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- from importlib import import_module
- import os
- from flask import Flask, render_template, Response, request, send_file
- import cv2
- import subprocess
- import time
- import sys
- import logging
- import pdb
- sys.path.append('./') # to run '$ python *.py' files in subdirectories
- logger = logging.getLogger(__name__)
- import torch
- from models.common import *
- from models.experimental import *
- from models.yolo import *
- from utils.autoanchor import check_anchor_order
- from utils.general import make_divisible, check_file, set_logging, check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
- scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
- from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
- select_device, copy_attr
- from utils.loss import SigmoidBin
- # import camera driver
- # from object_detection import VideoStreaming
- # if os.environ.get('CAMERA'):
- # Camera = import_module('camera_' + os.environ['CAMERA']).Camera
- # else:
- # from camera import Camera
- app = Flask(__name__)
- # def gen(camera):
- # while True:
- # frame = VideoStreaming.get_frame()
- # # cv2.imencode('.jpg', frame)
- # yield (b'--frame\r\n'
- # b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
- PATH_WEIGHT = './models/best.pt'
- img_mean = np.array([104., 117., 123.])[:, np.newaxis, np.newaxis].astype('float32')
- class Net:
- def __init__(self, device='cuda'):
- tstamp = time.time()
- self.device = select_device(device)
- print('[yolo] loading with', self.device)
- # self.net = Model('/root/helmet_det/yolov7-main/cfg/training/yolov7_custom.yaml').to(self.device)
- # state_dict = torch.load(PATH_WEIGHT, map_location=self.device)['model'].state_dict()
- self.net = attempt_load(PATH_WEIGHT).cuda()
- # self.net.load_state_dict(state_dict)
- self.net.eval()
- print('[yolo] finished loading (%.4f sec)' % (time.time() - tstamp))
- def detect_faces(self, image, conf_th=0.8, scales=[1]):
-
- w, h = image.shape[1], image.shape[0]
- # print(w,h)
- # pdb.set_trace()
- bboxes = np.empty(shape=(0, 5))
- with torch.no_grad():
- for s in scales:
- # print(image, image.shape)
- scaled_img = cv2.resize(image, dsize=(0, 0), fx=s, fy=s, interpolation=cv2.INTER_LINEAR)
- scaled_img = np.swapaxes(scaled_img, 1, 2)
- scaled_img = np.swapaxes(scaled_img, 1, 0)
- # scaled_img = scaled_img[[2, 1, 0], :, :]
- scaled_img = scaled_img.astype('float32')
- # scaled_img -= img_mean
- # scaled_img = scaled_img[[2, 1, 0], :, :]
- x = torch.FloatTensor(scaled_img).unsqueeze(0).to(self.device)
- #x = x.permute(0,3,1,2) # (B, W, H, C) --> (B, C, W, H)
- # x = torch.from_numpy(scaled_img).to(self.device)
- # pdb.set_trace()
- y = self.net(x)[0]
- y = non_max_suppression(y)
-
- # Process detections
- for i, det in enumerate(y): # detections per image
-
- # gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
- if len(det):
- # Rescale boxes from img_size to im0 size
- det[:, :4] = scale_coords(x.shape[2:], det[:, :4], x.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' * (n > 1)}, " # add to string
- # # Write results
- # for *xyxy, conf, cls in reversed(det):
- # if save_txt: # Write to file
- # xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
- # line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
- # with open(txt_path + '.txt', 'a') as f:
- # f.write(('%g ' * len(line)).rstrip() % line + '\n')
- # if save_img or view_img: # Add bbox to image
- # label = f'{names[int(cls)]} {conf:.2f}'
- # plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
-
- # Print time (inference + NMS)
- # print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
- # 교수님 코드
- # detections = y.data
- detections = y
- scale = torch.Tensor([w, h, w, h])
-
- # for i in range(detections.size(1)):
- # j = 0
- # while detections[0, i, j, 0] > conf_th:
- # score = detections[0, i, j, 0]
- # pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
- # bbox = (pt[0], pt[1], pt[2], pt[3], score)
- # bboxes = np.vstack((bboxes, bbox))
- # j += 1
- # keep = nms_(bboxes, 0.1) ## nms?
- # bboxes = bboxes[keep]
- return bboxes
- # return y
- def get_stream_video():
- # camera 정의
-
- cam = cv2.VideoCapture('rtsp://astrodom:hdci12@192.168.170.73:554/stream1')
- model = Net()
- # print(model)
- # pdb.set_trace()
- while True:
- # 카메라 값 불러오기
- success, frame = cam.read()
- # print(frame)
- # print(type(frame))
- if not success:
- break
- else:
- # frame을 byte로 변경 후 특정 식??으로 변환 후에
- # yield로 하나씩 넘겨준다.
- # ret, buffer = cv2.imencode('.jpeg', frame)
- # decode_img = cv2.imdecode(buffer, 1)
- # frame = cv2.imencode(model.detect_faces(buffer)).tobytes()
- image_np = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
- image_np = cv2.resize(image_np, (640, 384))
- # pdb.set_trace()
- frame = model.detect_faces(image_np).tobytes()
-
- # frame = buffer.tobytes()
- # print(type(frame))
- # pdb.set_trace()
- yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + bytearray(frame) + b'\r\n')
- @app.route('/')
- def index():
- return render_template('index.html')
- # 스트리밍 경로를 /video 경로로 설정.
- @app.route("/video", methods=['GET'])
- def video():
- # StringResponse함수를 return하고,
- # 인자로 OpenCV에서 가져온 "바이트"이미지와 type을 명시
- return Response(get_stream_video(), mimetype="multipart/x-mixed-replace; boundary=frame")
- # ipcam 열기
- @app.route("/stream", methods=['GET'])
- def stream():
- print("here")
- result = subprocess.run(['python3', '/root/helmet_det/yolov7-main/detect.py', '--source', 'rtsp://astrodom:hdci12@192.168.170.73:554/stream1', '--weights', '/root/helmet_det/yolov7-main/models/best.pt'])
- print(result)
- return result
- # rtsp://astrodom:hdci12@192.168.170.73:554/stream1
- if __name__ == '__main__':
- app.run(host='0.0.0.0', port=5000, debug=True)
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