python opencv多边形掩膜 掩码

目录

掩膜方法1：

提取掩摸方法2：提取掩膜内的图片：其余是0

改变掩摸内容：

掩摸反向选择操作，比如改变背景：

python画多边形：

多边形ROI，主要利用鼠标交互进行绘制：

多边形ROI实例，保存csv

掩膜方法1：

坐标点需要顺时针顺序。

img = cv2.imread(r'd:/nv1.jpg') shape = img.shape points_704 = np.array([[80, 130], [140, 130], [170, 50], [225, 50]]) img_origin = img.copy() cv2.fillConvexPoly(img, points_704, 1) bitwisexor = cv2.bitwise_xor(img, img_origin) cv2.imshow("asdf",bitwisexor) cv2.waitKey(0)

提取掩摸方法2：提取掩膜内的图片：其余是0

# coding=utf-8 import numpy as np import cv2 image = cv2.imread("d:/bgs.jpg") b = np.array([[[100,100], [250,100], [300,220],[100,230]]], dtype = np.int32) im = np.zeros(image.shape[:2], dtype = "uint8") cv2.polylines(im, b, 1, 255) cv2.fillPoly(im, b, 255) mask = im cv2.imshow("Mask", mask) masked = cv2.bitwise_and(image, image, mask=mask) cv2.imshow("Mask to Image", masked) cv2.waitKey(0)

改变掩摸内容：

# -*- coding: utf-8 -*- import os import numpy as np import cv2 import cv2 import numpy as np mask_threth = 50 img = cv2.imread('d:/rat_466-100-100.jpg') #自己截图一张图片就行，要大于下面的坐标点 # binary mask coordinates = [] coordinate1 = [[[40, 135], [168,132], [264,330], [2,328]]] coordinate2 = [[[300, 300], [600,300], [600,600], [300,600]]] coordinate1 = np.array(coordinate1) coordinate2 = np.array(coordinate2) coordinates.append(coordinate1) coordinates.append(coordinate2) mask = np.zeros(img.shape[:2], dtype=np.int8) mask = cv2.fillPoly(mask, coordinates, 255) cv2.imwrite('./mask1.png', mask) bbox_mask = mask color_mask = np.array([0, 0, 255], dtype=np.uint8) bbox_mask = bbox_mask.astype(np.bool) # cv2.imwrite('./bbox_mask.png', bbox_mask) img[bbox_mask] = color_mask * 0.5 cv2.imshow("sadf",img) cv2.waitKey()

掩摸反向选择操作，比如改变背景：

# -*- coding: utf-8 -*- import os import numpy as np import cv2 import cv2 import numpy as np mask_threth = 50 img = cv2.imread('d:/rat_466-100-100.jpg') #自己截图一张图片就行，要大于下面的坐标点 # binary mask coordinates = [] coordinate1 = [[[40, 135], [168,132], [264,330], [2,328]]] coordinate2 = [[[300, 300], [600,300], [600,600], [300,600]]] coordinate1 = np.array(coordinate1) coordinate2 = np.array(coordinate2) coordinates.append(coordinate1) coordinates.append(coordinate2) bbox_mask = np.zeros(img.shape[:2], dtype=np.int8) bbox_mask[::]=255 bbox_mask = cv2.fillPoly(bbox_mask, coordinates, 0) cv2.imshow("mask",bbox_mask) color_mask = np.array([0, 0, 255], dtype=np.uint8)* 0.5 bbox_mask = bbox_mask.astype(np.bool) img[bbox_mask] =[0, 0, 255]# color_mask cv2.imshow("sadf",img) cv2.waitKey()

如果是两张图叠加：

改为同等大小的图的掩摸部分：

src2[bbox_mask] = img_o[bbox_mask]

python画多边形：

注意：a为多边形坐标点，需要是三维的，比如[1,4,2]

外面的是list或者numpy矩阵都支持。

 import numpy as np import cv2 a = np.array([[10,10], [50,10], [100,100], [10,80]], dtype = np.int32) b = np.array([[[50,100], [200,230], [150,200], [150,220]]], dtype = np.int32) print(a.shape) im = np.zeros([240, 320,3], dtype = np.uint8) cv2.polylines(im, [a], 1, (0,0,255)) cv2.fillPoly(im, b, (0,255,255)) cv2.imshow("image",im) cv2.waitKey()

