Mask R-CNN:python-cv全套多目标物体检测

1 说明

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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

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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

4 代码

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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('\n').split('\n')

#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('\n').split('\n')

#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。