| from flask import Flask, render_template, request, jsonify
|
| import os
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| import cv2
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| import numpy as np
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| import tensorflow as tf
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| from object_detection.utils import label_map_util
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| from object_detection.utils import visualization_utils as viz_utils
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| from object_detection.builders import model_builder
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| from object_detection.utils import config_util
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|
|
| app = Flask(__name__)
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|
|
|
|
| CUSTOM_MODEL_NAME = 'my_ssd_mobnet'
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| paths = {
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| 'CHECKPOINT_PATH': os.path.join('Tensorflow', 'workspace', 'models', CUSTOM_MODEL_NAME),
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| 'LABELMAP': os.path.join('Tensorflow', 'workspace', 'annotations', 'label_map.pbtxt')
|
| }
|
|
|
| configs = config_util.get_configs_from_pipeline_file(os.path.join(paths['CHECKPOINT_PATH'], 'pipeline.config'))
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| detection_model = model_builder.build(model_config=configs['model'], is_training=False)
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| ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
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| ckpt.restore(os.path.join(paths['CHECKPOINT_PATH'], 'ckpt-7')).expect_partial()
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| category_index = label_map_util.create_category_index_from_labelmap(paths['LABELMAP'])
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|
|
|
|
| @tf.function
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| def detect_fn(image):
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| image, shapes = detection_model.preprocess(image)
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| prediction_dict = detection_model.predict(image, shapes)
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| detections = detection_model.postprocess(prediction_dict, shapes)
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| return detections
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|
|
|
|
| @app.route('/detect', methods=['POST'])
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| def detect():
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|
|
| file = request.files['image']
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|
|
|
|
| img = cv2.imdecode(np.frombuffer(file.read(), np.uint8), cv2.IMREAD_COLOR)
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| image_np = np.array(img)
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|
|
|
|
| input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
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| detections = detect_fn(input_tensor)
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|
|
| num_detections = int(detections.pop('num_detections'))
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| detections = {key: value[0, :num_detections].numpy() for key, value in detections.items()}
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| detections['num_detections'] = num_detections
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| detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
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|
|
| label_id_offset = 1
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| image_np_with_detections = image_np.copy()
|
|
|
| viz_utils.visualize_boxes_and_labels_on_image_array(
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| image_np_with_detections,
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| detections['detection_boxes'],
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| detections['detection_classes'] + label_id_offset,
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| detections['detection_scores'],
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| category_index,
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| use_normalized_coordinates=True,
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| max_boxes_to_draw=10,
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| min_score_thresh=.4,
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| agnostic_mode=False
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| )
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|
|
|
|
| ret, buffer = cv2.imencode('.jpg', image_np_with_detections)
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| img_str = buffer.tobytes()
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|
|
| return img_str
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|
|
|
|
| @app.route('/', methods=['GET'])
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| def index():
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| return render_template('index.html')
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|
|
| if __name__ == "__main__":
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| app.run(debug=True)
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|
|
