111 lines
4.8 KiB
Python
111 lines
4.8 KiB
Python
# -*- coding: utf-8 -*-
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from flask import request, Flask
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import pdb
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import numpy as np
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import json
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import time
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import cv2, base64
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import argparse
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import sys, os
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import torch
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from PIL import Image
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import datetime
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from torchvision import transforms
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from models.experimental import attempt_load
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from utils.torch_utils import select_device
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# from models.modeling import VisionTransformer, CONFIGS
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from gevent.pywsgi import WSGIServer
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sys.path.insert(0, ".")
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import detect
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import logging.config
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from skywalking import agent, config
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SW_SERVER = os.environ.get('SW_AGENT_COLLECTOR_BACKEND_SERVICES')
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SW_SERVICE_NAME = os.environ.get('SW_AGENT_NAME')
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if SW_SERVER and SW_SERVICE_NAME:
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config.init() #采集服务的地址，给自己的服务起个名称
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#config.init(collector="123.60.56.51:11800", service='ieemoo-ai-search') #采集服务的地址，给自己的服务起个名称
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agent.start()
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def setup_logging(path):
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if os.path.exists(path):
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with open(path, 'r') as f:
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config = json.load(f)
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logging.config.dictConfig(config)
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logger = logging.getLogger("root")
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return logger
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logger = setup_logging('utils/logging.json')
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app = Flask(__name__)
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app.use_reloader=False
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parser = argparse.ArgumentParser()
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#parser.add_argument('--weights', nargs='+', type=str, default='runs/zhanting/yolov5m_finetune/exp4/weights/best.pt', help='model.pt path(s)')
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parser.add_argument('--weights', nargs='+', type=str, default='../module/ieemoo-ai-zhanting/model/now/best.pt', help='model.pt path(s)')
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parser.add_argument('--source', type=str, default='../module/ieemoo-ai-zhanting/imgs/1.jpg', help='source') # file/folder, 0 for webcam
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parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
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parser.add_argument('--conf-thres', type=float, default=0.60, help='object confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
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parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--view-img', type=bool, default=True, help='display results')
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parser.add_argument('--save-txt', type=bool, default=True, help='save results to *.txt')
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parser.add_argument('--save-conf', type=bool, default=True, help='save confidences in --save-txt labels')
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parser.add_argument('--nosave', type=bool, default=True, help='do not save images/videos')
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parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
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parser.add_argument('--agnostic-nms', type=bool, default=True, help='class-agnostic NMS')
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parser.add_argument('--augment', type=bool, default=True, help='augmented inference')
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parser.add_argument('--update', type=bool, default=True, help='update all models')
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parser.add_argument('--project', default='runs/detect', help='save results to project/name')
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parser.add_argument('--name', default='exp', help='save results to project/name')
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parser.add_argument('--exist-ok', type=bool, default=True, help='existing project/name ok, do not increment')
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#opt = parser.parse_args()
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opt, unknown = parser.parse_known_args()
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def init_model(opt):
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device = select_device(opt.device)
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#half = device.type != 'cpu'
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model = attempt_load(opt.weights, map_location=device)
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stride = int(model.stride.max())
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#if half:
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model.half()
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model.eval()
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return model, stride
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model,stride = init_model(opt)
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@app.route("/zhanting", methods=['POST'])
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def get_isempty():
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data = request.get_data()
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ip = request.remote_addr
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print('------ ip = %s ------' % ip)
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json_data = json.loads(data.decode("utf-8"))
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pic = json_data.get("pic")
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result = {"success": "false",
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"rst_cls": '-1',
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}
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try:
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imgdata = base64.b64decode(pic)
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image_path = '../module/ieemoo-ai-zhanting/imgs/1.jpg'
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file = open(image_path, 'wb')
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file.write(imgdata)
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img = cv2.imread(image_path)
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#pdb.set_trace()
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if img.shape[0] == 1280:
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site = np.array([[[0, 1280],[0, 671], [300, 390], [740, 390], [1024, 635], [1024,1280]]], dtype=np.int32)
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else:
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site = np.array([[[0, 1024],[0, 571], [313, 365], [949, 367], [1277, 596], [1280, 1024]]], dtype=np.int32)
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im = np.zeros(img.shape[:2], dtype="uint8")
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cv2.polylines(im, site, 1, 255)
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cv2.fillPoly(im, site, 255)
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mask = im
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masked = cv2.bitwise_or(img, img, mask=mask)
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img0 = masked
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cv2.imwrite("../module/ieemoo-ai-zhanting/imgs/1.jpg",img0)
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pred = detect.detect(opt, model, stride)
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logger.info(pred)
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except Exception as e:
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logger.warning(e)
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return pred
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return pred
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if __name__ == "__main__":
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app.run(host='192.168.1.142', port=8000)
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