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Multiple_Object_BB_Detection_v1.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3790075f525122a276322023210320447f931f314d9a4e60279a55e08c698829
+size 52033665

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("Multiple_Object_BB_Detection_v1.pt")
+
+def detect_objects(images):
+    results = model(images)
+    all_bboxes = []
+    all_bboxes2 = []
+    for result in results:
+        boxes = result.boxes.xywhn.tolist()
+        boxes2 = result.boxes.xywh.tolist()
+        all_bboxes.append(boxes)
+        all_bboxes2.append(boxes2)
+    return all_bboxes, all_bboxes2
+
+def create_solutions(image_urls, all_bboxes, all_bboxes2):
+    solutions = []  
+    img_id =1
+    box_id =1
+    cat_id =1
+    for image_url, bbox, bbox2 in zip(image_urls, all_bboxes, all_bboxes2):       # Loop through each image ID and its corresponding prediction
+
+        for subbox, subbox2 in zip(bbox, bbox2):
+
+            w = subbox2[2]
+            h = subbox2[3]
+            area = w*h
+            seg=[[]]
+            ans = {"segmentation":seg, "area":area, 'iscrowd':0, "image_id":img_id, "bbox": subbox, "category_id":cat_id, "id":box_id }
+            ansx=[]
+            ansx.append(ans)
+            obj = {"url": image_url, 'answer':ansx}                 
+            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):
+    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]  # images from URLs
+    except Exception as e:
+        logging.error(f"Error loading images: {e}")
+        return {"error": f"Error loading images: {str(e)}"}
+        
+    all_bboxes, all_bboxes2 = detect_objects(images)  # Perform object detection
+    solutions = create_solutions(image_urls, all_bboxes, all_bboxes2)  # 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})
+
+
+inputt = gr.Textbox(label="Parameters (JSON format)")
+outputs = gr.JSON()
+
+application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Multiple Object Detection with API Integration")
+application.launch()

requirements.txt

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+ultralytics