Delete app.py

app.py

@@ -1,80 +0,0 @@
-from flask import Flask, render_template, request, jsonify
-import os
-import cv2
-import numpy as np
-import tensorflow as tf
-from object_detection.utils import label_map_util
-from object_detection.utils import visualization_utils as viz_utils
-from object_detection.builders import model_builder
-from object_detection.utils import config_util
-
-app = Flask(__name__)
-
-# Load model and label map
-CUSTOM_MODEL_NAME = 'my_ssd_mobnet'
-paths = {
-    'CHECKPOINT_PATH': os.path.join('Tensorflow', 'workspace', 'models', CUSTOM_MODEL_NAME),
-    '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'))
-detection_model = model_builder.build(model_config=configs['model'], is_training=False)
-ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
-ckpt.restore(os.path.join(paths['CHECKPOINT_PATH'], 'ckpt-8')).expect_partial()
-category_index = label_map_util.create_category_index_from_labelmap(paths['LABELMAP'])
-
-# Define detection function
-@tf.function
-def detect_fn(image):
-    image, shapes = detection_model.preprocess(image)
-    prediction_dict = detection_model.predict(image, shapes)
-    detections = detection_model.postprocess(prediction_dict, shapes)
-    return detections
-
-# Define route for object detection
-@app.route('/detect', methods=['POST'])
-def detect():
-    # Get image file from request
-    file = request.files['image']
-    
-    # Read image and convert to numpy array
-    img = cv2.imdecode(np.frombuffer(file.read(), np.uint8), cv2.IMREAD_COLOR)
-    image_np = np.array(img)
-    
-    # Perform object detection
-    input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
-    detections = detect_fn(input_tensor)
-    
-    num_detections = int(detections.pop('num_detections'))
-    detections = {key: value[0, :num_detections].numpy() for key, value in detections.items()}
-    detections['num_detections'] = num_detections
-    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
-    
-    label_id_offset = 1
-    image_np_with_detections = image_np.copy()
-    
-    viz_utils.visualize_boxes_and_labels_on_image_array(
-        image_np_with_detections,
-        detections['detection_boxes'],
-        detections['detection_classes'] + label_id_offset,
-        detections['detection_scores'],
-        category_index,
-        use_normalized_coordinates=True,
-        max_boxes_to_draw=10,
-        min_score_thresh=.4,
-        agnostic_mode=False
-    )
-    
-    # Convert image back to byte stream
-    ret, buffer = cv2.imencode('.jpg', image_np_with_detections)
-    img_str = buffer.tobytes()
-    
-    return img_str
-
-# Define index route
-@app.route('/', methods=['GET'])
-def index():
-    return render_template('index.html')
-
-if __name__ == "__main__":
-    app.run(debug=True)