Create segmentation_model

segmentation_model

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+import torch
+import torchvision.transforms as T
+from torchvision.models.detection import maskrcnn_resnet50_fpn
+from PIL import Image
+import matplotlib.pyplot as plt
+import numpy as np
+import uuid
+import os
+import cv2
+import json 
+
+
+input_images_dir = 'data/input_images/'
+segmented_objects_dir = 'data/segmented_objects/'
+os.makedirs(input_images_dir, exist_ok=True)
+os.makedirs(segmented_objects_dir, exist_ok=True)
+
+#Loading the model
+
+def load_model():
+    model = maskrcnn_resnet50_fpn(pretrained=True)
+    # Using a different backbone
+    #model = maskrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False, backbone_name='resnext50_32x4d')
+    model.eval()  
+    """
+    We have set this to evaluation mode, 
+    because we have loaded a pretrained model 
+    so we must deactivate dropout layers and other 
+    training-specific behaviors.
+    """
+    return model
+
+model = load_model() #model initialization
+
+
+def transform_image(image):
+    transform = T.Compose([
+        T.Resize((256, 256)),  # Resize to match model input
+        T.ToTensor(),          # Convert to torch tensor
+        T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  # Normalize
+    ])
+    return transform(image).unsqueeze(0) # Add batch dimension to get [1,C,H,W] #C is channels, RGB has 3, greyscale has 1
+ 
+
+# # Test image transformation
+# image_path = "D:\multiobject.jpeg"  # Replace with the path to your image
+# image_tensor = transform_image(image_path)
+
+def run_inference(model,image_tensor):
+    with torch.no_grad():
+        outputs = model(image_tensor)
+    return outputs
+
+def extract_object(image, mask):
+    img_np = np.array(image)
+    
+    # Resize mask to match image dimensions
+    mask_resized = cv2.resize(mask, (img_np.shape[1], img_np.shape[0]), interpolation=cv2.INTER_NEAREST)
+    
+    # Create an empty image with the same dimensions as the original image
+    object_img = np.zeros_like(img_np)
+
+    # Apply the mask to the image
+    for c in range(3):  # Assuming image has 3 channels (RGB)
+        object_img[:, :, c] = img_np[:, :, c] * mask_resized
+    
+    return Image.fromarray(object_img)
+
+# def extract_object(image, mask):
+#     object_img = Image.fromarray((np.array(image) * mask[:, :, None]).astype(np.uint8))
+#     return object_img
+
+# Save the input image
+def save_input_image(image, master_id):
+    input_image_path = os.path.join(input_images_dir, f'{master_id}.png')
+    image.save(input_image_path)
+    return input_image_path
+
+# Save the extracted objects and their metadata
+def save_objects_and_metadata(extracted_objects, master_id):
+    object_metadata = []
+    
+    for i, obj_img in enumerate(extracted_objects):
+        object_id = str(uuid.uuid4())
+        object_image_path = os.path.join(segmented_objects_dir, f'{object_id}.png')
+        obj_img.save(object_image_path)
+        
+        metadata = {
+            'object_id': object_id,
+            'master_id': master_id,
+            'object_image_path': object_image_path
+        }
+        object_metadata.append(metadata)
+    
+    metadata_file = os.path.join(segmented_objects_dir, f'{master_id}_metadata.json')
+    with open(metadata_file, 'w') as f:
+        json.dump(object_metadata, f, indent=4)
+    
+    return object_metadata
+# Run inference
+#print(outputs)  # This will print the model's output, including masks, labels, and scores
+
+
+# def extract_objects(image, masks):
+#     """
+#     Extract objects from the segmented image using masks.
+    
+#     Args:
+#     - image (PIL.Image): The original image.
+#     - masks (Tensor): Masks obtained from the segmentation model.
+    
+#     Returns:
+#     - List of extracted objects as images.
+#     """
+#     image_np = np.array(image)
+#     extracted_objects = []
+    
+#     for i, mask in enumerate(masks):
+#         # Convert mask to binary
+#         binary_mask = mask[0].mul(255).byte().cpu().numpy()
+        
+#         # Extract object using the mask
+#         masked_image = cv2.bitwise_and(image_np, image_np, mask=binary_mask)
+        
+#         # Find the bounding box of the object
+#         x, y, w, h = cv2.boundingRect(binary_mask)
+#         cropped_object = masked_image[y:y+h, x:x+w]
+        
+#         # Convert cropped object back to PIL Image
+#         cropped_object_pil = Image.fromarray(cropped_object)
+#         extracted_objects.append(cropped_object_pil)
+    
+#     return extracted_objects
+
+# import os
+# import uuid
+# from PIL import Image
+# import json
+
+# # Directories to save the input images and segmented objects
+# input_images_dir = 'data/input_images/'
+# segmented_objects_dir = 'data/segmented_objects/'
+# os.makedirs(input_images_dir, exist_ok=True)
+# os.makedirs(segmented_objects_dir, exist_ok=True)
+
+# def save_input_image(image, master_id):
+#     """
+#     Save the original input image with a unique master ID.
+    
+#     Args:
+#     - image (PIL.Image): The original input image.
