如何在 GPU 上运行对象检测？

问题描述

该程序正在从网络摄像头检测事物，但速度很慢，所以我如何使其更快以获得更好的 FPS，以及我如何使用 GPU 进行更快的检测和更好的性能。我如何才能使它完美。在这个程序中，我使用了 Yolo 配置和带有 coco 数据集的权重。

import cv2
import numpy as np


net = cv2.dnn.readNet('yolov4-custom.cfg','yolov4.weights')

classes = []
with open("coco.names","r") as f:
    classes = f.read().splitlines()

cap = cv2.VideoCapture(0)
#cap = cv2.VideoCapture('videoplayback.mp4')
font = cv2.FONT_HERShey_PLAIN
colors = np.random.uniform(0,255,size=(100,3))

while True:
    _,img = cap.read()
    height,width,_ = img.shape

    blob = cv2.dnn.blobFromImage(img,1/255,(416,416),(0,0),swapRB=True,crop=False)
    net.setInput(blob)
    output_layers_names = net.getUnconnectedOutLayersNames()
    layerOutputs = net.forward(output_layers_names)

    Boxes = []
    confidences = []
    class_ids = []

    for output in layerOutputs:
        for detection in output:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.2:
                center_x = int(detection[0]*width)
                center_y = int(detection[1]*height)
                w = int(detection[2]*width)
                h = int(detection[3]*height)

                x = int(center_x - w/2)
                y = int(center_y - h/2)

                Boxes.append([x,y,w,h])
                confidences.append((float(confidence)))
                class_ids.append(class_id)

    indexes = cv2.dnn.NMSBoxes(Boxes,confidences,0.2,0.4)

    if len(indexes)>0:
        for i in indexes.flatten():
            x,h = Boxes[i]
            label = str(classes[class_ids[i]])
            confidence = str(round(confidences[i],2))
            color = colors[i]
            cv2.rectangle(img,(x,y),(x+w,y+h),color,2)
            cv2.putText(img,label + " " + confidence,y+20),font,2,(255,255),2)

    cv2.imshow('Image',img)
    key = cv2.waitKey(1)
    if key==27:
        break

cap.release()
cv2.destroyAllWindows()

解决方法

要使用 Gpu，我们必须编译 opencv，这可以在博客中按如下方式完成：https://haroonshakeel.medium.com/build-opencv-4-4-0-with-cuda-gpu-support-on-windows-10-without-tears-aa85d470bcd0

然后添加两行，这将检测 Gpu 并且程序将在 GPU 上运行。

import cv2
import numpy as np


net = cv2.dnn.readNet('yolov4-custom.cfg','yolov4.weights')

classes = []
with open("coco.names","r") as f:
    classes = f.read().splitlines()

# this below two line will help to run the detetection.
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)

net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

cap = cv2.VideoCapture(0)
#cap = cv2.VideoCapture('videoplayback.mp4')
font = cv2.FONT_HERSHEY_PLAIN
colors = np.random.uniform(0,255,size=(100,3))

while True:
    _,img = cap.read()
    height,width,_ = img.shape

    blob = cv2.dnn.blobFromImage(img,1/255,(416,416),(0,0),swapRB=True,crop=False)
    net.setInput(blob)
    output_layers_names = net.getUnconnectedOutLayersNames()
    layerOutputs = net.forward(output_layers_names)

    boxes = []
    confidences = []
    class_ids = []

    for output in layerOutputs:
        for detection in output:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.2:
                center_x = int(detection[0]*width)
                center_y = int(detection[1]*height)
                w = int(detection[2]*width)
                h = int(detection[3]*height)

                x = int(center_x - w/2)
                y = int(center_y - h/2)

                boxes.append([x,y,w,h])
                confidences.append((float(confidence)))
                class_ids.append(class_id)

    indexes = cv2.dnn.NMSBoxes(boxes,confidences,0.2,0.4)

    if len(indexes)>0:
        for i in indexes.flatten():
            x,h = boxes[i]
            label = str(classes[class_ids[i]])
            confidence = str(round(confidences[i],2))
            color = colors[i]
            cv2.rectangle(img,(x,y),(x+w,y+h),color,2)
            cv2.putText(img,label + " " + confidence,y+20),font,2,(255,255),2)

    cv2.imshow('Image',img)
    key = cv2.waitKey(1)
    if key==27:
        break

cap.release()
cv2.destroyAllWindows()