return objects
if __name__ == '__main__':
try:
edge_filter_dict = {
'sobel': apply_sobel,
'prewitt': apply_prewitt,
'canny': apply_canny,
}
filter_func = edge_filter_dict.get(sys.argv[1])
if len(sys.argv) < 3 and filter_func:
start_cv_video(img_filter=filter_func)
elif len(sys.argv) < 4 and filter_func:
img_path = sys.argv[2]
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
new_img = filter_func(img)
plt_show_img(new_img)
else:
raise Exception('Method not implemented')
except Exception as error:
print(error)
print(
'Usage: python3 edge_detection[canny | sobel | prewitt] [file_path]'
)
for (x, y, r) in circles:
# draw the circle in the output image, then draw a rectangle
# corresponding to the center of the circle
cv2.circle(output, (x, y), r, RGB_GREEN, 4)
cv2.rectangle(output, (x - 5, y - 5), (x + 5, y + 5), RGB_GREEN, -1)
return output
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='Emoji segmentation')
parser.add_argument('-i', '--image', type=str, action='store', dest='src_img', help='The image to apply the filter')
parser.add_argument('-ks', '--kernel_size', type=int, default=5, action='store', dest='kernel_size', help='The size of the kernel when applying dilatation or erotion')
parser.add_argument('-d', '--dilate', type=int, default=0, action='store', dest='dilate_iter', help='Number of times to apply the Dilate operation')
parser.add_argument('-e', '--erode', type=int, default=0, action='store', dest='erode_iter', help='Number of times to apply the Erode operation')
args = parser.parse_args()
if args.src_img:
try:
img = cv2.imread(args.src_img, cv2.IMREAD_COLOR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
rgb_img = filter(img)
plt_show_img(rgb_img)
except Exception as error:
print(error)
else:
start_cv_video(0, filter)
nimg[:rows, :cols] = img
return nimg
if __name__ == '__main__':
img = cv2.imread('images/ciel_bw.jpg', cv2.IMREAD_GRAYSCALE)
#img = optimize_img_for_DFT(img)
#spectrum = get_spectrum(img)
hpf = apply_hpf(img)
#lpf = apply_lpf(img)
#bpf = apply_bpf(img)
#img_comparison = np.concatenate((img, spectrum), axis=1)
helpers.plt_show_img(hpf)
def grab_frame(cap):
_, frame = cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame = optimize_img_for_DFT(frame)
spectrum = get_spectrum(frame)
img_comparison = np.concatenate((frame, spectrum), axis=1)
return img_comparison
DEFAULT_CAM = 0
cap = cv2.VideoCapture(DEFAULT_CAM)
frame_view = plt.imshow(grab_frame(cap), cmap='gray')
def update(i):
def slow_binarization(img, threshold=127):
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
height, width = img.shape
binary = np.zeros([height, width, 1], 'uint8')
for row in range(0, height):
for col in range(0, width):
if img[row][col] > threshold:
binary[row][col] = 255
return binary
if __name__ == '__main__':
if len(sys.argv) < 2:
start_cv_video(img_filter=otsu_binarization)
else:
try:
img_path = sys.argv[1]
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
binarized_img = otsu_binarization(img)
plt_show_img(binarized_img)
if (sys.argv[2] == '-h'):
plt_hist(img.ravel(), 'Color Histogram')
except Exception as error:
print(error)