colorful.zip

# -*- coding: utf-8 -*- """ Created on Wed Dec 18 23:43:19 2019 @author: 官宇霞 """ import os.path import cv2 import os import numpy as np MAX_FEATURES = 10000 GOOD_MATCH_PERCENT = 0.15 def covert_zip(): img_fold_A = 'E:/QQ/test1/before' img_list1 = os.listdir(img_fold_A) num_imgs1 = len(img_list1) for i in range(num_imgs1): save_fold_A = 'E:/QQ/test1/after' name_A = img_list1[i] path_A = os.path.join(img_fold_A, name_A) im_A = cv2.imread(path_A, 1) if('.tif' in name_A): file_name_temp = name_A[:-4] file_name = os.path.join(save_fold_A , file_name_temp+'.jpg') cv2.imwrite(file_name, im_A) image = cv2.imread(file_name) res = cv2.resize(image, (394,1024), interpolation=cv2.INTER_AREA) cv2.imwrite(file_name,res) else: file_name_temp = name_A[:-4] file_name = os.path.join(save_fold_A , file_name_temp+'.jpg') cv2.imwrite(file_name, im_A) def get_gradient(im) : # Calculate the x and y gradients using Sobel operator grad_x = cv2.Sobel(im,cv2.CV_32F,1,0,ksize=3) grad_y = cv2.Sobel(im,cv2.CV_32F,0,1,ksize=3) # Combine the two gradients grad = cv2.addWeighted(np.absolute(grad_x), 0.5, np.absolute(grad_y), 0.5, 0) return grad def alignImages(im1,im2): # Convert images to grayscale #im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY) #im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY) # Detect ORB features and compute descriptors. orb = cv2.ORB_create(MAX_FEATURES) keypoints1, descriptors1 = orb.detectAndCompute(im1, None) keypoints2, descriptors2 = orb.detectAndCompute(im2, None) #print(descriptors1.shape) #print(descriptors2.shape) # Match features. matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING) matches = matcher.match(descriptors1, descriptors2, None) # Sort matches by score matches.sort(key=lambda x: x.distance, reverse=False) # Remove not so good matches numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT) matches = matches[:numGoodMatches] # Draw top matches imMatches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None) # Extract location of good matches points1 = np.zeros((len(matches), 2), dtype=np.float32) points2 = np.zeros((len(matches), 2), dtype=np.float32) for i, match in enumerate(matches): points1[i, :] = keypoints1[match.queryIdx].pt points2[i, :] = keypoints2[match.trainIdx].pt # Find homography h, mask = cv2.findHomography(points1, points2, cv2.RANSAC) # Use homography height, width = im2.shape im1Reg = cv2.warpPerspective(im1, h, (width, height)) return im1Reg def align(cropImg1,cropImg2): corners1 = cv2.goodFeaturesToTrack(cropImg1,25,0.01,10) m = int(len(corners1)) corners1 = corners1.reshape(m, 2) corners2 = cv2.goodFeaturesToTrack(cropImg2,25,0.01,10) n = int(len(corners2)) corners2 = corners2.reshape(n, 2) points1 = corners1 points2 = corners2 sumX1 = 0 sumY1 = 0 x,y = points1.shape for m in range(x): sumX1 = sumX1 + points1[m][0] sumY1 = sumY1 + points2[m][1] sumX2 = 0 sumY2 = 0 x,y = points2.shape for n in range(x): sumX2 = sumX2 + points2[n][0] sumY2 = sumY2 + points2[n][1] c1 = int((sumX2 / n) - (sumX1 / m)) c2 = int((sumY2 / n) - (sumY1 / m)) cropImg1=np.roll(cropImg1,shift=c1,axis=0) cropImg1=np.roll(cropImg1,shift=c2,axis=1) return cropImg1 def clear(img): img_gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY) gradX = cv2.Sobel(img_gray, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1) gradY = cv2.Sobel(img_gray, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=-1) img_gradient = cv2.subtract(gradX, gradY) img_gradient = cv2.convertScaleAbs(img_gradient) blurred = cv2.blur(img_gradient, (9, 9)) (_, thresh) = cv2.threshold(blurred, 90, 255, cv2.THRESH_BINARY) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25)) closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel) closed = cv2.erode(closed, None, iterations=4) closed = cv2.dilate(closed, None, iterations=4) closed = np.clip(closed, 0, 255) closed = np.array(closed,np.uint8) (cnts, _) = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) c = sorted(cnts, key=cv2.contourArea, reverse=True)[0] rect = cv2.minAreaRect(c) box = np.int0(cv2.boxPoints(rect)) cv2.drawContours(img, [box], -1, (0, 255, 0), 3) height,width,length = img.shape Xs = [i[0] for i in box] Ys = [i[1] for i in box] x1 = min(Xs) x2 = max(Xs) x1_ = (x2-x1)/2-140 x2_ = (x2-x1)/2+140 if x1_< 0: x1_ = 0 if x2_ > width: x2_ = width y1 = min(Ys) y2 = max(Ys) y1_ = (y2 - y1)/2-140 y2_ = (y2 - y1)/2+140 if y1_ < 0: y1_ = 0 if y2_ > height: y2_ = height img2 = img[int(y1_):int(y2_), int(x1_):int(x2_),:] return img2 if __name__ == '__main__': covert_zip() # Read 8-bit color image. # This is an image in which the three channels are # concatenated vertically. for root, dirs, files in os.walk('E:/QQ/test1/after/'): #这里路径需要修改！ for file in files: print(file) #讀入圖像 img_path = 'E:/QQ/test1/after/'+file #这里路径需要修改！ im = cv2.imread(img_path,cv2.IMREAD_GRAYSCALE) # Find the width and height of the color image sz=im.shape height = int(sz[0] / 3); width = sz[1] print(sz) # Extract the three channels from the gray scale image # and merge the three channels into one color image im_color = np.zeros((height,width,3), dtype=np.uint8 ) for i in range(0,3) : im_color[:,:,i] = im[ i * height:(i+1) * height,:] # Allocate space for aligned image # The blue and green channels will be aligned to the red channel. # So copy the red channel # Define motion model warp_mode = cv2.MOTION_HOMOGRAPHY # Set the warp matrix to identity. if warp_mode == cv2.MOTION_HOMOGRAPHY : warp_matrix = np.eye(3, 3, dtype=np.float32) else : warp_matrix = np.eye(2, 3, dtype=np.float32) # Set the stopping criteria for the algorithm. criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 5000, 1e-10) # Warp the blue and green channels to the red channel for i in range(0,2) : im_color[:,:,i]=alignImages(im_color[:,:,i],im_color[:,:,2]) # Show final output if '.jpg' in file: im_color=clear(im_color) cv2.imwrite('E:/QQ/test1/result/a/'+file,im_color) #这里路径需要修改！