negative = Controller().load_batch("./Test_negative/")
print(len(positive))
positive = Controller().grascale(positive)
negative = Controller().grascale(negative)
print(len(positive))
i = 0
for image in positive:
i += 1
grad_x, grad_y = Canny().gradient_generation(image)
egde_mag = Canny().edge_magnitude(grad_x, grad_y)
cv2.imwrite("./results/Postive" + str(i) + ".jpg", egde_mag)
i = 0
for image in negative:
i += 1
grad_x, grad_y = Canny().gradient_generation(image)
egde_mag = Canny().edge_magnitude(grad_x, grad_y)
cv2.imwrite("./results/Negative" + str(i) + ".jpg", egde_mag)
image = cv2.imread("./Train_positive/crop001278a.bmp")
image = 0.114 * image[:, :, 0] + 0.587 * image[:, :, 1] + 0.299 * image[:, :,
2]
descriptor = HOG().generate_hog_features(image, (8, 8), 2)
np.savetxt("crop001278a.csv", descriptor.flatten(), delimiter="\n")
image = cv2.imread("./Test_positive/crop001045b.bmp")
image = 0.114 * image[:, :, 0] + 0.587 * image[:, :, 1] + 0.299 * image[:, :,
2]
descriptor = HOG().generate_hog_features(image, (8, 8), 2)
np.savetxt("crop001045b.csv", descriptor.flatten(), delimiter="\n")
