yolov5 github：https://github.com/ultralytics/yolov5
跟蹤：https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch
TensorRT：https://github.com/TrojanXu/yolov5-tensorrt
NCNN：https://github.com/WZTENG/YOLOv5_NCNN

detect:

from torchvision import transforms import torch from PIL import Image,ImageDraw from models import yolo from utils.general import non_max_suppression from models.experimental import attempt_load# model = yolo.Model(r"D:\GoogleEarthProPortable\yolov5-master\models\yolov5s.yaml") # model.load_state_dict(torch.load(r"D:\GoogleEarthProPortable\yolov5-master\weights\yolov5s.pt")) model = attempt_load("weights/yolov5s.pt") # load FP32 model model.eval()img = Image.open("inference/images/bus.jpg")tf = transforms.Compose([transforms.Resize((512,640)),transforms.ToTensor() ])print(img.size) # w,h scale_w = img.size[0] /640 scale_h = img.size[1] /512 im = img.resize((640,512))img_tensor = tf(img)pred = model(img_tensor[None])[0] pred = non_max_suppression(pred,0.3,0.5)imgDraw = ImageDraw.Draw(img) for box in pred[0]:b = box.cpu().detach().long().numpy()print(b)imgDraw.rectangle((b[0]*scale_w,b[1]*scale_h,b[2]*scale_w,b[3]*scale_h))# imgDraw.rectangle((b[0],b[1],b[2],b[3])) img.show()

serving：

import io import jsonfrom torchvision import models import torchvision.transforms as transforms from PIL import Image,ImageDrawfrom utils.general import non_max_suppression from models.experimental import attempt_loadfrom flask import Flask, jsonify, request app = Flask(__name__)model = attempt_load("weights/yolov5s.pt") # load FP32 model model.eval()names= ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light','fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow','elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee','skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard','tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple','sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch','potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone','microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear','hair drier', 'toothbrush']def transform_image(image_bytes):my_transforms = transforms.Compose([transforms.Resize((512,640)),transforms.ToTensor(),])image = Image.open(io.BytesIO(image_bytes))return my_transforms(image)def get_prediction(image_bytes):tensor = transform_image(image_bytes=image_bytes)outputs = model(tensor[None])[0]print(outputs)outputs = non_max_suppression(outputs,0.3,0.5)boxs = outputs[0]print(boxs[0])print(int(boxs[0][-1].item()))class_name = names[int(boxs[0][5].item())]print(boxs.shape)boxes = []for i in range(boxs.shape[0]):boxes.append([boxs[i][0].item(),boxs[i][1].item(),boxs[i][2].item(),boxs[i][3].item(),boxs[i][4].item(),boxs[i][5].item()])return boxes@app.route('/predict', methods=['POST']) def predict():if request.method == 'POST':file = request.files['file']img_bytes = file.read()boxes = get_prediction(image_bytes=img_bytes)return ({'boxes': boxes})if __name__ == '__main__':app.run()

client：

import requests import osfor i in os.listdir("inference/images"):image = open("inference/images/"+i,'rb')payload = {'file':image}r = requests.post(" http://localhost:5000/predict", files=payload).json()print(r)

git bash控制臺：
啟動flask服務器：FLASK_ENV=development FLASK_APP=app.py flask run
測試命令：curl -X POST -F file=@test_img/dog.jpg http://localhost:5000/predict

總結

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