Update app.py

app.py

@@ -1,3 +1,85 @@
+# 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
@@ -21,52 +103,58 @@ def detect_objects(images):
         sub_arrays = [arr.tolist() for arr in masks]     
         all_segments.append(sub_arrays)
 
-        return all_bboxes, all_bboxes2, all_segments
+    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
+    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
+            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
+            obj = {
+                "image_id": img_id, 
+                "image_url": image_url, 
+                "id": box_id, 
+                "area": area, 
+                "category_id": cat_id, 
+                "bbox": subbox, 
+                "segment": flattened_segmnt
+            }
+            solutions.append(obj)
+            box_id += 1
+        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
+        return response.json()
     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
+    images = [Image.open(requests.get(url, stream=True).raw) for url in image_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
+    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}"
+    # Uncomment the next line if you want to send results to an API
     # send_results_to_api(solutions, result_url)
 
     return json.dumps({"solutions": solutions}, indent=4)
@@ -76,4 +164,4 @@ 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()
+application.launch()