南宁门户网站有哪些,如何让百度搜到网站,网站高端网站建设,如何制作网络游戏文章目录 1、任务描述2、MASR RCNN 网络结构3、方法实现4、结果展示5、涉及到的库getPerfProfile 6、参考 1、任务描述 利用 mask rcnn 网络#xff0c;进行图片和视频的目标检测和实例分割
2、MASR RCNN 网络结构 3、方法实现
# Copyright (C) 2018-2019, BigVision LLC (L… 文章目录 1、任务描述2、MASR RCNN 网络结构3、方法实现4、结果展示5、涉及到的库getPerfProfile 6、参考 1、任务描述 利用 mask rcnn 网络进行图片和视频的目标检测和实例分割
2、MASR RCNN 网络结构 3、方法实现
# 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)import cv2 as cv
import argparse
import numpy as np
import os.path
import sys
import random# Initialize the parameters
confThreshold 0.5 # Confidence threshold
maskThreshold 0.3 # Mask thresholdparser argparse.ArgumentParser(descriptionUse this script to run Mask-RCNN object detection and segmentation)
parser.add_argument(--image, helpPath to image file.)
parser.add_argument(--video, helpPath to video file., defaultcars.mp4)
parser.add_argument(--device, defaultgpu, helpDevice to inference on)
args parser.parse_args()
python mask_rcnn.py --image ./images/person.jpg --device cpu
python mask_rcnn.py --video ./cars.mp4 --device cpu
# 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.cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)# Print a label of class.label %.2f % confif classes:assert (classId len(classes))label %s:%s % (classes[classId], label) # person:1.00# Display the label at the top of the bounding boxlabelSize, 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 imageclassMask 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 colorscolorIndex 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 imagemask 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 shapenumClasses masks.shape[1] # 90numDetections boxes.shape[2] # 100frameH frame.shape[0] # 531frameW frame.shape[1] # 800for i in range(numDetections): # traverse top 100 ROIbox boxes[0, 0, i] # (1, 1, 100, 7) - (7,)# array([0. , 0. , 0.99842095, 0.7533724 , 0.152397 , 0.92448074, 0.9131955 ], dtypefloat32)mask masks[i] # (100, 90, 15, 15) - (90, 15, 15)score box[2] # 0.99842095if score confThreshold:classId int(box[1])# Extract the bounding boxleft 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 objectclassMask mask[classId]# Draw bounding box, colorize and show the mask on the imagedrawBox(frame, classId, score, left, top, right, bottom, classMask)# Load names of classes
classesFile mscoco_labels.names
classes None
person
bicycle
car
motorcycle
airplane
bus
train
truck
boat
traffic light
fire hydrantstop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffebackpack
umbrellahandbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottlewine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
couch
potted plant
beddining tabletoilettv
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigeratorbook
clock
vase
scissors
teddy bear
hair drier
toothbrush
with open(classesFile, rt) as f:classes f.read().rstrip(\n).split(\n)# Give the textGraph and weight files for the model
textGraph ./mask_rcnn_inception_v2_coco_2018_01_28.pbtxt
modelWeights ./mask_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb# Load the network
net cv.dnn.readNetFromTensorflow(modelWeights, textGraph)if args.device cpu:net.setPreferableBackend(cv.dnn.DNN_TARGET_CPU)print(Using CPU device)
elif args.device gpu:net.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA)net.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA)print(Using GPU device)# Load the classes
colorsFile colors.txt
with open(colorsFile, rt) as f:colorsStr f.read().rstrip(\n).split(\n)# [0 255 0, 0 0 255, 255 0 0, 0 255 255, 255 255 0, 255 0 255, 80 70 180,# 250 80 190, 245 145 50, 70 150 250, 50 190 190]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)
[array([ 0., 255., 0.]), array([ 0., 0., 255.]), array([255., 0., 0.]), array([ 0., 255., 255.]),
array([255., 255., 0.]), array([255., 0., 255.]), array([ 80., 70., 180.]), array([250., 80., 190.]),
array([245., 145., 50.]), array([ 70., 150., 250.]), array([ 50., 190., 190.])]
