initial commit

.gitignore

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+bridge_hand_detect
+bridge_hand_detect2

app.py

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+import gradio as gr
+import pandas as pd
+import subprocess
+import os
+from tempfile import mkdtemp
+from timeit import default_timer as timer
+
+# Download model and libraries from repo
+try:
+    token = os.environ.get("model_token")
+    subprocess.run(["git", "clone", f"https://oauth2:{token}@huggingface.co/vincentlui/bridge_hand_detect"])
+except:
+    print('Fail to download code')
+
+try:
+    from bridge_hand_detect2.predict import CardDetectionModel, draw_bboxes
+    from bridge_hand_detect2.pbn import create_pbn_file
+    model_dir = 'bridge_hand_detect2/model.onnx'
+except:
+    from bridge_hand_detect.predict import CardDetectionModel, draw_bboxes
+    from bridge_hand_detect.pbn import create_pbn_file
+    model_dir = 'bridge_hand_detect/model.onnx'
+
+custom_css = \
+"""
+/* Hide sort buttons at gr.DataFrame */
+.sort-button {
+    display: none !important;
+}
+"""
+
+INPUT_IMG_HEIGHT = 480
+OUTPUT_IMG_HEIGHT = 320 
+css = ".output_img {display:block; margin-left: auto; margin-right: auto}"
+model = CardDetectionModel(model_dir)
+
+def predict(image_path):
+    start = timer()
+    df = None
+    try:
+        hands, (width,height) = model(image_path)
+        print(hands)
+        # Output dataframe
+        df = pd.DataFrame(['♠', '♥', '♦', '♣'], columns=[''])
+        for hand in hands:
+            df[hand.direction] = [''.join(c) for c in hand.cards]
+    except:
+        gr.Error('Cannot process image')
+
+    end = timer()
+    print(f'Process time: {end - start:.02f} seconds')
+    
+    return df
+    
+
+default_df = df = pd.DataFrame({'':['♠', '♥', '♦', '♣'], 
+                                'N': ['']*4, 
+                                'E': ['']*4, 
+                                'S': ['']*4, 
+                                'W': ['']*4})
+
+
+def save_file2(df, cache_dir, total):
+    d = cache_dir
+    if cache_dir is None:
+        d = mkdtemp()
+    total += 1
+    file_name = f'bridgehand{total:03d}.pbn'
+    file_path = os.path.join(d,file_name)
+    with open(file_path, 'w') as f:
+        pbn_str = create_pbn_file(df)
+        f.write(pbn_str)
+
+    return file_path, d, total
+
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown(
+        """
+        # Bridge Hand Scanner - Read all four hands from an image
+
+        This app scans an image taken from (e.g. your smartphone) and reads all 52 cards. The top hand is regarded as North.
+        The results can be exported as a PBN file, which can be imported to other bridge software such as double dummy solvers.
+        1. Upload an image showing all four hands fanned as shown in the example.
+        2. Click *Submit*. The scan result will be displayed in the table.
+        3. Verify the output and correct any missing or wrong card in the table. Then click *Save* to generate a PBN file.
+
+        Tips:
+        - This AI reads the values at the top left corner of the playing cards. Make sure they are visible and as large as possible.
+        - To get the best accuracy, place the cards following the layout in the examples. 
+
+        I need your feedback to make this app more useful. Please send your comments to <vincentlui123@gmail.com>.
+        """)
+
+    total = gr.State(0)
+    gradio_cache_dir = gr.State()
+    
+    with gr.Row():
+        with gr.Column():
+            a1 = gr.Image(type="filepath",sources=['upload'],interactive=True,height=INPUT_IMG_HEIGHT)
+            with gr.Row():
+                a2 = gr.ClearButton()
+                a3 = gr.Button('Submit',variant="primary")
+            a4 = gr.Examples('examples', a1)
+
+        with gr.Column():
+            b1 = gr.Dataframe(value=default_df, datatype="str", row_count=(4,'fixed'), col_count=(5,'fixed'), 
+                            headers=['', 'N', 'E', 'S', 'W'],
+                            interactive=True, column_widths=['8%', '23%','23%','23%','23%'])
+            b2 = gr.Button('Save')
+            b3 = gr.File(interactive=False)
+
+    a2.add([a1, b1, b3])
+    a3.click(predict, [a1], [b1])
+    b2.click(save_file2, [b1, gradio_cache_dir, total], [b3, gradio_cache_dir, total])
+
+demo.queue().launch()

requirements.txt

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+scikit-learn
+onnx
+onnxruntime
+lap
+opencv-python