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ieemoo-ai-predict.py
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211
ieemoo-ai-predict.py
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import sys
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sys.path.append('/root/data')
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import argparse
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import time
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from pathlib import Path
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from PIL import Image
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import numpy
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import numpy as np
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import os,base64
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import cv2
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import torch
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import logging
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import torch.backends.cudnn as cudnn
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from gevent.pywsgi import WSGIServer
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from numpy import random
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import json
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from models.experimental import attempt_load
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from utils.datasets import LoadStreams, LoadImages
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from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
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scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
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from utils.plots import plot_one_box
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from utils.torch_utils import select_device, load_classifier, time_synchronized
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from flask import request, Flask, jsonify, request
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#from ocr_system.predict_all import *
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import logging
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app = Flask(__name__)
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# 日志系统配置
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handler = logging.FileHandler('app.log', encoding='UTF-8')
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handler.setLevel(logging.DEBUG)
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logging_format = logging.Formatter(
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'%(asctime)s - %(levelname)s - %(filename)s - %(funcName)s - %(lineno)s - %(message)s')
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handler.setFormatter(logging_format)
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app.logger.addHandler(handler)
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'''
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load model
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'''
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', nargs='+', type=str, default='weightsv5s', help='model.pt path(s)')
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parser.add_argument('--Form', type=str, default='')
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parser.add_argument('--source', type=str, default='data/images', 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=0.25, 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='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--view-img', action='store_true', help='display results')
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parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
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parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
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parser.add_argument('--nosave', action='store_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', action='store_true', help='class-agnostic NMS')
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parser.add_argument('--augment', action='store_true', help='augmented inference')
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parser.add_argument('--update', action='store_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', action='store_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 loadModel():
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all_model = {}
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device = select_device(opt.device)
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half = device.type != 'cpu'
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for name in os.listdir(opt.weights):
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all_model[name.split('.')[0]] = attempt_load(os.sep.join(['weightsv5s', name]), map_location=device)
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return all_model
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def CheckRange(code):
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with open('goodsCategory.json', 'r', encoding='utf-8') as jsonPath:
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loadDict = json.load(jsonPath)
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for key in loadDict:
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if code in loadDict[key]:
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return key, loadDict[key]
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#return key, code
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return 'nonentity',None
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def detect(names, save_img=False, model=None):
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source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
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save_img = not opt.nosave and not source.endswith('.txt') # save inference images
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webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
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('rtsp://', 'rtmp://', 'http://'))
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# Initialize
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set_logging()
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device = select_device(opt.device)
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half = device.type != 'cpu' # half precision only supported on CUDA
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# Load model
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#model = attempt_load(weights, map_location=device) # load FP32 model
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stride = int(model.stride.max()) # model stride
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imgsz = check_img_size(imgsz, s=stride) # check img_size
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if half:
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model.half() # to FP16
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# Second-stage classifier
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classify = False
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if classify:
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modelc = load_classifier(name='resnet101', n=2) # initialize
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modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
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# Set Dataloader
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vid_path, vid_writer = None, None
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if webcam:
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view_img = check_imshow()
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cudnn.benchmark = True # set True to speed up constant image size inference
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dataset = LoadStreams(source, img_size=imgsz, stride=stride)
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else:
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dataset = LoadImages(source, img_size=imgsz, stride=stride)
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# Get names and colors
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#names = model.module.names if hasattr(model, 'module') else model.names
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names = names
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colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
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# Run inference
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if device.type != 'cpu':
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model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
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t0 = time.time()
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for path, img, im0s, vid_cap 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_synchronized()
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pred = model(img, augment=opt.augment)[0]
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# Apply NMS
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pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
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t2 = time_synchronized()
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# Apply Classifier
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if classify:
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pred = apply_classifier(pred, modelc, img, im0s)
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# Process detections
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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, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
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else:
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p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
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p = Path(p) # to Path
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s += '%gx%g ' % img.shape[2:] # print string
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
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result = {}
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if len(det) == 0:
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result["TargetDetect"] = 'Search fail'
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result["tag"] = False
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return result
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if len(det):
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# Rescale boxes from img_size to im0 size
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
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# Print results
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for c in det[:, -1].unique():
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n = (det[:, -1] == c).sum() # detections per class
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s+= "{n} {names[int(c)]}{'s' * (n > 1)}, "# add to string
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# return results
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result = {}
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res = []
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for nu , data in enumerate(reversed(det.cpu()).numpy()):
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res.append( {"xy1":(str(data[0]),str(data[1])), "xy2":(str(data[2]),str(data[3])), "precision":str(data[4]), "Class":names[int(data[5])]})
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result["TargetDetect"] = res
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result["tag"] = True
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print(result)
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return result
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all_model = loadModel()
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#barcode, ocr = loadOcrModel()
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@app.route('/predict',methods=['POST'])
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def predict():
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if request.method == 'POST':
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try:
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data = request.get_data()
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data_str = data.decode("utf-8")
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json_data = json.loads(data_str)
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pic = json_data.get("file")
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code = json_data.get("code")
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imgdata = base64.b64decode(pic)
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imgdata_np = np.frombuffer(imgdata, dtype='uint8')
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image = cv2.imdecode(imgdata_np, cv2.IMREAD_COLOR)
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section,names = CheckRange(code)
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#print('section',section,names)
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if section == 'nonentity' or section not in all_model.keys():
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model = None
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section = 'nonentity'
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else:
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model = all_model[section]
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cv2.imwrite(os.sep.join(['data/images',code+'.jpg']), image)
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with torch.no_grad():
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if section == 'nonentity':
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tar_res = {'tag':False}
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tar_res['TargetDetect'] = 'No goods'
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else:
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tar_res = detect(names, model=model)
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#ocr_res = detectOcr(barcode, ocr, 'data/images/'+code+'.jpg')
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ocr_res = {"Ocr": {"barcode_rst": "", "barcode_success": "false", "ocr_rst": "", "ocr_success": False}}
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ocr_res.update(tar_res)
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os.remove(os.sep.join(['data/images',code+'.jpg']))
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return ocr_res
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except:
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return {"Ocr": {"barcode_rst": "", "barcode_success": "false", "ocr_rst": "", "ocr_success": False}, "TargetDetect": {""}, "tag": False}
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if __name__ == '__main__':
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#http_server = WSGIServer(('0.0.0.0', 8084), app)
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#http_server.serve_forever()
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app.run()
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