多边形ROI，主要利用鼠标交互进行绘制：

1. 单击左键，选择多边形的点；

2. 单击右键，删除最近一次选择的点；

3. 单击中键，确定ROI区域并可视化。

4. 按”S“键，将多边形ROI区域的点保存到本地”config.pkl”文件中。

话不多说，以下是核心代码

import cv2    import numpy as np import joblib    pts = []  # 用于存放点       # 统一的：mouse callback function def draw_roi(event, x, y, flags, param):     img2 = img.copy()        if event == cv2.EVENT_LBUTTONDOWN:  # 左键点击，选择点         pts.append((x, y))        if event == cv2.EVENT_RBUTTONDOWN:  # 右键点击，取消最近一次选择的点         pts.pop()        if event == cv2.EVENT_MBUTTONDOWN:  # 中键绘制轮廓         mask = np.zeros(img.shape, np.uint8)         points = np.array(pts, np.int32)         points = points.reshape((-1, 1, 2))         # 画多边形         mask = cv2.polylines(mask, [points], True, (255, 255, 255), 2)         mask2 = cv2.fillPoly(mask.copy(), [points], (255, 255, 255))  # 用于求 ROI         mask3 = cv2.fillPoly(mask.copy(), [points], (0, 255, 0))  # 用于 显示在桌面的图像            show_image = cv2.addWeighted(src1=img, alpha=0.8, src2=mask3, beta=0.2, gamma=0)            cv2.imshow("mask", mask2)         cv2.imshow("show_img", show_image)            ROI = cv2.bitwise_and(mask2, img)         cv2.imshow("ROI", ROI)         cv2.waitKey(0)        if len(pts) > 0:         # 将pts中的最后一点画出来         cv2.circle(img2, pts[-1], 3, (0, 0, 255), -1)        if len(pts) > 1:         # 画线         for i in range(len(pts) - 1):             cv2.circle(img2, pts[i], 5, (0, 0, 255), -1)  # x ,y 为鼠标点击地方的坐标             cv2.line(img=img2, pt1=pts[i], pt2=pts[i + 1], color=(255, 0, 0), thickness=2)        cv2.imshow('image', img2)       # 创建图像与窗口并将窗口与回调函数绑定 img = cv2.imread("111.jpg")    cv2.namedWindow('image') cv2.setMouseCallback('image', draw_roi) print("[INFO] 单击左键：选择点，单击右键：删除上一次选择的点，单击中键：确定ROI区域") print("[INFO] 按‘S'确定选择区域并保存") print("[INFO] 按 ESC 退出")    while True:     key = cv2.waitKey(1) & 0xFF     if key == 27:         break     if key == ord("s"):         saved_data = {             "ROI": pts         }         joblib.dump(value=saved_data, filename="config.pkl")         print("[INFO] ROI坐标已保存到本地.")         break cv2.destroyAllWindows()

