# -*- coding: utf-8 -*- import cv2 import os import numpy as np from skimage import measure from matplotlib import pyplot as plt def uniformsample(pgtnp_px2, newpnum): #print(pgtnp_px2) pnum, cnum = pgtnp_px2.shape assert cnum == 2 idxnext_p = (np.arange(pnum, dtype=np.int32) + 1) % pnum pgtnext_px2 = pgtnp_px2[idxnext_p] edgelen_p = np.sqrt(np.sum((pgtnext_px2 - pgtnp_px2) ** 2, axis=1)) edgeidxsort_p = np.argsort(edgelen_p) #print(edgeidxsort_p) # two cases # we need to remove gt points # we simply remove shortest paths if pnum > newpnum: edgeidxkeep_k = edgeidxsort_p[pnum - newpnum:] edgeidxsort_k = np.sort(edgeidxkeep_k) pgtnp_kx2 = pgtnp_px2[edgeidxsort_k] assert pgtnp_kx2.shape[0] == newpnum return pgtnp_kx2 else: edgenum = np.round(edgelen_p * newpnum / np.sum(edgelen_p)).astype(np.int32) for i in range(pnum): if edgenum[i] == 0: edgenum[i] = 1 # after round, it may has 1 or 2 mismatch edgenumsum = np.sum(edgenum) if edgenumsum != newpnum: if edgenumsum > newpnum: id = -1 passnum = edgenumsum - newpnum while passnum > 0: edgeid = edgeidxsort_p[id] if edgenum[edgeid] > passnum: edgenum[edgeid] -= passnum passnum -= passnum else: passnum -= edgenum[edgeid] - 1 edgenum[edgeid] -= edgenum[edgeid] - 1 id -= 1 else: id = -1 edgeid = edgeidxsort_p[id] edgenum[edgeid] += newpnum - edgenumsum assert np.sum(edgenum) == newpnum psample = [] for i in range(pnum): pb_1x2 = pgtnp_px2[i:i + 1] pe_1x2 = pgtnext_px2[i:i + 1] pnewnum = edgenum[i] wnp_kx1 = np.arange(edgenum[i], dtype=np.float32).reshape(-1, 1) / edgenum[i] pmids = pb_1x2 * (1 - wnp_kx1) + pe_1x2 * wnp_kx1 psample.append(pmids) psamplenp = np.concatenate(psample, axis=0) return psamplenp # 这个函数的作用是保证轮廓闭合（轮廓的第一个点和最后一个点能对上） def close_contour(contour): if not np.array_equal(contour[0], contour[-1]): contour = np.vstack((contour, contour[0])) return contour # 这个函数用于将二值掩码转化为多边形，很重要 def binary_mask_to_polygon(root, tolerance=0): # new_data_result 中的二值不是0和1，而是0和255 mask = cv2.imread(root,0) mask = np.array(mask) mask = mask/128 # 255/128 = 1 binary_mask = mask.astype(int) polygons = [] # pad new_data_result to close contours of shapes which start and end at an edge 填充掩码以闭合那些在边缘开始和结束的形状的轮廓 padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0) contours = measure.find_contours(padded_binary_mask, 0.5) # 使用 measure.find_contours 函数寻找填充后的二进制掩码中的所有轮廓。 import matplotlib.pyplot as plt # map = np.zeros([1550, 1550]) # for i in range(len(contours)): # # for point_num in range(len(contours[i])): # point = np.round(contours[i][point_num]).astype(np.int) # # map[point[0],point[1]] += 1 # # # 将ndarray转换为Pillow图像 # from PIL import Image # image = Image.fromarray(map, 'L') # 'L' 模式表示灰度图 # # 保存图像 # image.save(root+'counter111.png') # # 因为matplotlib不能直接显示形状为(512, 512, 1)的图像，我们需要去掉最后一个维度 # image = padded_binary_mask.squeeze() # # # 使用matplotlib显示图像 # plt.imshow(image, cmap='gray') # 使用灰度色图显示 # plt.colorbar() # 显示色标 # plt.show() #唐梓轩添加，因为默认处理单连通区域，所以这里取最大的单连通区域 #max=0 #if len(contours)!=1: # for i,counter1 in enumerate(contours): # if counter1.shape[0]>max: # max=counter1.shape[0] # contours=[counter1] contours = [np.subtract(contour, 1) for contour in contours] # 将轮廓中的每个点的坐标减去1，以补偿之前添加的填充。 for contour in contours: contour = close_contour(contour) # 这一步的作用是保证轮廓闭合（轮廓的第一个点和最后一个点能对上） contour = measure.approximate_polygon(contour, tolerance) if len(contour) < 3: continue contour = np.flip(contour, axis=1) segmentation = contour.ravel().tolist() # after padding and subtracting 1 we may get -0.5 points in our segmentation segmentation = [0 if i < 0 else i for i in segmentation] polygons.append(segmentation) polys=[] # 下面的 for 循环是为了整理轮廓的坐标格式为（x,y）型，不用细看 for j in range(len(polygons)): poly=[] for i in range(int(len(polygons[j])/2)): poly.append([polygons[j][2*i],polygons[j][2*i+1]]) polys.append([np.array(poly)]) return polys if __name__ == '__main__': np.set_printoptions(threshold=np.inf) #root = 'E:\PyCharm-ZRC\PyCharm-Project\DeepSnake_remove_c\multiple_segmentdata\images600/0_mask.jpg' # 修改为你对应的文件路径 root = 'E:\PyCharm-ZRC\PyCharm-Project\DeepSnake_remove_c\multiple_segmentdata\images600_2/1_mask_2.jpg' poly = binary_mask_to_polygon(root) #print(len(poly)) #print(len(poly[0][0])) # 输出331，这个掩膜轮廓上有331个点 #img = cv2.imread(root) #for num in range(len(poly)): # poly2visual=poly[num] # print(poly2visual,poly2visual.shape) # for i in range(poly2visual.shape[0]): # cv2.line(img, (int(poly2visual[i,0]),int(poly2visual[i,1])), (int(poly2visual[(i+1)%poly2visual.shape[0],0]),int(poly2visual[(i+1)%poly2visual.shape[0],1])), color=(255, 0, 255), thickness=2) #cv2.imwrite('/data/tzx/snake-master/visual_result/poly.jpg',img)