yolov5 检测图片里面的对象

yolov5 检测图片里面的对象

这是比较早的代码，偶尔碰见了打开看看，运行结果如下：

import argparse
import time
from pathlib import Path

import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random

import numpy as np

import requests
from models.experimental import attempt_load

from utils.datasets import LoadStreams,LoadStreams2, LoadImages,LoadWebcam,letterbox

from utils.general import check_img_size, check_requirements, non_max_suppression, apply_classifier, scale_coords, \
    xyxy2xywh, strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized


device = select_device('')
augment = False
conf_thres=0.15
iou_thres=0.25
model = attempt_load('yolov5s.pt', map_location=device)
img_size = 640

names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]


def detectionObjectFunction():
    #vc = cv2.VideoCapture(2)
    #rval, frame = vc.read()
    #rval, cameraImg = vc.read()
    img_file = requests.get("http://182.61.200.7/pic/20200621_76_100737/20200621161706340.jpg")
    cameraImg = cv2.imdecode(np.fromstring(img_file.content, np.uint8), 1)
    
    
    img = letterbox(cameraImg, new_shape=img_size)[0]
    # Convert
    img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
    img = np.ascontiguousarray(img)

    ####################################################
    img = torch.from_numpy(img).to(device)
    #img = img.half() if half else img.float()  # uint8 to fp16/32
    im0 = cameraImg.copy()
    
    img = img.half()
    img = img.float()
    img /= 255.0  # 0 - 255 to 0.0 - 1.0
    if img.ndimension() == 3:
        img = img.unsqueeze(0)

    # Inference
    t1 = time_synchronized()
    pred = model(img, augment=augment)[0]
    #pred = model(img, augment=opt.augment)[0]

    #print('thres:%d '%conf_thres)
    # Apply NMS
    pred = non_max_suppression(pred, conf_thres, iou_thres)
    #def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
    t2 = time_synchronized()
    
    # Apply Classifier
    
    # Process detections
    for i, det in enumerate(pred):  # detections per image
        # batch_size >= 1
        #if webcam:  
        #    p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
        #else:
        #    p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
        #    
        #p = Path(p)  # to Path
        #save_path = str(save_dir / p.name)  # img.jpg
        #txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
        #s += '%gx%g ' % img.shape[2:]  # print string

        # normalization gain whwh
        #gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  
        if len(det):
            # Rescale boxes from img_size to im0 size
            det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
            
            # Print results
            for c in det[:, -1].unique():
                n = (det[:, -1] == c).sum()  # detections per class
                #s += f'{n} {names[int(c)]}s, '  # add to string
    
            # Write results
            for *xyxy, conf, cls in reversed(det):
                
                
                label = f'{names[int(cls)]} {conf:.2f}'
                #plot_one_box2(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=2)
                #plot_one_box2(xyxy, im0, label=label, color=(0,255,0), line_thickness=2)
                #plot_one_box(xyxy, im0, label=label, color=(0,255,0), line_thickness=2)
                plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=2)
            
        # Print time (inference + NMS)
        print(f'detection time. ({t2 - t1:.3f}s)')

        # Stream results
        #if view_img:
        cv2.imshow("win1", im0)
        cv2.imwrite("2021050412.jpg",im0)
        #img2 = im0.copy()
            


        ####################################################
        #pass



detectionObjectFunction()

 

 

 

 

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