def video_to_frames(video: cv2.VideoCapture,
out_path,
start=0,
end=-1,
step=24):
if not video.isOpened():
print('No video')
return
video.set(cv2.CAP_PROP_POS_FRAMES, start)
if end == -1:
end = video.get(cv2.CAP_PROP_FRAME_COUNT)
paths = []
f_count = 0
while video.isOpened() and video.get(cv2.CAP_PROP_POS_FRAMES) < end:
_, img = video.read()
path = os.path.join(out_path, f'video-{f_count}.png')
question_2.write_image(img, path)
paths.append(path)
f_count += 1
current_frame = video.get(cv2.CAP_PROP_POS_FRAMES)
video.set(cv2.CAP_PROP_POS_FRAMES, current_frame + step)
return paths
def main():
img = to_greyscale('../res/img.png')
question_2.display_image(img)
question_2.write_image(img,
'../outputs/Assignment1/question_3_greyscale.png')
grey = to_grey('../res/img.png')
question_2.display_image(grey, 'custom function')
question_2.write_image(
grey, '../outputs/Assignment1/question_3_greyscale_custom.png')
def contrast_stretch(image):
r1 = 70
s1 = 0
r2 = 140
s2 = 255
pixel_vec = np.vectorize(pixel_val)
contrast_stretched = pixel_vec(image, r1, s1, r2, s2)
display_image(image, win_name='original')
display_image(contrast_stretched, win_name='output')
write_image(contrast_stretched, '../outputs/Assignment1/question_10.png')
def main():
img = read_image('../res/img.png', cv2.IMREAD_GRAYSCALE)
flat_img_array = img.flatten()
hist = calc_hist(flat_img_array, 256)
cumulative = cumulative_freq(hist)
equalized = equalize_hist(img, flat_img_array, cumulative)
display_image(img, 'original')
display_image(equalized, 'equalized')
cv2.waitKey()
cv2.destroyAllWindows()
write_image(equalized, '../outputs/Assignment1/question_6.png')
def gamma_correction(image):
for gamma in [0.1, 0.5, 1.2, 2.2]:
gamma_corrected = np.array(255 * (image / 255) ** gamma, dtype=np.uint8)
write_image(gamma_corrected, f'../outputs/Assignment1/question_9-{gamma}.png')