Update app.py

app.py

@@ -1,85 +1,3 @@
-# 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()
-
-
-    
 import gradio as gr
 from PIL import Image
 from ultralytics import YOLO
@@ -158,8 +76,8 @@ def process_images(params):
     solutions = create_solutions(image_urls, all_bboxes, all_bboxes2, all_segments)
 
     result_url = f"{api}/{job_id}"
-    # Uncomment the next line if you want to send results to an API
-    # send_results_to_api(solutions, result_url)
+    
+    send_results_to_api(solutions, result_url)
 
     return json.dumps({"solutions": solutions}, indent=4)
 
@@ -169,3 +87,90 @@ 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()
+
+
+