app file

script.py

@@ -0,0 +1,72 @@
+import streamlit as st
+import cv2
+from PIL import Image
+import os
+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
+
+
+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-3')).expect_partial()
+category_index = label_map_util.create_category_index_from_labelmap(paths['LABELMAP'])
+
+
+@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
+
+
+def main():
+    st.title('Furniture Detection')
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+    if uploaded_file is not None:
+        image = np.array(Image.open(uploaded_file))
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+        st.write("")
+        st.write("Detection In Process...")
+
+        
+        input_tensor = tf.convert_to_tensor(np.expand_dims(image, 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.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=5,
+            min_score_thresh=.3,
+            agnostic_mode=False
+        )
+
+        st.image(image_np_with_detections, caption='Detected Teeth', use_column_width=True)
+
+if __name__ == "__main__":
+    main()