Create app.py

app.py

@@ -0,0 +1,80 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from six import BytesIO
+from PIL import Image
+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.utils import ops as utils_op
+import tarfile
+import wget 
+import gradio as gr
+from huggingface_hub import snapshot_download
+import os 
+
+PATH_TO_LABELS = 'data/label_map.pbtxt'   
+category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
+
+def pil_image_as_numpy_array(pilimg):
+
+    img_array = tf.keras.utils.img_to_array(pilimg)
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+    
+def load_image_into_numpy_array(path):
+                                    
+    image = None
+    image_data = tf.io.gfile.GFile(path, 'rb').read()
+    image = Image.open(BytesIO(image_data))
+    return pil_image_as_numpy_array(image)            
+
+def load_model():
+    download_dir = snapshot_download(REPO_ID)
+    saved_model_dir = os.path.join(download_dir, "saved_model")
+    detection_model = tf.saved_model.load(saved_model_dir)
+    return detection_model
+
+def predict(pilimg):
+
+    image_np = pil_image_as_numpy_array(pilimg)
+    return predict2(image_np)
+
+def predict2(image_np):
+
+    results = detection_model(image_np)
+
+    # different object detection models have additional results
+    result = {key:value.numpy() for key,value in results.items()}
+    
+    label_id_offset = 0
+    image_np_with_detections = image_np.copy()
+
+    viz_utils.visualize_boxes_and_labels_on_image_array(
+        image_np_with_detections[0],
+        result['detection_boxes'][0],
+        (result['detection_classes'][0] + label_id_offset).astype(int),
+        result['detection_scores'][0],
+        category_index,
+        use_normalized_coordinates=True,
+        max_boxes_to_draw=200,
+        min_score_thresh=.60,
+        agnostic_mode=False,
+        line_thickness=2)
+
+    result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
+    
+    return result_pil_img
+
+
+REPO_ID = "ksh-nyp/TFODModel-1-LeoD"
+detection_model = load_model()
+# pil_image = Image.open(image_path)
+# image_arr = pil_image_as_numpy_array(pil_image)
+
+# predicted_img = predict(image_arr)
+# predicted_img.save('predicted.jpg')
+
+gr.Interface(fn=predict,
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Image(type="pil")
+             ).launch(share=True)