winName Mask-RCNN Object detection and Segmentation in OpenCV
cv.namedWindow(winName, cv.WINDOW_NORMAL)outputFile mask_rcnn_out_py.avi
if (args.image):# Open the image fileif not os.path.isfile(args.image):print(Input image file , args.image, doesnt exist)sys.exit(1)cap cv.VideoCapture(args.image)outputFile args.image[:-4] _mask_rcnn_out_py.jpg
elif (args.video):# Open the video fileif not os.path.isfile(args.video):print(Input video file , args.video, doesnt exist)sys.exit(1)cap cv.VideoCapture(args.video)outputFile args.video[:-4] _mask_rcnn_out_py.avi
else:# Webcam inputcap 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:# Get frame from the videohasFrame, frame cap.read()# Stop the program if reached end of videoif 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, swapRBTrue, cropFalse) # (1, 3, 531, 800)# Set the input to the networknet.setInput(blob)# Run the forward pass to get output from the output layersboxes, masks net.forward([detection_out_final, detection_masks])(1, 1, 100, 7) top 100 RoI, (0, classid, score, x0, y0, x1, y1)(100, 90, 15, 15) 100 RoI, 90 classes, 15*15 feature maps size# Extract the bounding box and mask for each of the detected objectspostprocess(boxes, masks)# Put efficiency information.t, _ net.getPerfProfile()label Mask-RCNN 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 boxesif (args.image):cv.imwrite(outputFile, frame.astype(np.uint8));else:vid_writer.write(frame.astype(np.uint8))cv.imshow(winName, frame)根据 bbox 的类别取 mask 输出特征图对应类别的通道特征 classMask mask[classId]
画 mask 的时候先 resize 到 bbox 的大小再借助了 cv2.findContours 和 drawContours 绘制出轮廓
4、结果展示
输入图片 输出结果 输入图片 输出结果 输入图片 输出结果 输入图片 输出结果 输入图片 输出结果 输入图片 输出结果 输入图片 输出结果 看看视频的结果 cars_mask_rcnn_out 5、涉及到的库
getPerfProfile
getPerfProfile 是 OpenCV 库中用于获取深度学习模型推理性能概况的一个函数。该函数主要用于分析模型中各层的执行时间帮助开发者了解模型性能瓶颈和优化方向。
一、功能
getPerfProfile 函数返回一个包含模型各层执行时间的向量或类似结构单位通常为毫秒或秒具体取决于函数实现和调用方式。
通过这个函数开发者可以获取到模型推理过程中每一层所消耗的时间进而分析哪些层是性能瓶颈需要进一步优化。 使用场景
在使用 OpenCV 进行深度学习模型推理时尤其是在对实时性要求较高的应用场景中如视频处理、实时监控系统等使用 getPerfProfile 函数可以帮助开发者评估和优化模型性能。
二、示例代码
import cv2 # 加载预训练模型
net cv2.dnn.readNet(model.xml, model.bin) # 假设有一个输入图像 blob
blob cv2.dnn.blobFromImage(...) # 设置输入并进行推理
net.setInput(blob)
outputs net.forward() # 获取性能概况
t, _ net.getPerfProfile() # 假设 t 是以秒为单位的时间转换为毫秒并打印
print(Inference time: %.2f ms % (t * 1000.0)) # 如果需要更详细的每层时间可以遍历 t
# 注意这里的 t 可能是一个向量包含了多层的执行时间
for layer_idx, layer_time in enumerate(t): print(fLayer {layer_idx}: {layer_time * 1000.0} ms)三、注意事项
getPerfProfile 函数的返回值和单位可能因 OpenCV 的不同版本或不同的深度学习后端如 DNN 模块支持的 TensorFlow、Caffe、PyTorch 等而有所不同。
在使用该函数时请确保您的 OpenCV 版本支持该功能并仔细阅读相关文档以了解其具体用法和注意事项。
由于深度学习模型的复杂性和多样性getPerfProfile 函数提供的性能数据仅供参考实际的优化工作还需要结合模型的具体结构和应用场景进行。
6、参考 论文解读 【Mask RCNN】《Mask R-CNN》 tensorflow 代码解读 Mask RCNN without Mask OpenCV进阶7在 OpenCV中使用 Mask RCNN实现对象检测和实例分割