+#     - master_id (str): Unique ID for the original image.
+    
+#     Returns:
+#     - str: Path to the saved input image.
+#     """
+#     input_image_path = os.path.join(input_images_dir, f'{master_id}.png')
+#     image.save(input_image_path)
+#     return input_image_path
+
+# def save_objects_and_metadata(extracted_objects, master_id):
+#     """
+#     Save the extracted objects as images and store their metadata.
+    
+#     Args:
+#     - extracted_objects (List[PIL.Image]): List of extracted objects as images.
+#     - master_id (str): Unique ID for the original image.
+    
+#     Returns:
+#     - List of metadata dictionaries for each object.
+#     """
+#     object_metadata = []
+    
+#     for i, obj_img in enumerate(extracted_objects):
+#         # Generate a unique ID for each object
+#         object_id = str(uuid.uuid4())
+        
+#         # Save the object image
+#         object_image_path = os.path.join(segmented_objects_dir, f'{object_id}.png')
+#         obj_img.save(object_image_path)
+        
+#         # Prepare metadata for the object
+#         metadata = {
+#             'object_id': object_id,
+#             'master_id': master_id,
+#             'object_image_path': object_image_path
+#         }
+#         object_metadata.append(metadata)
+    
+#     # Save metadata to JSON (or you can save to a database)
+#     metadata_file = os.path.join(segmented_objects_dir, f'{master_id}_metadata.json')
+#     with open(metadata_file, 'w') as f:
+#         json.dump(object_metadata, f, indent=4)
+    
+#     return object_metadata
+
+# # Example usage
+# master_id = str(uuid.uuid4())  # Generate a unique master ID for the original image
+
+# # Save the input image
+# input_image_path = save_input_image(image, master_id)
+
+# # Save the objects and their metadata
+# metadata = save_objects_and_metadata(extracted_objects, master_id)
+
+
+
+
+
+
+# import cv2
+# import os
+# import json
+# import uuid
+# import numpy as np
+# from PIL import Image
+
+# # Directories to save the segmented objects and metadata
+# segmented_objects_dir = 'data/segmented_objects/'
+# metadata_file = 'data/segmented_objects_metadata.json'
+
+# # Ensure directories exist
+# os.makedirs(segmented_objects_dir, exist_ok=True)
+
+# def extract_objects(image_path, masks, master_id):
+#     # Load the original image
+#     image = Image.open(image_path)
+#     image_np = np.array(image)
+    
+#     object_metadata = []
+    
+#     for i, mask in enumerate(masks):
+#         # Generate a unique ID for each object
+#         object_id = str(uuid.uuid4())
+        
+#         # Extract object using the mask
+#         masked_image = cv2.bitwise_and(image_np, image_np, mask=mask)
+        
+#         # Find the bounding box of the object
+#         x, y, w, h = cv2.boundingRect(mask)
+#         cropped_object = masked_image[y:y+h, x:x+w]
+        
+#         # Save the object image
+#         object_image_path = os.path.join(segmented_objects_dir, f'{object_id}.png')
+#         cv2.imwrite(object_image_path, cropped_object)
+        
+#         # Save metadata
+#         object_metadata.append({
+#             'object_id': object_id,
+#             'master_id': master_id,
+#             'object_image_path': object_image_path,
+#             'bounding_box': (x, y, w, h)
+#         })
+    
+#     # Save metadata to JSON
+#     with open(metadata_file, 'w') as f:
+#         json.dump(object_metadata, f, indent=4)
+
+#     return object_metadata
+
+# # Example usage:
+# # Assuming `masks` is a list of binary masks (numpy arrays) from your segmentation model
+# # and `image_path` is the path to the original image
+# master_id = str(uuid.uuid4())
+# image_path = 'data/input_images/sample_image.png'
+# masks = [...]  # Replace with actual masks
+
+# object_metadata = extract_objects(image_path, masks, master_id)
+
+
+# #Extracting and saving segmented objects
+# # def save_segmented_objects(image_path, outputs, output_dir='data\segmented_objects'):
+# #     image = Image.open(image_path).convert("RGB")
+# #     image_np = np.array(image)
+# #     masks = outputs[0]['masks']
+# #     scores = outputs[0]['scores']
+    
+# #     if not os.path.exists(output_dir):
+# #         os.makedirs(output_dir)
+    
+# #     for i in range(len(scores)):
+# #         if scores[i] > 0.5:  # Confidence threshold
+# #             mask = masks[i].squeeze().cpu().numpy()
+# #             mask = np.where(mask > 0.5, 1, 0).astype(np.uint8)  # Binarize mask
+            
+# #             # Create a new image for the masked object
+# #             masked_image = np.zeros_like(image_np)
+# #             for c in range(3):  # Apply the mask to each channel (R, G, B)
+# #                 masked_image[:, :, c] = image_np[:, :, c] * mask
+            
+# #             # Save the masked object
+# #             masked_image_pil = Image.fromarray(masked_image)
+# #             masked_image_pil.save(f"{output_dir}object_{i+1}.png")
+
+# # # Run the function to save segmented objects
+# # save_segmented_objects(image_path, outputs)