绘制不规则多边形：

然后按鼠标中间

多边形ROI实例，保存csv

import cv2 import numpy as np import pandas as pd from skimage import io from get_point import cross_point,GetCrossPoint,Point,Line label_file='S1.csv' df_label = pd.read_csv(label_file) def get_temper(value): return value/ 100 - 273.15 aaa=[] import pandas as pd headers = ['file_name', 'max_temp', 'min_temp', 'mean_temp','len'] datas=[] max_temp=0 min_temp=100 for index_a, data in df_label.iterrows(): id_path="S1/"+ data["ID"]+".tiff" img_path="S1/"+ data["ID"].replace("R","N")+".jpg" img_show=cv2.imread(img_path) img_tif = io.imread(id_path) end=73 for ai in range(73): p_x = data['x' + str(ai)] p_y = data['y' + str(ai)] if np.isnan(p_x) or np.isnan(p_y): if ai == 43: end = 42 break print("end",end) pts = [] pts_right=[] for ai in range(end): p_x=data['x'+str(ai)] p_y=data['y'+str(ai)] if np.isnan(p_x) or np.isnan(p_y): continue p_x=int(min(img_show.shape[1]-1,max(0,p_x))) p_y=int(min(img_show.shape[0]-1,max(0,p_y))) font = cv2.FONT_HERSHEY_SIMPLEX if end==42: if ai > 27 and ai < 36: # mouse continue cv2.circle(img_show, (int(p_x), int(p_y)), 2, color=(0, 255, 0)) cv2.putText(img_show, str(ai ), (p_x, p_y + 5), font, 0.2, (0, 0, 255), 1, cv2.LINE_AA) # print(ai,p_x, p_y, round(img_tif[int(p_y)][int(p_x)] , 2)) elif ai>16 and ai<22: #nose continue cv2.circle(img_show, (int(p_x), int(p_y)), 2, color=(0, 255, 0)) cv2.putText(img_show, str(ai), (p_x, p_y + 5), font, 0.2, (0, 0, 255), 1, cv2.LINE_AA) pts.append([p_x, p_y]) elif ai>=39 and ai<=44: #forehead continue cv2.circle(img_show, (int(p_x), int(p_y)), 2, color=(0, 255, 0)) cv2.putText(img_show, str(ai), (p_x, p_y + 5), font, 0.2, (0, 0, 255), 1, cv2.LINE_AA) pts.append([p_x, p_y]) elif (ai >= 10 and ai <= 11) or ( ai>37 and ai<=38): # xiaba # continue cv2.circle(img_show, (int(p_x), int(p_y)), 2, color=(0, 255, 0)) cv2.putText(img_show, str(ai), (p_x, p_y + 5), font, 0.2, (0, 0, 255), 1, cv2.LINE_AA) pts.append([p_x, p_y]) else: continue cv2.circle(img_show, (int(p_x), int(p_y)), 2, color=(0, 255, 0)) cv2.putText(img_show, str(ai), (p_x, p_y + 5), font, 0.2, (0, 0, 255), 1, cv2.LINE_AA) pts.append([p_x, p_y]) elif end==73: if ai > 27 and ai < 36: #nose continue cv2.circle(img_show, (int(p_x), int(p_y)), 2, color=(0, 255, 0)) cv2.putText(img_show, str(ai ), (p_x, p_y + 5), font, 0.2, (0, 0, 255), 1, cv2.LINE_AA) # print(ai,p_x, p_y, round(img_tif[int(p_y)][int(p_x)] , 2)) pts.append([p_x, p_y]) elif ai >= 68 and ai <= 72: # forehead continue cv2.circle(img_show, (int(p_x), int(p_y)), 2, color=(0, 255, 0)) cv2.putText(img_show, str(ai), (p_x, p_y + 5), font, 0.2, (0, 0, 255), 1, cv2.LINE_AA) pts.append([p_x, p_y]) elif (ai >= 6 and ai <= 10) or(ai==57): # xiaba cv2.circle(img_show, (int(p_x), int(p_y)), 2, color=(0, 255, 0)) cv2.putText(img_show, str(ai), (p_x, p_y + 5), font, 0.2, (0, 0, 255), 1, cv2.LINE_AA) pts.append([p_x, p_y]) else: continue cv2.circle(img_show, (int(p_x), int(p_y)), 2, color=(0, 255, 0)) cv2.putText(img_show, str(ai), (p_x, p_y + 5), font, 0.2, (0, 0, 255), 1, cv2.LINE_AA) pts.append([p_x, p_y]) # if ai>47 and ai<68: # cv2.circle(img_show, (int(p_x), int(p_y)), 2, color=(0, 255, 0)) # cv2.putText(img_show, str(ai + 1), (p_x,p_y+5), font, 0.2, (0, 0, 255), 1, cv2.LINE_AA) # # print(ai,p_x, p_y, round(img_tif[int(p_y)][int(p_x)] , 2)) # pts.append([p_x, p_y]) if len(pts)>2: mask = np.zeros(img_tif.shape, np.uint8) points = np.array(pts, np.int32) points = points.reshape((-1, 1, 2)) mask2 = cv2.fillPoly(mask.copy(), [points], (255, 255, 255)) # 用于求 ROI ROI=img_tif[mask2 > 0] b = np.sum(ROI) max_v=np.around(get_temper(max(ROI)),2) min_v=np.around(get_temper(min(ROI)),2) avg_v=np.around(get_temper(b/len(ROI)),2) # print("max",max_v,"min",min_v,"avg",avg_v) max_temp=max(max_temp,max_v) min_temp=min(min_temp,min_v) aaa.append(get_temper(b/len(ROI))) gdp1 = pd.Series([img_path, max_v, min_v, avg_v, len(ROI)],index=headers) datas.append(gdp1) cv2.imshow("ROI", mask2) cv2.imshow("img_show", img_show) cv2.waitKey(1) gdp4=pd.concat(datas,axis=1).T gdp4.to_csv("xiaba.csv",index=False) node_data=np.asarray(aaa) noze_avg= np.sum(node_data)/len(node_data) print(noze_avg,len(node_data),"max",max_temp,"min",min_temp)