AI图像识别初次尝试

1.人形识别结果

2.代码

python+OpenCV+yolov3训练库，代码如下：

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import cv2
import numpy as np
import osimgFiles=["pic03.jpg", "pic04.jpg"]net=None
classes=None
colors=Nonedef recog_person(imgFile, ratio):print(imgFile)global net, classes, colorsif(net==None):# 加载模型配置和权重net = cv2.dnn.readNet("../common/yolov3.weights", "../common/yolov3.cfg")# 加载对象类别classes = []with open("coco.names", "r") as f:classes = [line.strip() for line in f.readlines()]# 随机颜色colors = np.random.uniform(0, 255, size=(len(classes), 3))# 加载图像image = cv2.imread(imgFile)# 获取图像尺寸height, width, _ = image.shapenew_height = 416new_width = int(width * (new_height / height))resized_image = cv2.resize(image, (new_width, new_height))# 构建输入blobblob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False)# 设置输入blob作为网络的输入net.setInput(blob)# 前向传递，获取输出层layer_names = net.getLayerNames()print("layer names counts=%d" %(len(layer_names)))print(net.getUnconnectedOutLayers())#print(length(layer_names))#output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]outs = net.forward(output_layers)# 初始化边界框、置信度和类别列表boxes = []confidences = []class_ids = []# 对每个输出层进行处理for out in outs:for detection in out:# 获取类别置信度scores = detection[5:]class_id = np.argmax(scores)confidence = scores[class_id]# 过滤掉低置信度的预测if confidence > ratio:# 获取边界框坐标center_x = int(detection[0] * width)center_y = int(detection[1] * height)w = int(detection[2] * width)h = int(detection[3] * height)# 边界框的左上角坐标x = int(center_x - w / 2)y = int(center_y - h / 2)# 更新边界框、置信度和类别列表boxes.append([x, y, w, h])confidences.append(float(confidence))class_ids.append(class_id)# 非最大值抑制indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)print("indices", len(indices), indices)print("-------------------------------")print("class_ids", len(class_ids), class_ids)print("classes", len(classes));print("colors", len(colors));print("boxes counts=", len(boxes), len(indices));# 绘制边界框和类别标签font = cv2.FONT_HERSHEY_SIMPLEXfor i in range(len(boxes)):if i in indices:x, y, w, h = boxes[i]#label = classes[class_ids[i]]#color = colors[class_ids[i]]color = (128,0,128)cv2.rectangle(image, (x, y), (x+w, y+h), color, 2)#cv2.putText(image, label, (x, y-10), font, 0.5, color, 2)# 显示结果图像name, extension = os.path.splitext(imgFile)output_filename = name + ".ai.jpg";cv2.imwrite(output_filename, image)for file in imgFiles:recog_person(file,0.5)