app file

app.py

@@ -0,0 +1,80 @@
+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)