Initial Commit

README.md

@@ -1,10 +1,11 @@
 ---
 title: 23A052W
 emoji: 🦀
-colorFrom: purple
-colorTo: yellow
+colorFrom: indigo
+colorTo: red
+python_version: 3.8
 sdk: gradio
-sdk_version: 4.14.0
+sdk_version: 4.0.2
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -0,0 +1,79 @@
+import gradio as gr
+from huggingface_hub import snapshot_download
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+from object_detection.utils import label_map_util
+from object_detection.utils import visualization_utils as viz_utils
+
+# Path to the label map
+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_model(repo_id):
+    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 predict_combined_models(image_np, detection_model1, detection_model2)
+
+def predict_combined_models(image_np, model1, model2):
+    # Process with first model
+    results1 = model1(image_np)
+    result1 = {key:value.numpy() for key,value in results1.items()}
+
+    # Process with second model
+    results2 = model2(image_np)
+    result2 = {key:value.numpy() for key,value in results2.items()}
+
+    # Visualization for model 1
+    image_np_with_detections = image_np.copy()
+    viz_utils.visualize_boxes_and_labels_on_image_array(
+        image_np_with_detections[0],
+        result1['detection_boxes'][0],
+        (result1['detection_classes'][0]).astype(int),
+        result1['detection_scores'][0],
+        category_index,
+        use_normalized_coordinates=True,
+        max_boxes_to_draw=200,
+        min_score_thresh=.60,
+        agnostic_mode=False,
+        line_thickness=2)
+
+    # Visualization for model 2 (can adjust styles to differentiate)
+    viz_utils.visualize_boxes_and_labels_on_image_array(
+        image_np_with_detections[0],
+        result2['detection_boxes'][0],
+        (result2['detection_classes'][0]).astype(int),
+        result2['detection_scores'][0],
+        category_index,
+        use_normalized_coordinates=True,
+        max_boxes_to_draw=200,
+        min_score_thresh=.60,
+        agnostic_mode=False,
+        line_thickness=2)
+
+    # Combine and return final image
+    result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
+    return result_pil_img
+
+# Load your models
+REPO_ID1 = "dtyago/23a052w-iti107-assn2_tfodmodel"
+REPO_ID2 = "dtyago/23a052w-iti107-assn2_tfodmodel_run1"
+detection_model1 = load_model(REPO_ID1)
+detection_model2 = load_model(REPO_ID2)
+
+# Gradio interface
+gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(type="pil"), 
+    outputs=gr.Image(type="pil")
+).launch(share=True)

data/label_map.pbtxt

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+item {
+    id: 2
+    name: 'Gramophone'
+}
+item {
+    id: 3
+    name: 'Veena'
+}

requirements.txt

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+#tf2-tensorflow-object-detection-api
+tf-models-research-object-detection
+matplotlib
+wget
+Pillow==9.5
+huggingface_hub