import numpy as np
import cv2
from matplotlib import pyplot as plt
from convolve_np import convolve_np
import time
img = cv2.imread('images/jet.jpg', cv2.IMREAD_GRAYSCALE)
height = img.shape[0]
width = img.shape[1]
Hx = 1 / 4 * np.array([[1, 0, -1], [2, 0, -2], [1, 0, -1]])
Hy = 1 / 4 * np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]])
t0 = time.time()
img_x = convolve_np(img, Hx)
img_y = convolve_np(img, Hy)
img_out = np.sqrt(np.power(img_x, 2) + np.power(img_y, 2))
img_out = (img_out / np.max(img_out)) * 255
t1 = time.time()
print(t1 - t0)
cv2.imwrite('images/edge_sobel.jpg', img_out)
plt.imshow(img_out, cmap='gray', interpolation='bicubic')
plt.xticks([]), plt.yticks([])
plt.show()
img = cv2.imread('images/jet.jpg', cv2.IMREAD_GRAYSCALE)
height = img.shape[0]
width = img.shape[1]
Hx = np.array([[-1, 0, 1],
[-2, 0, 2],
[-1, 0, 1]])
Hy = np.array([[-1, -2, -1],
[0, 0, 0],
[1, 2, 1]])
t0 = time.time()
img_x = convolve_np(img, Hx) / 8.0
img_y = convolve_np(img, Hy) / 8.0
img_out = np.sqrt(np.power(img_x, 2) + np.power(img_y, 2))
img_out = (img_out / np.max(img_out)) * 255
t1 = time.time()
print(t1-t0)
cv2.imwrite('images/edge_sobel.jpg', img_out)
plt.imshow(img_out, cmap = 'gray', interpolation = 'bicubic')
plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis
plt.show()
from convolve_np import convolve_np
import edge_canny_get_orientation_sector as ors
import edge_canny_is_local_max as locmax
import edge_canny_trace_and_threshold as tt
img = cv2.imread('images/butterfly.jpg', cv2.IMREAD_GRAYSCALE)
height = img.shape[0]
width = img.shape[1]
# step 1: blur with Gaussian
gauss = (1.0 / 57) * np.array([[0, 1, 2, 1, 0], [1, 3, 5, 3, 1], [
2, 5, 9, 5, 2
], [1, 3, 5, 3, 1], [0, 1, 2, 1, 0]])
img_blur = convolve_np(img, gauss)
# step 2: compute gradient magnitude
img_x = convolve_np(img_blur, np.array([[-0.5, 0, 0.5]]))
img_y = convolve_np(img_blur, np.array([[-0.5], [0], [0.5]]))
E_mag = np.sqrt(np.power(img_x, 2) + np.power(img_y, 2))
E_mag = (E_mag / np.max(E_mag)) * 255
# step 3: non-maximum suppression
t_low = 4
E_nms = np.zeros((height, width))
for i in np.arange(1, height - 1):
for j in np.arange(1, width - 1):
dx = img_x[i, j]
dy = img_y[i, j]
s_theta = ors.get_orientation_sector(dx, dy)
import numpy as np
from matplotlib import pyplot as plt
from scipy import ndimage
from convolve_np import convolve_np
img = cv2.imread('../img/mano.jpg', cv2.IMREAD_GRAYSCALE)
#Sovel
height = img.shape[0]
width = img.shape[1]
Hx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
Hy = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]])
sobel_x = convolve_np(img, Hx) / 8.0
sobel_y = convolve_np(img, Hy) / 8.0
#prewitt
Px = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]])
Py = np.array([[-1, -1, -1], [0, 0, 0], [1, 1, 1]])
prewitt_x = convolve_np(img, Px) / 6.0
prewitt_y = convolve_np(img, Py) / 6.0
#robert cross
roberts_cross_v = np.array([[0, 0, 0], [0, 1, 0], [0, 0, -1]])