MaskRcv全套多目标物体检测

　　1 说明
　　====
　　1.1 Mask R-CNN是一个实例分割（Instance segmentation）算法，可以用来做＂目标检测＂、＂目标实例分割＂、＂目标关键点检测＂。
　　1.2 即实例分割需要在语义分割的基础上对同类物体进行更精细，给予不同颜色的分割。
　　1.3 Mask R-CNN是一个非常灵活的框架，可以增加不同的分支完成不同的任务，可以完成目标分类、目标检测、语义分割、实例分割、人体姿势识别等多种任务，真不愧是一个好算法！
　　2 准备
　　=====
　　2.1 环境：python3.8+deepin-linux深度操作系统+微软编辑器vscode。
　　2.2 软件：opencv4.2.0+numpy1.19.4
　　2.3 代码来源：对代码进行修改bug，注释，调试，方便大家使用。https://github.com/spmallick/learnopencv #里的Mask-RCNN文件夹，感谢开源，别忘了给他个星星！# Copyright (C) 2018-2019, BigVision LLC (LearnOpenCV.com), All Rights Reserved.  # Author : Sunita Nayak # Article : https://www.learnopencv.com/deep-learning-based-object-detection-and-instance-segmentation-using-mask-r-cnn-in-opencv-python-c/ # License: BSD-3-Clause-Attribution (Please read the license file.) # This work is based on OpenCV samples code (https://opencv.org/license.html)
　　2.4 frozen_inference_graph.pb需要自己下载#wget http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_v2_coco_2018_01_28.tar.gz #并解压放在代码目录下
　　3 效果图
　　图片效果图
　　视频效果
　　4 代码
　　=====
　　4.1 操作方法#本机操作方法，在代码目录下，打开终端，输入即可 #图片检测方法 #python3.8 11.py --image=12.jpeg #视频mp4文件检测 #python3.8 11.py --video=cars.mp4 #打开摄像头检测 #python3.8 11.py
　　4.2 完整代码：#第1步：导入模块 import cv2 as cv import argparse import numpy as np import os.path import sys import random  #第2步：初始参数和终端参数说明和设定 # Initialize the parameters confThreshold = 0.5  # Confidence threshold maskThreshold = 0.3  # Mask threshold  parser = argparse.ArgumentParser(description=＂Use this script to run Mask-RCNN object detection and segmentation＂) parser.add_argument(＂--image＂, help=＂Path to image file＂) parser.add_argument(＂--video＂, help=＂Path to video file.＂) args = parser.parse_args()  #第3步：画框函数 # Draw the predicted bounding box, colorize and show the mask on the image def drawBox(frame, classId, conf, left, top, right, bottom, classMask):     # Draw a bounding box.     #画框     cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)     #文字标签     # Print a label of class.     label = ＂%.2f＂ % conf     if classes:         assert(classId < len(classes))         label = ＂%s:%s＂ % (classes[classId], label)          # Display the label at the top of the bounding box     labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)     top = max(top, labelSize[1])     #画框，天蓝色的框     cv.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine), (255, 255, 255), cv.FILLED)     #画框上的文字输出，黑色的字     cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0,0,0), 1)      # Resize the mask, threshold, color and apply it on the image     classMask = cv.resize(classMask, (right - left + 1, bottom - top + 1))     mask = (classMask > maskThreshold)     roi = frame[top:bottom+1, left:right+1][mask]          #颜色mask掩膜的可变颜色设定     # color = colors[classId%len(colors)]     # Comment the above line and uncomment the two lines below to generate different instance colors     colorIndex = random.randint(0, len(colors)-1)     color = colors[colorIndex]      frame[top:bottom+1, left:right+1][mask] = ([0.3*color[0], 0.3*color[1], 0.3*color[2]] + 0.7 * roi).astype(np.uint8)      # Draw the contours on the image     mask = mask.astype(np.uint8)     #查找边界     #im2, contours, hierarchy = cv.findContours(mask,cv.RETR_TREE,cv.CHAIN_APPROX_SIMPLE) #bug     contours, hierarchy = cv.findContours(mask,cv.RETR_TREE,cv.CHAIN_APPROX_SIMPLE) #修改bug，去掉一个im2      #画识别物体的边界     cv.drawContours(frame[top:bottom+1, left:right+1], contours, -1, color, 3, cv.LINE_8, hierarchy, 100)  #第4步：预侦测 # For each frame, extract the bounding box and mask for each detected object def postprocess(boxes, masks):     # Output size of masks is NxCxHxW where     # N - number of detected boxes     # C - number of classes (excluding background)     # HxW - segmentation shape     numClasses = masks.shape[1]     numDetections = boxes.shape[2]      frameH = frame.shape[0]     frameW = frame.shape[1]      for i in range(numDetections):         box = boxes[0, 0, i]         mask = masks[i]         score = box[2]         if score > confThreshold:             classId = int(box[1])                          # Extract the bounding box             left = int(frameW * box[3])             top = int(frameH * box[4])             right = int(frameW * box[5])             bottom = int(frameH * box[6])                          left = max(0, min(left, frameW - 1))             top = max(0, min(top, frameH - 1))             right = max(0, min(right, frameW - 1))             bottom = max(0, min(bottom, frameH - 1))                          # Extract the mask for the object             classMask = mask[classId]              # Draw bounding box, colorize and show the mask on the image             drawBox(frame, classId, score, left, top, right, bottom, classMask)  #第5步：加载文件 #识别物的分类名文件 # Load names of classes classesFile = ＂mscoco_labels.names＂ classes = None with open(classesFile, ＂rt＂) as f:    classes = f.read().rstrip(＂ ＂).split(＂ ＂)  #模型 # Give the textGraph and weight files for the model textGraph = ＂./mask_rcnn_inception_v2_coco_2018_01_28.pbtxt＂ #缺少这个frozen_inference_graph.pb，下载地址 #wget http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_v2_coco_2018_01_28.tar.gz  modelWeights = ＂./mask_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb＂  #加载网络 # Load the network net = cv.dnn.readNetFromTensorflow(modelWeights, textGraph) net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV) net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)  # Load the classes colorsFile = ＂colors.txt＂ with open(colorsFile, ＂rt＂) as f:     colorsStr = f.read().rstrip(＂ ＂).split(＂ ＂)  #颜色列表，可变的 colors = [] #[0,0,0] for i in range(len(colorsStr)):     rgb = colorsStr[i].split(＂ ＂)     color = np.array([float(rgb[0]), float(rgb[1]), float(rgb[2])])     colors.append(color)   #第6步：窗口设置和保存文件设置 winName = ＂Mask-RCNN Object detection and Segmentation in OpenCV＂ cv.namedWindow(winName, cv.WINDOW_NORMAL)  #视频保存 outputFile = ＂mask_rcnn_out_py.avi＂ #判断 if (args.image):     # Open the image file，图片的     if not os.path.isfile(args.image):         print(＂Input image file ＂, args.image, ＂ doesn＂t exist＂)         sys.exit(1)     #获取图片和保存     cap = cv.VideoCapture(args.image)     outputFile = args.image[:-4]+＂_mask_rcnn_out_py.jpg＂ #视频mp4文件的 elif (args.video):     # Open the video file     if not os.path.isfile(args.video):         print(＂Input video file ＂, args.video, ＂ doesn＂t exist＂)         sys.exit(1)     cap = cv.VideoCapture(args.video)     outputFile = args.video[:-4]+＂_mask_rcnn_out_py.avi＂ else:     # Webcam input，摄像头视频     cap = cv.VideoCapture(0)  #如果不是图片，需要保存为视频方法 # Get the video writer initialized to save the output video if (not args.image):     vid_writer = cv.VideoWriter(outputFile, cv.VideoWriter_fourcc(＂M＂,＂J＂,＂P＂,＂G＂), 28, (round(cap.get(cv.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv.CAP_PROP_FRAME_HEIGHT))))   #第7步：循环 while cv.waitKey(1) < 0:          # Get frame from the video     hasFrame, frame = cap.read()          # Stop the program if reached end of video     if not hasFrame:         print(＂Done processing !!!＂)         print(＂Output file is stored as ＂, outputFile)         cv.waitKey(3000)         break      # Create a 4D blob from a frame.     blob = cv.dnn.blobFromImage(frame, swapRB=True, crop=False)      # Set the input to the network     net.setInput(blob)      # Run the forward pass to get output from the output layers     boxes, masks = net.forward([＂detection_out_final＂, ＂detection_masks＂])      # Extract the bounding box and mask for each of the detected objects     postprocess(boxes, masks)      # Put efficiency information.     t, _ = net.getPerfProfile()     label = ＂Mask-RCNN on 2.5 GHz Intel Core i7 CPU, Inference time for a frame : %0.0f ms＂ % abs(t * 1000.0 / cv.getTickFrequency())     cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))          #保存判断     # Write the frame with the detection boxes     #图片就保存为图片     if (args.image):         cv.imwrite(outputFile, frame.astype(np.uint8))     else:         #视频就保存为视频         vid_writer.write(frame.astype(np.uint8))     #显示图片和视频标示过程     cv.imshow(winName, frame)
　　自己整理并分享。