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import cv2
import numpy as np
from skimage.util import view_as_windows
from skimage.exposure import match_histograms
class ImgProcess:
def __init__(self, img1 = None, img2 = None):
self.img1 = None
self.img2 = None
self.img1LAB = None
self.img2LAB = None
self.load(img1, img2)
def load(self, img1 = None, img2 = None):
if img1 != None:
self.img1 = cv2.cvtColor(np.array(img1), cv2.COLOR_RGB2BGR)
if img2 != None:
self.img2 = cv2.cvtColor(np.array(img2), cv2.COLOR_RGB2BGR)
def isImageSet(self):
if type(self.img1) == np.ndarray and type(self.img2) == np.ndarray:
# if self.img1 != None and self.img2 != None:
return True
else:
print(f"Error! Images not set!\nImage1Set = {self.img1 != None}, Image1Type = {type(self.img1)}\nImage2Set = {self.img2 != None}, Image2Type = {type(self.img2)}")
return False
def __changeGrayaxisToRGB(self):
if len(self.img1.shape) == 3:
if self.img1.shape[2] == 1:
self.img1 = cv2.cvtColor(self.img1,cv2.COLOR_GRAY2BGR)
else:
self.img1 = cv2.cvtColor(self.img1,cv2.COLOR_GRAY2BGR)
if len(self.img2.shape) == 3:
if self.img2.shape[2] == 1:
self.img2 = cv2.cvtColor(self.img2,cv2.COLOR_GRAY2BGR)
else:
self.img2 = cv2.cvtColor(self.img2,cv2.COLOR_GRAY2BGR)
def __convertRGBToLAB(self):
self.img1LAB = cv2.cvtColor(self.img1,cv2.COLOR_BGR2LAB)
self.img2LAB = cv2.cvtColor(self.img2,cv2.COLOR_BGR2LAB)
def __calculateLABColorMeanAndStd(self):
img1mean_lst = []
img1std_lst = []
img2mean_lst = []
img2std_lst = []
for c in range(self.img1LAB.shape[2]):
img1mean_lst.append(np.mean(self.img1LAB[:, :, c]))
std1 = np.std(self.img1LAB[:, :, c])
img2mean_lst.append(np.mean(self.img2LAB[:, :, c]))
std2 = np.std(self.img2LAB[:, :, c])
img1std_lst.append(std1 if std1 != 0 else (std2 if std2 != 0 else 1))
img2std_lst.append(std2 if std2 != 0 else 1)
return (img1mean_lst, img1std_lst), (img2mean_lst, img2std_lst)
def histogramMatching(self):
matched = match_histograms(self.img1, self.img2, channel_axis=-1)
matched_rgb = cv2.cvtColor(matched.astype(np.uint8), cv2.COLOR_BGR2RGB)
return matched_rgb
def transferColor(self, funPercent = 1.0):
print("Transferring ...")
output_img = None
if self.isImageSet():
self.__changeGrayaxisToRGB()
self.__convertRGBToLAB()
(img1mean_lst, img1std_lst), (img2mean_lst, img2std_lst) = self.__calculateLABColorMeanAndStd()
img1_trnsfrmd = []
for c in range(self.img1LAB.shape[2]):
img1_star = self.img1LAB[:, :, c] - img1mean_lst[c]
img1_dash = img1_star * (img2std_lst[c] / (funPercent * img1std_lst[c]))
img1_trnsfrmd.append(img1_dash + img2mean_lst[c])
img1_trnsfrmd_arr = np.uint8(np.clip(np.rint(np.array(img1_trnsfrmd)), 0, 255))
img1_trnsfrmd_arr = np.moveaxis(img1_trnsfrmd_arr, 0, -1)
output_img = cv2.cvtColor(img1_trnsfrmd_arr,cv2.COLOR_LAB2RGB)
return output_img
def transferTexture1(self, img1, img2):
img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0]))
# Generate Gaussian pyramid for img1
g1 = img1.copy()
gp1 = [g1]
for i in range(6):
g1 = cv2.pyrDown(g1)
gp1.append(g1)
# Generate Gaussian pyramid for img2
g2 = img2.copy()
gp2 = [g2]
for i in range(6):
g2 = cv2.pyrDown(g2)
gp2.append(g2)
# Generate Laplacian Pyramid for img1
lp1 = [gp1[5]]
for i in range(5, 0, -1):
GE = cv2.pyrUp(gp1[i])
print(GE.shape, gp1[i - 1].shape)
GE = cv2.resize(GE, (gp1[i-1].shape[1], gp1[i-1].shape[0]))
print(GE.shape, gp1[i - 1].shape)
L = cv2.subtract(gp1[i - 1], GE)
lp1.append(L)
# Generate Laplacian Pyramid for img2
lp2 = [gp2[5]]
for i in range(5, 0, -1):
GE = cv2.pyrUp(gp2[i])
GE = cv2.resize(GE, (gp2[i-1].shape[1], gp2[i-1].shape[0]))
L = cv2.subtract(gp2[i - 1], GE)
lp2.append(L)
# Now add left and right halves of images in each level
LS = []
for l1, l2 in zip(lp1, lp2):
rows, cols, dpt = l1.shape
ls = np.hstack((l1[:, 0:cols // 2], l2[:, cols // 2:]))
LS.append(ls)
# Reconstruct the image
ls_ = LS[0]
for i in range(1, 6):
ls_ = cv2.pyrUp(ls_)
ls_ = cv2.resize(ls_, (LS[i].shape[1], LS[i].shape[0]))
ls_ = cv2.add(ls_, LS[i])
# Convert back to RGB for Gradio to display
texture_transferred = cv2.cvtColor(ls_, cv2.COLOR_BGR2RGB)
return texture_transferred
def transferTexture2(self, img1, img2):
img2 = cv2.resize(np.array(img2), (img1.shape[1], img1.shape[0]))
# Convert both images to numpy arrays (in BGR format for OpenCV)
# img1 = np.array(img1)
# img2 = np.array(img2)
patch_size = 24 # Patch size in pixels
overlap_size = 8
texture_patches = view_as_windows(img2, (patch_size, patch_size, 3), step=patch_size - overlap_size)
texture_patches = texture_patches.reshape(-1, patch_size, patch_size, 3)
# Create output image
output_image = np.zeros_like(img1)
# Place patches in the output image by matching overlaps
for i in range(0, img1.shape[0] - patch_size + 1, patch_size - overlap_size):
for j in range(0, img1.shape[1] - patch_size + 1, patch_size - overlap_size):
# Select a random patch to blend (for simplicity)
best_patch = texture_patches[np.random.randint(len(texture_patches))]
# Blend best patch into output with minimal seams
output_image[i:i + patch_size, j:j + patch_size] = best_patch
self.blend_patches(output_image, best_patch, i, j, overlap_size, patch_size)
return output_image
def loadAndTransfer(self, img1, img2, funPercent = 1.0):
self.load(img1, img2)
color_img1 = self.transferColor(funPercent)
color_img2 = self.histogramMatching()
return color_img1, color_img2
#return self.transferTexture1(color_img, self.img2)
#return self.transferTexture2(color_img, self.img2) |