Update app.py

app.py

@@ -284,7 +284,7 @@ def _predict_single_dog(image):
     return top1_prob, topk_breeds, topk_probs_percent
 
 
-async def detect_multiple_dogs(image, conf_threshold=0.2, iou_threshold=0.5):
+async def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.5):
     results = model_yolo(image, conf=conf_threshold, iou=iou_threshold)[0]
     dogs = []
     for box in results.boxes:
@@ -296,118 +296,6 @@ async def detect_multiple_dogs(image, conf_threshold=0.2, iou_threshold=0.5):
     return dogs
 
 
-# async def predict(image):
-#     if image is None:
-#         return "Please upload an image to start.", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
-#     try:
-#         if isinstance(image, np.ndarray):
-#             image = Image.fromarray(image)
-
-#         # 嘗試檢測多隻狗
-#         dogs = await detect_multiple_dogs(image)
-#         if len(dogs) == 0:
-#             # 單狗情境
-#             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
-#             if top1_prob < 0.2:
-#                 return "The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
-#             breed = topk_breeds[0]
-#             description = get_dog_description(breed)
-
-#             if top1_prob >= 0.5:
-#                 formatted_description = format_description(description, breed)
-#                 return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-#             else:
-#                 explanation = (
-#                     f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n"
-#                     f"1. **{topk_breeds[0]}** ({topk_probs_percent[0]} confidence)\n"
-#                     f"2. **{topk_breeds[1]}** ({topk_probs_percent[1]} confidence)\n"
-#                     f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]} confidence)\n\n"
-#                     "Click on a button to view more information about the breed."
-#                 )
-#                 return explanation, image, gr.update(visible=True, value=f"More about {topk_breeds[0]}"), gr.update(visible=True, value=f"More about {topk_breeds[1]}"), gr.update(visible=True, value=f"More about {topk_breeds[2]}")
-
-#         # 多狗情境
-#         color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
-#         explanations = []
-#         annotated_image = image.copy()
-#         draw = ImageDraw.Draw(annotated_image)
-#         font = ImageFont.load_default()
-
-#         for i, (cropped_image, _, box) in enumerate(dogs):
-#             top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(cropped_image)
-#             color = color_list[i % len(color_list)]
-#             draw.rectangle(box, outline=color, width=3)
-#             draw.text((box[0], box[1]), f"Dog {i+1}", fill=color, font=font)
-
-#             breed = topk_breeds[0]
-#             if top1_prob >= 0.5:
-#                 description = get_dog_description(breed)
-#                 formatted_description = format_description(description, breed)
-#                 explanations.append(f"Dog {i+1}: {formatted_description}")
-#             elif top1_prob >= 0.2:
-#                 explanations.append(f"Dog {i+1}: Top 3 possible breeds:\n"
-#                                     f"1. **{topk_breeds[0]}** ({topk_probs_percent[0]} confidence)\n"
-#                                     f"2. **{topk_breeds[1]}** ({topk_probs_percent[1]} confidence)\n"
-#                                     f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]} confidence)")
-#             else:
-#                 explanations.append(f"Dog {i+1}: The image is unclear or the breed is not in the dataset.")
-
-#         final_explanation = "\n\n".join(explanations)
-#         return final_explanation, annotated_image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
-#     except Exception as e:
-#         return f"An error occurred: {str(e)}", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
-
-# def show_details(choice):
-#     if not choice:
-#         return "Please select a breed to view details."
-
-#     try:
-#         breed = choice.split("More about ")[-1]
-#         description = get_dog_description(breed)
-#         return format_description(description, breed)
-#     except Exception as e:
-#         return f"An error occurred while showing details: {e}"
-
-
-# with gr.Blocks() as iface:
-#     gr.HTML("<h1 style='text-align: center;'>🐶 Dog Breed Classifier 🔍</h1>")
-#     gr.HTML("<p style='text-align: center;'>Upload a picture of a dog, and the model will predict its breed, provide detailed information, and include an extra information link!</p>")
-    
-#     with gr.Row():
-#         input_image = gr.Image(label="Upload a dog image", type="pil")
-#         output_image = gr.Image(label="Annotated Image")
-    
-#     output = gr.Markdown(label="Prediction Results")
-    
-#     with gr.Row():
-#         btn1 = gr.Button("View More 1", visible=False)
-#         btn2 = gr.Button("View More 2", visible=False)
-#         btn3 = gr.Button("View More 3", visible=False)
-
-#     input_image.change(
-#         predict,
-#         inputs=input_image,
-#         outputs=[output, output_image, btn1, btn2, btn3]
-#     )
-
-#     btn1.click(show_details, inputs=btn1, outputs=output)
-#     btn2.click(show_details, inputs=btn2, outputs=output)
-#     btn3.click(show_details, inputs=btn3, outputs=output)
-
-#     gr.Examples(
-#         examples=['Border_Collie.jpg', 'Golden_Retriever.jpeg', 'Saint_Bernard.jpeg', 'French_Bulldog.jpeg', 'Samoyed.jpg'],
-#         inputs=input_image
-#     )
-
-#     gr.HTML('For more details on this project and other work, feel free to visit my GitHub <a href="https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/Dog_Breed_Classifier">Dog Breed Classifier</a>')
-
-# if __name__ == "__main__":
-#     iface.launch()
-
-
 async def predict(image):
     if image is None:
         return "Please upload an image to start.", None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), None
@@ -416,7 +304,7 @@ async def predict(image):
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image)
 
-        dogs = await detect_multiple_dogs(image, conf_threshold=0.05)
+        dogs = await detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.5)
         
         if len(dogs) <= 1:
             return await process_single_dog(image)