def __init__(self, json_path, weight_path, width=260, height=260):
"""
图像大小是260*260,其他尺寸估计需要重新训练模型
:param json_path: 结构文件路径
:param weight_path: 权重文件路径
:param width: 260
:param height: 260
"""
self.image_width = width
self.image_height = height
self.model = load_keras_model(json_path, weight_path)
# anchor configuration
feature_map_sizes = [[33, 33], [17, 17], [9, 9], [5, 5], [3, 3]]
anchor_sizes = [[0.04, 0.056], [0.08, 0.11], [0.16, 0.22], [0.32, 0.45], [0.64, 0.72]]
anchor_ratios = [[1, 0.62, 0.42]] * 5
# generate anchors
self.anchors = generate_anchors(feature_map_sizes, anchor_sizes, anchor_ratios)
# for inference , the batch size is 1, the model output shape is [1, N, 4],
# so we expand dim for anchors to [1, anchor_num, 4]
self.anchors_exp = np.expand_dims(self.anchors, axis=0)
self.id2class = {0: 'Mask', 1: 'NoMask'}
# -*- coding:utf-8 -*-
import cv2
import time
import argparse
import os
import numpy as np
from PIL import Image
from keras.models import model_from_json
from utils.anchor_generator import generate_anchors
from utils.anchor_decode import decode_bbox
from utils.nms import single_class_non_max_suppression
from load_model.keras_loader import load_keras_model, keras_inference
model = load_keras_model('models/face_mask_detection.json', 'models/face_mask_detection.hdf5')
# anchor configuration
feature_map_sizes = [[33, 33], [17, 17], [9, 9], [5, 5], [3, 3]]
anchor_sizes = [[0.04, 0.056], [0.08, 0.11], [0.16, 0.22], [0.32, 0.45], [0.64, 0.72]]
anchor_ratios = [[1, 0.62, 0.42]] * 5
# generate anchors
anchors = generate_anchors(feature_map_sizes, anchor_sizes, anchor_ratios)
# for inference , the batch size is 1, the model output shape is [1, N, 4],
# so we expand dim for anchors to [1, anchor_num, 4]
anchors_exp = np.expand_dims(anchors, axis=0)
id2class = {0: 'Mask', 1: 'NoMask'}
import numpy as np
from PIL import Image
from keras.models import model_from_json
from utils.anchor_generator import generate_anchors
from utils.anchor_decode import decode_bbox
from utils.nms import single_class_non_max_suppression
from load_model.keras_loader import load_keras_model, keras_inference
from tornado.wsgi import WSGIContainer
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
app = Flask(__name__)
model = load_keras_model(
'/home/smasoft/AI_AICare/FaceMaskDetection/models/face_mask_detection.json',
'/home/smasoft/AI_AICare/FaceMaskDetection/models/face_mask_detection.hdf5'
)
# anchor configuration
feature_map_sizes = [[33, 33], [17, 17], [9, 9], [5, 5], [3, 3]]
anchor_sizes = [[0.04, 0.056], [0.08, 0.11], [0.16, 0.22], [0.32, 0.45],
[0.64, 0.72]]
anchor_ratios = [[1, 0.62, 0.42]] * 5
# generate anchors
anchors = generate_anchors(feature_map_sizes, anchor_sizes, anchor_ratios)
# for inference , the batch size is 1, the model output shape is [1, N, 4],
# so we expand dim for anchors to [1, anchor_num, 4]
anchors_exp = np.expand_dims(anchors, axis=0)