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OpenCV中使用Mask R-CNN进行实例分割

OpenCV 迷途小书童 0评论

软硬兼环境

  • windows 10 64bit
  • nivdia gtx 1066
  • opencv 4.4.0

简介

Mask R-CNN是在原有的R-CNN基础上实现了区域ROI的像素级别分割。tensorflow框架有个扩展模块叫做models,里面包含了很多预训练的网络模型,提供给开发者直接使用或者迁移学习使用,tensorflow object detection model zone中现在有四个使用不同骨干网(InceptionV2ResNet50ResNet101Inception-ResnetV2)的Mask R-CNN模型,这些模型都是在MSCOCO数据集上训练出来的,其中使用Inception的模型是这四个中最快的,本文也是使用该模型。

模型下载

首先需要下载Mask R-CNN网络模型,地址是: http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_v2_coco_2018_01_28.tar.gz, 下载后解压出里面的模型文件frozen_inference_graph.pb

代码示例

import cv2 as cv
import argparse
import numpy as np
import os.path
import sys
import random

# 设置目标检测的置信度阈值和Mask二值化分割阈值
confThreshold = 0.5
maskThreshold = 0.3

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()


# 画bounding box
def drawBox(frame, classId, conf, left, top, right, bottom, classMask):
    # Draw a bounding box.
    cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)

    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])
    cv.rectangle(frame, (left, top - round(1.5 * labelSize[1])), (left + round(1.5 * labelSize[0]), top + baseLine),
                 (255, 255, 255), cv.FILLED)
    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]

    # 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)
    contours, hierarchy = cv.findContours(mask, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
    cv.drawContours(frame[top:bottom + 1, left:right + 1], contours, -1, color, 3, cv.LINE_8, hierarchy, 100)


# 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)


# mscoco_labels.names包含MSCOCO所有标注对象的类名称,共80个类别
classesFile = "mscoco_labels.names"
classes = None
with open(classesFile, 'rt') as f:
    classes = f.read().rstrip('\n').split('\n')

# 定义如何加载模型权重
textGraph = "./mask_rcnn_inception_v2_coco_2018_01_28.pbtxt"

# 模型权重
modelWeights = "./frozen_inference_graph.pb"

# 使用dnn模块加载模型
net = cv.dnn.readNetFromTensorflow(modelWeights, textGraph)

# 可以使用GPU或者CPU进行推断
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)

# colors.txt是在图像上标出实例时,所属类显示的颜色值
colorsFile = "colors.txt";
with open(colorsFile, 'rt') as f:
    colorsStr = f.read().rstrip('\n').split('\n')
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)

winName = 'Mask-RCNN Object detection and Segmentation in OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)

# 处理后的结果保存到视频中
outputFile = "mask_rcnn_out.avi"
if (args.image):
    # Open the image file
    if not os.path.isfile(args.image):
        print("Input image file ", args.image, " doesn't exist")
        sys.exit(1)
    cap = cv.VideoCapture(args.image)
    outputFile = args.image[:-4] + '_mask_rcnn_out.jpg'
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.avi'
else:
    # Webcam input
    cap = cv.VideoCapture(0)

# 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))))

while cv.waitKey(1) < 0:

    # 对每一帧数据进行处理
    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)

    # 得到目标bounding box和mask
    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 = '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))

    # Write the frame with the detection boxes
    if (args.image):
        cv.imwrite(outputFile, frame.astype(np.uint8));
    else:
        vid_writer.write(frame.astype(np.uint8))

    cv.imshow(winName, frame)

上述代码同时支持图片和视频

python demo.py --video test.mp4

opencv_mask_rcnn

OpenCV教程

https://xugaoxiang.com/category/ai/opencv/

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