flask-TensorFlow推理

旧城等待， 2022-12-21 04:48 111阅读 0赞

'''
    Coding: # -*- coding: utf-8 -*-
    Author: Johnson
    Date: 2020-11-14 19:55:50
    Description: 
    Email: 593956670@qq.com
    '''
    import tensorflow as tf 
    import numpy as np 
    
    PATH_TO_CKPT = 'models/ssdlite_mobilenet_v2.pb'
    
    def load_model():
        detection_graph = tf.Graph()
        with detection_graph.as_default():
            od_graph_def = tf.GraphDef()
            with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
                serialized_graph = fid.read()
                od_graph_def.ParseFromString(serialized_graph)
                tf.import_graph_def(od_graph_def, name='')
                with detection_graph.as_default():
                    sess = tf.Session(graph=detection_graph)
                    return sess, detection_graph
    
    
    
    def inference(sess, detection_graph, img_arr, conf_thresh=0.5):
        # with detection_graph.as_default():
        #     with tf.Session(graph=detection_graph) as sess:
                # Definite input and output Tensors for detection_graph
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        # Each box represents a part of the image where a particular object was detected.
        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        # Each score represent how level of confidence for each of the objects.
        # Score is shown on the result image, together with the class label.
        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')
        image_np_expanded = np.expand_dims(img_arr, axis=0)
        (boxes, scores, classes, num) = sess.run(
                        [detection_boxes, detection_scores, detection_classes, num_detections],
                        feed_dict={image_tensor: image_np_expanded})
    
        height, width, _ = img_arr.shape
        results = []
        for idx, class_id in enumerate(classes[0]):
            conf = scores[0, idx]
            if conf > conf_thresh:
                bbox = boxes[0, idx]
                ymin, xmin, ymax, xmax = bbox[0] * height, bbox[1] * width, bbox[2] * height, bbox[3] * width
                
                results.append({"name": id2name[class_id],
                                "conf": str(conf),
                                "bbox": [int(xmin), int(ymin), int(xmax), int(ymax)]
                })
    
        return {"results":results}

'''
    Coding: # -*- coding: utf-8 -*-
    Author: Johnson
    Date: 2020-11-14 19:51:57
    Description: 
    Email: 593956670@qq.com
    '''
    from flask import Flask, request, jsonify
    from PIL import Image
    import numpy as np
    import base64
    import io
    import os
    
    from tf_inference import load_model, inference
    
    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
    
    sess, detection_graph = load_model()
    
    app = Flask(__name__)
    
    @app.route('/api/', methods=["POST"])
    def main_interface():
        response = request.get_json()
        data_str = response['image']
        point = data_str.find(',')
        base64_str = data_str[point:]  # remove unused part like this: "data:image/jpeg;base64,"
    
        image = base64.b64decode(base64_str)       
        img = Image.open(io.BytesIO(image))
    
        if(img.mode!='RGB'):
            img = img.convert("RGB")
        
        # convert to numpy array.
        img_arr = np.array(img)
    
        # do object detection in inference function.
        results = inference(sess, detection_graph, img_arr, conf_thresh=0.5)
        print(results)
    
        return jsonify(results)
    
    @app.after_request
    def add_headers(response):
        response.headers.add('Access-Control-Allow-Origin', '*')
        response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')
        return response
    
    
    if __name__ == '__main__':
        app.run(debug=True, host='0.0.0.0')