())
buf[current_frame - frame_number] = harris_result
# cv2.imshow('Frame', harris_result)
if divmod(print_iter, 60)[1] == 0:
print(
f'Progress: {100*(current_frame-frame_number)/(end_frame-frame_number)}%'
)
# Press Q on keyboard to exit
if cv2.waitKey(10) & 0xFF == ord('q'):
print(f'current_frame: {current_frame}')
break
if current_frame == end_frame:
fvs.stop()
break
#save numpy matrix of feature frames
np.save(f'{filename}.npy', buf)
