218 lines
11 KiB
Python
218 lines
11 KiB
Python
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import os
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import cv2
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from base_camera import BaseCamera
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import torch
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from pathlib import Path
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import sys
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from models.common import DetectMultiBackend
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from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
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from utils.general import (check_img_size, increment_path, non_max_suppression, scale_coords)
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from utils.plots import Annotator, colors
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from utils.torch_utils import select_device, time_sync
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import requests
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[0] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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class Camera(BaseCamera):
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video_source = None
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# 违规动作集
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actions = []
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def __init__(self, source_name="", img_flag=True):
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if os.environ.get('OPENCV_CAMERA_SOURCE'):
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Camera.set_video_source(int(os.environ['OPENCV_CAMERA_SOURCE']))
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Camera.video_source = source_name
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self.img_flag = img_flag
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super(Camera, self).__init__()
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@staticmethod
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def set_video_source(source):
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Camera.video_source = source
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@staticmethod
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def frames():
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out, weights, imgsz = \
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ROOT / 'output', ROOT / 'weights/best.pt', 640
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device = select_device(0) # 0 or 0,1,2,3 or cpu 没有gpu的记得改为cpu
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# device = select_device("cpu")
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# Directories
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save_dir = increment_path(Path(out) / 'exp', exist_ok=False) # increment run
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(save_dir).mkdir(parents=True, exist_ok=True) # make dir
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model = DetectMultiBackend(weights, device=device, dnn=False, data=ROOT / 'data/safe_det.yaml')
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stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
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imgsz = check_img_size(imgsz, s=stride) # check image size
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# Half
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half = False
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half &= (pt or jit or onnx or engine) and device.type != 'cpu' # FP16 supported on limited backends with CUDA
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if pt or jit:
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model.model.half() if half else model.model.float()
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# Dataloader
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print(Camera.video_source)
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is_file = Path(Camera.video_source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
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is_url = Camera.video_source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
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webcam = Camera.video_source.isnumeric() or Camera.video_source.endswith('.txt') or (is_url and not is_file)
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if webcam:
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dataset = LoadStreams(Camera.video_source, img_size=imgsz)
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else:
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dataset = LoadImages(Camera.video_source, img_size=imgsz)
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vid_path, vid_writer = [None], [None]
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# save_result
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save_img = True
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# Run inference
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t0 = time_sync()
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img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
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_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
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for path, img, im0s, vid_cap, s in dataset:
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float() # uint8 to fp16/32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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if img.ndimension() == 3:
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img = img.unsqueeze(0)
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# Inference
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t1 = time_sync()
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pred = model(img, augment=False, visualize=False)
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# nms
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pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False,
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max_det=1000)
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t2 = time_sync()
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for i, det in enumerate(pred): # detections per image
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if webcam: # batch_size >= 1
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p, im0, frame = path[i], im0s[i].copy(), dataset.count
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s += f'{i}: '
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else:
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# p, s, im0 = path, '', im0s
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p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
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p = Path(p) # to Path
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save_path = str(save_dir / p.name)
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s += '%gx%g ' % img.shape[2:]
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]
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annotator = Annotator(im0, line_width=3, example=str(names))
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# 请求后端接口
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for *xyxy, conf, cls in reversed(det):
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# url = 'http://127.0.0.1:7861/chat/knowledge_base_chat'
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url = 'http://192.168.0.21:17861/chat/knowledge_base_chat'
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headers = {
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'Content-Type': 'application/json'
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}
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# 未戴安全帽,只有当置信度大于0.5时才处理
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if int(cls) == 2 and conf > 0.5:
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Camera.actions.append(2)
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# data = {
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# "query": "作业现场人员未佩戴安全帽是几级违规行为？具体违反了哪些条款？条款内容是什么？",
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# "knowledge_base_name": "violation",
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# "top_k": 2,
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# "score_threshold": 2.0,
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# "history": [],
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# "stream": False,
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# "model_name": "chatglm3-6b",
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# "temperature": 0.01,
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# "max_tokens": 1024,
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# "prompt_name": "default"
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# }
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# time_start = time.time()
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# response = requests.post(url, headers=headers, json=data, stream=True)
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# response = requests.post(url, headers=headers, json=data)
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# time_end = time.time()
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# time_c = time_end - time_start # 运行所花时间
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# print('time cost', time_c, 's')
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# json_encoded=response.answer
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# print('================', response.answer)
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# print('---------------API called, response:', response.text)
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# for chunk in response.iter_content(chunk_size=1024):
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# # 处理响应内容
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# print(chunk.decode("utf-8", 'ignore'), type(chunk))
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# 未戴安全带
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elif int(cls) == 4 and conf > 0.5:
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Camera.actions.append(4)
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# data = {
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# "query": "高处作业未佩戴安全带是几级违规行为？具体违反了哪些条款？条款内容是什么？",
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# "knowledge_base_name": "violation",
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# "top_k": 2,
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# "score_threshold": 2.0,
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# "history": [],
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# "stream": False,
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# "model_name": "chatglm3-6b",
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# "temperature": 0.01,
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# "max_tokens": 1024,
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# "prompt_name": "default"
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# }
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# response = requests.post(url, headers=headers, json=data, stream=True)
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# response = requests.post(url, headers=headers, json=data)
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# print('---------------API called, response:', response.text)
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# for chunk in response.iter_content(chunk_size=1024):
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# # 处理响应内容
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# print(chunk.decode("utf-8", 'ignore'))
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# 跨越围栏
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elif int(cls) == 5 and conf > 0.5:
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Camera.actions.append(5)
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# data = {
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# "query": "作业现场人员跨越围栏是几级违规行为？具体违反了哪些条款？条款内容是什么？",
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# "knowledge_base_name": "violation",
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# "top_k": 2,
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# "score_threshold": 2.0,
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# "history": [],
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# "stream": False,
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# "model_name": "chatglm3-6b",
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# "temperature": 0.01,
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# "max_tokens": 1024,
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# "prompt_name": "default"
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# }
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# response = requests.post(url, headers=headers, json=data, stream=True)
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# response = requests.post(url, headers=headers, json=data)
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# print('---------------API called, response:', response.text)
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# for chunk in response.iter_content(chunk_size=1024):
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# # 处理响应内容
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# print(chunk.decode("utf-8", 'ignore'))
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if det is not None and len(det):
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
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for c in det[:, -1].detach().unique():
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n = (det[:, -1] == c).sum()
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s += '%g %s, ' % (n, names[int(c)])
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for *xyxy, conf, cls in det:
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label = '%s %.2f' % (names[int(cls)], conf)
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c = int(cls)
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annotator.box_label(xyxy, label, color=colors(c, True))
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print('%sDone. (%.3fs)' % (s, t2 - t1))
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# Save results (image with detections)
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if save_img:
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if dataset.mode == 'image':
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cv2.imwrite(save_path, im0)
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else: # 'video' or 'stream'
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if vid_path[i] != save_path: # new video
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vid_path[i] = save_path
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if isinstance(vid_writer[i], cv2.VideoWriter):
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vid_writer[i].release() # release previous video writer
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if vid_cap: # video
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else: # stream
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
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vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
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vid_writer[i].write(im0)
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# yield cv2.imencode('.jpg', im0)[1].tobytes()
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# frame_encoded = cv2.imencode('.jpg', im0)[1].tobytes()
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# json_encoded=
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yield cv2.imencode('.jpg', im0)[1].tobytes()
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