def test_funcs(img1, img2):
img3 = img2
funcs.show(img1)
funcs.save(img1, filename='out_1.png')
#funcs.getWidth funcs.getHeight
print funcs.getWidth(img1), funcs.getHeight(img1)
#funcs.mix
mixed_pics = funcs.mix(img1, img3, .5)
funcs.show(mixed_pics)
#funcs.tint
tinted_pic = funcs.tint(img1, (0, 0, 255), .5)
funcs.show(tinted_pic)
#funcs.grayscale
gray_pic = funcs.grayscale(img1)
funcs.show(gray_pic)
gray_pic_2 = funcs.grayscale(img1, two_dimensional=True)
funcs.show(gray_pic_2)
#funcs.rotate
img_rotate = funcs.rotate(img1, 180)
funcs.show(img_rotate)
def getSeam(img, blur=1):
img = img.copy()
img = funcs.grayscale(img, True)
img = np.float32(cv2.GaussianBlur(img, (blur, blur), 0))
derivative_kernel = np.float32([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
Ix = cv2.filter2D(img, -1, derivative_kernel)
Iy = cv2.filter2D(img, -1, derivative_kernel.T)
path = Ix**2 + Iy**2
path = path**.5
kernel = np.float32([[1, 1, 1]])
h, w = img.shape[:2]
for i in range(1, h):
row = path[i - 1, :]
row = cv2.erode(row, kernel.T)
path[i:i + 1, :] += row.T
x = np.argmin(path[-1])
y = h - 1
seam = []
seam.append(x)
while y > 0:
y -= 1
minX = seam[0] - 1
maxX = seam[0] + 2
x = np.argmin(path[y, max(minX, 0):min(maxX, w - 1)]) + x
if x != 0:
x -= 1
seam.insert(0, x)
return seam
def getSeam(img, blur=1, debug=False, attractor=np.zeroes(img.shape)):
img_original = img.copy()
img = img.copy()
img = funcs.grayscale(img, True)
img = np.float32(cv2.GaussianBlur(img, (blur, blur), 0))
derivative_kernel = np.float32([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
Ix = cv2.filter2D(img, -1, derivative_kernel)
Iy = cv2.filter2D(img, -1, derivative_kernel.T)
path = Ix**2 + Iy**2
path = path**.5
path += (atrractor * 100.0)
if debug:
pic_1_c_0 = path.copy()
funcs.save(pic_1_c_0, "output/pic_1_a.png")
kernel = np.float32([[1, 1, 1]])
h, w = img.shape[:2]
for i in range(1, h):
row = path[i - 1, :]
row = cv2.erode(row, kernel.T)
path[i:i + 1, :] += row.T
if debug:
pic_1_c_1 = path.copy()
funcs.save(pic_1_c_1, "output/pic_1_b.png")
x = np.argmin(path[-1])
y = h - 1
seam = []
seam.append(x)
while y > 0:
y -= 1
minX = seam[0] - 1
maxX = seam[0] + 2
x = np.argmin(path[y, max(minX, 0):min(maxX, w - 1)]) + x
if x != 0:
x -= 1
seam.insert(0, x)
if debug:
funcs.save(showSeam(pic_1_c_0, seam), "output/pic_1_c_1.png")
funcs.save(showSeam(pic_1_c_1, seam), "output/pic_1_c_2.png")
funcs.save(showSeam(img_original, seam), "output/pic_1_c_0.png")
return seam
pic1 = cv2.imread('input/pic1.png')
pic2 = cv2.imread('input/pic2.png')
pic_2_a_1 = mark_features(pic1, blur=15)
cv2.imwrite('output/pic_2_a_1.png', pic_2_a_1)
pic_2_a_2 = mark_features(pic2, blur=15)
cv2.imwrite('output/pic_2_a_2.png', pic_2_a_2)
pic_2_b, M2 = draw_mathces(pic1, pic2)
cv2.imwrite('output/pic_2_b.png', pic_2_b)
h2, w2 = pic2.shape[:2]
pic_2_d = cv2.warpPerspective(pic1, M2, (w2, h2))
cv2.imwrite('output/pic_2_d.png', pic_2_d)
pic_2_e = np.float32(funcs.grayscale(pic2)) - funcs.grayscale(pic_2_d)
pic_2_e -= np.min(pic_2_e)
pic_2_e /= np.max(pic_2_e)
pic_2_e *= 255.999999
pic_2_e = np.uint8(pic_2_e)
cv2.imwrite('output/pic_2_e.png', pic_2_e)
minX, minY, maxX, maxY = getBoundingRectangle(w2, h2, M2)
M3 = np.float32([[1, 0, -minX if minX < 0 else 0],
[0, 1, -minY if minY < 0 else 0], [0, 0, 1]])
warp_dim_x = int((abs(minX) if minX < 0 else 0) +
(abs(maxX) if maxX > w2 else w2))
warp_dim_y = int((abs(minY) if minY < 0 else 0) +
(abs(maxY) if maxY > h2 else h2))
pic1_warp = cv2.warpPerspective(pic1, M3.dot(M2), (warp_dim_x, warp_dim_y))
pic2_warp = cv2.warpPerspective(pic2, M3, (warp_dim_x, warp_dim_y))
import cv2
import numpy as np
import funcs
cv2.namedWindow("webcam", cv2.WND_PROP_FULLSCREEN)
cv2.setWindowProperty("webcam", cv2.WND_PROP_FULLSCREEN, 1)
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
lastFrame = funcs.grayscale(frame)
"""
while True:
ret,frame=cap.read()
frame=funcs.grayscale(frame)
output=np.uint8(np.abs(lastFrame*1.0-frame))
lastFrame=frame
output=np.uint8((output>50)*255.0)
cv2.imshow("webcam",output)
key=cv2.waitKey(1)
if key==27:
break
cap.release()
cv2.destroyAllWindows()
"""