5分钟让你的电脑运行yolo图像识别

首先我们需要安装python3.8以上，具体安装方法就不多说了

我这里已经安装好了

第二步，安装依赖包

打开CMD，然后CD到yolo文件夹的位置上，然后pip install -r requirements.txt。等待他自己下载安装依赖包。

这里我已经全部下载好了。

第三步，打开IDLE(第一步已经安装), CTRL+O，然后打开yolo文件夹中的Untitled-1.py文件。

import cv2
import numpy as np
 
# Load Yolo
print("LOADING YOLO")
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
#save all the names in file o the list classes
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]
#get layers of the network
layer_names = net.getLayerNames()
#Determine the output layer names from the YOLO model 
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))
 
print("YOLO LOADED")
 
 
img = cv2.imread("room.png")
img = cv2.resize(img,None,fx = 0.4,fy = 0.4)
height, width, channel = img.shape
print(width,height,channel)
 
# Detect Objects
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
# print(outs)
 
print(width,height,channel)
 
 
 
####################################
 
# Showing Information on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5:
            # Object detection
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)
            # cv.circle(img, (center_x, center_y), 10, (0, 255, 0), 2 )
            # Reactangle Cordinate
            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)
 
# print(len(boxes))
# number_object_detection = len(boxes)
 
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
print(indexes)
 
font = cv2.FONT_HERSHEY_PLAIN
for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        label = str(classes[class_ids[i]])
        # print(label)
        color = colors[i]
        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
        cv2.putText(img, label, (x, y + 30), font, 3, color, 3)
 
cv2.imshow("IMG", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

第四步，按下F5运行程序，就能看到room.png这张照片中的物体被识别出来。

 当然，如果你会开启USB摄像头，就可以获得实时显示识别的效果。

文件地址：

链接：https://pan.baidu.com/s/1JT1K7q2-PR9pAYNBV-3DtQ?pwd=qnlo 
提取码：qnlo