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- #!/usr/bin/env python
- # -*- coding: utf-8 -*-
- # @Author : linjie
- # detection: 基于yolov5+flask 网页端实现
- import os
- import cv2
- from base_camera import BaseCamera
- from models.experimental import attempt_load
- import torch
- import torch.nn as nn
- import torchvision
- import numpy as np
- import argparse
- from utils.datasets import *
- from utils.utils import *
-
-
- class Camera(BaseCamera):
- video_source = 'people.mp4'
-
- def __init__(self):
- if os.environ.get('OPENCV_CAMERA_SOURCE'):
- Camera.set_video_source(int(os.environ['OPENCV_CAMERA_SOURCE']))
- super(Camera, self).__init__()
-
- @staticmethod
- def set_video_source(source):
- Camera.video_source = source
-
- @staticmethod
- def frames():
- out, weights, imgsz = \
- 'inference/output', 'weights/yolov5s.pt', 640
- source = 'people.mp4'
- device = torch_utils.select_device()
- if os.path.exists(out):
- shutil.rmtree(out) # delete output folder
- os.makedirs(out) # make new output folder
-
- # Load model
- model = attempt_load(weights, map_location=device) # load FP32 model
- model.to(device).eval()
-
- # Second-stage classifier
- classify = False
- if classify:
- modelc = torch_utils.load_classifier(name='resnet101', n=2) # initialize
- modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights
- modelc.to(device).eval()
-
- # Half precision
- half = False and device.type != 'cpu'
- print('half = ' + str(half))
-
- if half:
- model.half()
-
- # Set Dataloader
- vid_path, vid_writer = None, None
- dataset = LoadImages(source, img_size=imgsz)
- # dataset = LoadStreams(source, img_size=imgsz)
- names = model.names if hasattr(model, 'names') else model.modules.names
- colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
-
- # Run inference
- t0 = time.time()
- img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
- _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
- for path, img, im0s, vid_cap in dataset:
- img = torch.from_numpy(img).to(device)
- img = img.half() if half else img.float() # uint8 to fp16/32
- img /= 255.0 # 0 - 255 to 0.0 - 1.0
- if img.ndimension() == 3:
- img = img.unsqueeze(0)
-
- # Inference
- t1 = torch_utils.time_synchronized()
- pred = model(img, augment=False)[0]
-
- # Apply NMS
- pred = non_max_suppression(pred, 0.4, 0.5,
- fast=True, classes=None, agnostic=False)
- t2 = torch_utils.time_synchronized()
-
- # Apply Classifier
- if classify:
- pred = apply_classifier(pred, modelc, img, im0s)
-
-
-
- for i, det in enumerate(pred): # detections per image
- p, s, im0 = path, '', im0s
-
- save_path = str(Path(out) / Path(p).name)
- s += '%gx%g ' % img.shape[2:] # print string
- gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
- if det is not None and len(det):
- # Rescale boxes from img_size to im0 size
- det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
-
- # for c in det[:, -1].unique(): #probably error with torch 1.5
- for c in det[:, -1].detach().unique():
- n = (det[:, -1] == c).sum() # detections per class
- s += '%g %s, ' % (n, names[int(c)]) # add to string
-
- for *xyxy, conf, cls in det:
- label = '%s %.2f' % (names[int(cls)], conf)
- plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
- print('%sDone. (%.3fs)' % (s, t2 - t1))
-
- yield cv2.imencode('.jpg', im0)[1].tobytes()
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