129 lines
4.8 KiB
Python
Executable File
129 lines
4.8 KiB
Python
Executable File
# -*- coding: utf-8 -*-
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from flask import request, Flask
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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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from torchvision import transforms
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# import logging.config as log_config
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sys.path.insert(0, ".")
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#Flask对外服务接口
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app = Flask(__name__)
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#app.use_reloader=False
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print("Autor:ieemoo_lc&ieemoo_lx")
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print(torch.__version__)
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--img_size", default=600, type=int, help="Resolution size")
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parser.add_argument('--split', type=str, default='overlap', help="Split method")
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parser.add_argument('--slide_step', type=int, default=2, help="Slide step for overlap split")
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parser.add_argument('--smoothing_value', type=float, default=0.0, help="Label smoothing value")
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#使用自定义VIT
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parser.add_argument("--pretrained_model", type=str, default="../module/ieemoo-ai-isempty/model/now/ieemooempty_vit_checkpoint.pth", help="load pretrained model")
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opt, unknown = parser.parse_known_args()
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return opt
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class Predictor(object):
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def __init__(self, args):
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self.args = args
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#self.args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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#self.args.device = torch.device("cpu")
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#print(self.args.device)
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#self.args.nprocs = torch.cuda.device_count()
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self.cls_dict = {}
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self.num_classes = 0
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self.model = None
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self.prepare_model()
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self.test_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
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def prepare_model(self):
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# config = CONFIGS["ViT-B_16"]
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# config.split = self.args.split
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# config.slide_step = self.args.slide_step
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# self.num_classes = 5
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# self.cls_dict = {0: "noemp", 1: "yesemp", 2: "hard", 3: "fly", 4: "stack"}
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# self.model = VisionTransformer(config, self.args.img_size, zero_head=True, num_classes=self.num_classes, smoothing_value=self.args.smoothing_value)
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# if self.args.pretrained_model is not None:
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# if not torch.cuda.is_available():
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# self.model = torch.load(self.args.pretrained_model)
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# else:
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# self.model = torch.load(self.args.pretrained_model,map_location='cpu')
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self.model = torch.load(self.args.pretrained_model,map_location=torch.device('cpu'))
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self.model.eval()
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if torch.cuda.is_available():
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self.model.to("cuda")
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def normal_predict(self, img_data, result):
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# img = Image.open(img_path)
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if img_data is None:
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#print('error, img data is None')
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print('error, img data is None')
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return result
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else:
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with torch.no_grad():
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x = self.test_transform(img_data)
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if torch.cuda.is_available():
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x = x.cuda()
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part_logits = self.model(x.unsqueeze(0))
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probs = torch.nn.Softmax(dim=-1)(part_logits)
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topN = torch.argsort(probs, dim=-1, descending=True).tolist()
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clas_ids = topN[0][0]
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clas_ids = 0 if 0==int(clas_ids) or 2 == int(clas_ids) or 3 == int(clas_ids) else 1
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print("cur_img result: class id: %d, score: %0.3f" % (clas_ids, probs[0, clas_ids].item()))
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result["success"] = "true"
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result["rst_cls"] = str(clas_ids)
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return result
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args = parse_args()
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predictor = Predictor(args)
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@app.route("/isempty", methods=['POST'])
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def get_isempty():
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#print("begin")
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data = request.get_data()
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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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imgdata = base64.b64decode(pic)
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result ={}
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imgdata_np = np.frombuffer(imgdata, dtype='uint8')
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img_src = cv2.imdecode(imgdata_np, cv2.IMREAD_COLOR)
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img_data = Image.fromarray(np.uint8(img_src))
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result = predictor.normal_predict(img_data, result) # 1==empty, 0==nonEmpty
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return repr(result)
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def getByte(path):
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with open(path, 'rb') as f:
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img_byte = base64.b64encode(f.read())
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img_str = img_byte.decode('utf-8')
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return img_str
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if __name__ == "__main__":
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app.run(host='0.0.0.0', port=8888)
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# result ={}
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# imgdata = base64.b64decode(getByte("img.jpg"))
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# imgdata_np = np.frombuffer(imgdata, dtype='uint8')
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# img_src = cv2.imdecode(imgdata_np, cv2.IMREAD_COLOR)
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# img_data = Image.fromarray(np.uint8(img_src))
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# result = predictor.normal_predict(img_data, result)
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# print(result)
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