如何使用 GPU 运行经过训练的 Yolov3 模型

问题描述

我是 DL/实时对象检测领域的新手，我想从 youtube 上学习一些东西。我在 youtube 上观看了关于在 yolov3 上实时自定义对象检测的视频 https://www.youtube.com/watch?v=DLngCtsG3bk 并且我正确地完成了所有步骤。当我在笔记本电脑上运行对象检测 python 文件时，它在 CPU 上运行，我只能接收 2-3 fps。请谁能告诉我如何使用我的 GPU 来运行它。我得到的文件 yolov3_training_last.weights、yolov3_testing.cfg 和 classes.txt。 python 文件包含在下面，当我运行它时，我说我得到了低 fps。如果有什么方法可以用GPU来增加它，请教我。提前致谢。

Python 文件：

import cv2
import numpy as np
import time

net = cv2.dnn.readNet('yolov3_training_last.weights','yolov3_testing.cfg')

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

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

# used to record the time when we processed last frame
prev_frame_time = 0
 
# used to record the time at which we processed current frame
new_frame_time = 0

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 > 0.95:
                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]
            crop_img = img[y:y+h,x:x+w]
            (B,G,R) = [int(x) for x in cv2.mean(crop_img)[:3]]
            roi_face= img[y: y+ h,x:x+w]
            roi_face = cv2.blur(roi_face,(20,20))
            img[y: y+ h,x: x+ w]=[0,0]
            img[y: y+ h,x: x+ w]=cv2.add(roi_face,img[y: y+ h,x: x+ w])
            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,1,(255,255),2)

    
    font = cv2.FONT_HERSHEY_SIMPLEX
    # time when we finish processing for this frame
    new_frame_time = time.time()
 
    # Calculating the fps
 
    # fps will be number of frame processed in given time frame
    # since their will be most of time error of 0.001 second
    # we will be subtracting it to get more accurate result
    fps = 1/(new_frame_time-prev_frame_time)
    prev_frame_time = new_frame_time
 
    # converting the fps into integer
    fps = int(fps)
 
    # converting the fps to string so that we can display it on frame
    # by using putText function
    fps = str(fps)
 
    # putting the FPS count on the frame
    cv2.putText(img,fps,(7,70),3,(100,cv2.LINE_AA)

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

cap.release()
cv2.destroyAllWindows()

解决方法

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