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BP_Multiple_Objects_Complicated_v1(1).pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:44c578156d90f485fae816d44525f8214ddf9847c46aad6345f5beff5d91691e
+size 54829003

app.py

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+import gradio as gr
+from PIL import Image
+from ultralytics import YOLO
+import requests
+import json
+import logging
+
+logging.basicConfig(level=logging.INFO)
+
+model = YOLO("BP_Multiple_Objects_Complicated_v1.pt")
+
+def detect_objects(images):
+    results = model(images)
+    all_bboxes = []
+    all_bboxes2 = []
+    all_segments = []
+    for result in results:
+        boxes = result.boxes.xywhn.tolist()
+        boxes2 = result.boxes.xywh.tolist()
+        all_bboxes.append(boxes)
+        all_bboxes2.append(boxes2)
+
+        if result.masks is not None:
+            masks = result.masks.xyn                         
+            sub_arrays = [arr.tolist() for arr in masks]
+        else:
+            sub_arrays = []
+            
+        all_segments.append(sub_arrays)
+
+    return all_bboxes, all_bboxes2, all_segments
+
+def create_solutions(image_urls, all_bboxes, all_bboxes2, all_segments):
+    solutions = []   
+    img_id = 1
+    box_id = 1
+    cat_id = 1
+    for image_url, bbox, bbox2, segmnt in zip(image_urls, all_bboxes, all_bboxes2, all_segments):       
+
+        for subbox, subbox2, subsegmnt in zip(bbox, bbox2, segmnt):
+            w = subbox2[2]
+            h = subbox2[3]
+            area = w * h
+
+            flattened_segmnt = [item for sublist in subsegmnt for item in sublist]
+
+            ans = {"image_id": img_id, "id": box_id, "area": area, "category_id": cat_id, "bbox": subbox, "segment": flattened_segmnt}
+            obj ={"url": image_url, "answer":[ans]}
+            solutions.append(obj)
+            box_id += 1
+        img_id += 1
+    return solutions
+
+
+# def send_results_to_api(data, result_url):
+#     headers = {"Content-Type": "application/json"}
+#     response = requests.post(result_url, json=data, headers=headers)
+#     if response.status_code == 200:
+#         return response.json()
+#     else:
+#         return {"error": f"Failed to send results to API: {response.status_code}"}
+
+
+def process_images(params):
+    try:
+        params = json.loads(params)
+    except json.JSONDecodeError as e:
+        logging.error(f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}")
+        return {"error": f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}"}
+    
+    image_urls = params.get("urls", [])
+    # api = params.get("api", "")
+    # job_id = params.get("job_id", "")
+
+    if not image_urls:
+        logging.error("Missing required parameters: 'urls'")
+        return {"error": "Missing required parameters: 'urls'"}
+
+    try:
+        images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls]
+    except Exception as e:
+        logging.error(f"Error loading images: {e}")
+        return {"error": f"Error loading images: {str(e)}"}
+    
+    all_bboxes, all_bboxes2, all_segments = detect_objects(images)
+    solutions = create_solutions(image_urls, all_bboxes, all_bboxes2, all_segments)
+
+    # result_url = f"{api}/{job_id}"
+    # send_results_to_api(solutions, result_url)
+
+    return json.dumps({"solutions": solutions})
+
+
+inputt = gr.Textbox(label="Parameters (JSON format)")
+outputs = gr.JSON()
+
+application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Multiple Object Segmentation with API Integration")
+application.launch()
+
+
+
+
+
+# import gradio as gr
+# from PIL import Image
+# from ultralytics import YOLO
+# import requests
+# import json
+
+# model = YOLO("BP_Multiple_Objects_Complicated_v1.pt")
+
+# def detect_objects(images):
+#     results = model(images)
+#     all_bboxes = []
+#     all_bboxes2 = []
+#     all_segments = []
+#     for result in results:
+#         boxes = result.boxes.xywhn.tolist()
+#         boxes2 = result.boxes.xywh.tolist()
+#         all_bboxes.append(boxes)
+#         all_bboxes2.append(boxes2)
+
+#         masks = result.masks.xyn                         
+#         sub_arrays = [arr.tolist() for arr in masks]     
+#         all_segments.append(sub_arrays)
+
+#         return all_bboxes, all_bboxes2, all_segments
+
+# def create_solutions(image_urls, all_bboxes, all_bboxes2, all_segments):
+#     solutions = []   
+#     img_id =1
+#     box_id =1
+#     cat_id =1
+#     for image_url, bbox, bbox2, segmnt in zip(image_urls, all_bboxes, all_bboxes2, all_segments):       
+
+#         for subbox, subbox2, subsegmnt in zip(bbox, bbox2, segmnt):
+
+#             w = subbox2[2]
+#             h = subbox2[3]
+#             area = w*h
+
+#             flattened_segmnt = [item for sublist in subsegmnt for item in sublist]
+
+#             obj = {"image_id":img_id, "image_url": image_url, "id":box_id, "area":area, "category_id":cat_id, "bbox": subbox, "segment":flattened_segmnt}                 # Create an object for each image
+#             box_id +=1
+#             solutions.append(obj)                     
+#         img_id +=1
+#     return solutions
+
+# def send_results_to_api(data, result_url):
+#     # Example function to send results to an API
+#     headers = {"Content-Type": "application/json"}
+#     response = requests.post(result_url, json=data, headers=headers)
+#     if response.status_code == 200:
+#         return response.json()  # Return any response from the API if needed
+#     else:
+#         return {"error": f"Failed to send results to API: {response.status_code}"}
+
+# def process_images(params):
+#     # Parse the JSON string into a dictionary
+#     params = json.loads(params)
+    
+#     image_urls = params.get("image_urls", [])
+#     api = params.get("api", "")
+#     job_id = params.get("job_id", "")
+
+#     images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls]  # images from URLs
+
+#     all_bboxes, all_bboxes2, all_segments = detect_objects(images)  # Perform object detection
+#     solutions = create_solutions(image_urls, all_bboxes, all_bboxes2, all_segments)  # Create solutions with image URLs and bounding boxes
+
+#     result_url = f"{api}/{job_id}"
+#     # send_results_to_api(solutions, result_url)
+
+#     return json.dumps({"solutions": solutions}, indent=4)
+
+
+# inputt = gr.Textbox(label="Parameters (JSON format)")
+# outputs = gr.JSON()
+
+# application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Multiple Object Segmentation with API Integration")
+# application.launch()
+
+
+    

requirements.txt

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+ultralytics