Delete script.py

script.py

@@ -1,72 +0,0 @@
-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()