def main():
args = parse_args()
img = task1.read_image(args.img_path)
template = task1.read_image(args.template_path)
coordinates = detect(img, template)
template_name = "{}.json".format(os.path.splitext(os.path.split(args.template_path)[1])[0])
save_results(coordinates, template, template_name, args.rs_directory)
def main():
args = parse_args()
img = t1.read_image(args.img_path)
# template = utils.crop(img, xmin=10, xmax=30, ymin=10, ymax=30)
# template = np.asarray(template, dtype=np.uint8)
# cv2.imwrite("./data/proj1-task2-template.jpg", template)
template = t1.read_image(args.template_path)
x, y, max_value = match(img, template)
# The correct results are: x: 17, y: 129, max_value: 0.994
#print("x : " ,x , " y :", y , "max ncc: ", max_value )
with open(args.rs_path, "w") as file:
json.dump({"x": x, "y": y, "value": max_value}, file)
def main():
args = parse_args()
img = read_image(args.img_path)
template = read_image(args.template_path)
coordinates = detect(img, template)
print(coordinates)
# print(coordinates)
fig, ax = plt.subplots(1)
ax.imshow(img)
for i in coordinates:
ax.add_patch(Circle((i[1], i[0]), radius=1, color='red'))
plt.show(fig)
template_name = "{}.json".format(
os.path.splitext(os.path.split(args.template_path)[1])[0])
save_results(coordinates, template, template_name, args.rs_directory)
def main():
args = parse_args()
img = task1.read_image(args.img_path)
template = task1.read_image(args.template_path)
if (args.template_path) == "./data/a.jpg":
coordinates = detect(img, template, threshold=4.6)
elif (args.template_path) == "./data/b.jpg":
coordinates = detect(img, template, threshold=6.44)
elif (args.template_path) == "./data/c.jpg":
coordinates = detect(img, template, threshold=4.6)
elif (args.template_path) == "./data/wa_img.jpg":
coordinates = detect(img, template, threshold=4.6)
else:
print("Template does not exist.")
template_name = "{}.json".format(
os.path.splitext(os.path.split(args.template_path)[1])[0])
save_results(coordinates, template, template_name, args.rs_directory)
def main():
args = parse_args()
img = task1.read_image(args.img_path)
template = task1.read_image(args.template_path)
coordinates = detect(img, template)
template_name = "{}.json".format(
os.path.splitext(os.path.split(args.template_path)[1])[0])
save_results(coordinates, template, template_name, args.rs_directory)
img = cv2.imread(args.img_path, 1)
import matplotlib.pyplot as plt
from matplotlib.patches import Arrow, Circle
fig, ax = plt.subplots(1)
ax.imshow(img)
for i in coordinates:
ax.add_patch(Circle((i[1], i[0]), radius=1, color='red'))
plt.show(fig)
import cv2
import sys
import numpy as np
from task1 import min_filtered_image, max_filtered_image, path, read_image
def max_min(I, N=3):
A = max_filtered_image(I, N)
B = min_filtered_image(A, N)
O = np.zeros(I.shape).astype('uint8')
O = I - B + 255
return O, B
if __name__ == '__main__':
I = read_image(path)
if len(sys.argv) == 2:
N = int(sys.argv[1])
# check if N is odd
if N % 2 == 1:
O, background = max_min(I, N)
cv2.imwrite(f'task2_O_N_{N}.png', O)
print(f'task2_O_N_{N}.png saved in root.')
# optional
O_normalized = cv2.normalize(O,
None,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX,