def carveSeams(img, seams, show=False):
h, w = img.shape[:2]
seam_map = np.tile(np.arange(w), (h, 1))
for i in range(seams):
seam = getSeam(img)
seam_map = removeSeam(seam_map, seam)
img = removeSeam(img, seam)
if show:
funcs.show(img, False)
return img, seam_map
import numpy as np
import cv2
import funcs
img=cv2.imread("image.png",0)
img=np.float32(cv2.GaussianBlur(img,(5,5),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)
I=Ix**2+Iy**2
funcs.show(I**.5)
path=I*1
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
x=x+1 or x or x-1
seam.insert(x,0)
print seam
funcs.show(path)
src_pts = []
dst_pts = []
for match in matches:
src_pts.append(kp1[match.queryIdx].pt)
dst_pts.append(kp2[match.trainIdx].pt)
src_pts = np.array(src_pts)
dst_pts = np.array(dst_pts)
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
img1_transformed = cv2.warpPrespective(img1, M, img1.shape[::-1])
"""
img=cv2.imread('image1.png',0)
#funcs.show(img)
#img=cross_correlate(img,51)
#funcs.show(img)
img=cv2.GaussianBlur(img,(5,5),0,borderType=cv2.BORDER_REFLECT_101)
kernel=np.array([[0,0,0],[1,0,-1],[0,0,0]])
Ix=cv2.filter2D(img,-1,kernel,borderType=cv2.BORDER_REFLECT_101)
Iy=cv2.filter2D(img,-1,kernel.T,borderType=cv2.BORDER_REFLECT_101)
thing=cv2.cornerHarris(np.uint8(img),2,3,0.04)
""" """Dilate makes local maxes stick out""" """
thing_dilate=cv2.dilate(thing,np.ones((15,15)))
corners=thing_dilate==thing
corners[thing<.1*thing.max()]=0
funcs.show(thing)
funcs.show(thing_dilate)
funcs.show(corners)
xs,ys=corners
"""
row = img[i]
row = np.concatenate((row[:seam[i]], row[seam[i] + 1:]), axis=0)
out[i] = row
return out
def carveSeams(img, seams, show=False):
h, w = img.shape[:2]
seam_map = np.tile(np.arange(w), (h, 1))
for i in range(seams):
seam = getSeam(img)
seam_map = removeSeam(seam_map, seam)
img = removeSeam(img, seam)
if show:
funcs.show(img, False)
return img, seam_map
img1 = cv2.imread('image1_small.png')
seam1 = getSeam(img1)
print "show seam"
img1 = showSeam(img1, seam1)
funcs.show(img1)
img3 = cv2.imread('image1_small.png')
img3 = img3
print "carve seam"
img3, _ = carveSeams(img3, 400, True)
funcs.show(img3)
f.write(struct.pack("HHB", h, w, d))
for num in flat:
f.write(struct.pack("B", num))
f.close()
def openImg(filename):
f = file(filename + ".swrd", "rb")
h, w, d = struct.unpack("HHB", f.read(5))
shape = (h, w, d)
nums = []
for i in range(h * w * d):
nums.append(struct.unpack("B", f.read(1)))
img = np.uint8(nums)
img = img.reshape(shape)
#img+=16*np.random.rand(*shape)
return np.uint8(img)
img = cv2.imread("image1.png")
#funcs.show(img)
save(img, "test2")
img2 = openImg("test2")
cv2.imwrite("test2.png", img2)
funcs.show(img2)
#img2=flat.reshape((500,333,3))
#funcs.show(img2)
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)