Update code.txt

code.txt

@@ -1,212 +1,56 @@
-try:
-    import detectron2
-except:
-    import os 
-    os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
-
-from matplotlib.pyplot import axis
-import gradio as gr
-import requests
-import numpy as np
-from torch import nn
-import requests
-
-import torch
-import detectron2
-from detectron2 import model_zoo
-from detectron2.engine import DefaultPredictor
-from detectron2.config import get_cfg
-from detectron2.utils.visualizer import Visualizer
-from detectron2.data import MetadataCatalog
-from detectron2.utils.visualizer import ColorMode
-
-damage_model_path = 'damage/model_final.pth'
-scratch_model_path = 'scratch/model_final.pth'
-parts_model_path = 'parts/model_final.pth'
-
-if torch.cuda.is_available():
-    device = 'cuda'
-else:
-    device = 'cpu'
-
-cfg_scratches = get_cfg()
-cfg_scratches.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
-cfg_scratches.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.8
-cfg_scratches.MODEL.ROI_HEADS.NUM_CLASSES = 1
-cfg_scratches.MODEL.WEIGHTS = scratch_model_path
-cfg_scratches.MODEL.DEVICE = device
-
-predictor_scratches = DefaultPredictor(cfg_scratches)
-
-metadata_scratch = MetadataCatalog.get("car_dataset_val")
-metadata_scratch.thing_classes = ["scratch"]
-
-cfg_damage = get_cfg()
-cfg_damage.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
-cfg_damage.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7
-cfg_damage.MODEL.ROI_HEADS.NUM_CLASSES = 1
-cfg_damage.MODEL.WEIGHTS = damage_model_path
-cfg_damage.MODEL.DEVICE = device
-
-predictor_damage = DefaultPredictor(cfg_damage)
-
-metadata_damage = MetadataCatalog.get("car_damage_dataset_val")
-metadata_damage.thing_classes = ["damage"]
-
-cfg_parts = get_cfg()
-cfg_parts.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
-cfg_parts.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.75
-cfg_parts.MODEL.ROI_HEADS.NUM_CLASSES = 19
-cfg_parts.MODEL.WEIGHTS = parts_model_path
-cfg_parts.MODEL.DEVICE = device
-
-predictor_parts = DefaultPredictor(cfg_parts)
-
-metadata_parts = MetadataCatalog.get("car_parts_dataset_val")
-metadata_parts.thing_classes = ['_background_',
- 'back_bumper',
- 'back_glass',
- 'back_left_door',
- 'back_left_light',
- 'back_right_door',
- 'back_right_light',
- 'front_bumper',
- 'front_glass',
- 'front_left_door',
- 'front_left_light',
- 'front_right_door',
- 'front_right_light',
- 'hood',
- 'left_mirror',
- 'right_mirror',
- 'tailgate',
- 'trunk',
- 'wheel']
-
-def merge_segment(pred_segm):
-    merge_dict = {}
-    for i in range(len(pred_segm)):
-        merge_dict[i] = []
-        for j in range(i+1,len(pred_segm)):
-            if torch.sum(pred_segm[i]*pred_segm[j])>0:
-                merge_dict[i].append(j)
-    
-    to_delete = []
-    for key in merge_dict:
-        for element in merge_dict[key]:
-            to_delete.append(element)
-    
-    for element in to_delete:
-        merge_dict.pop(element,None)
-        
-    empty_delete = []
-    for key in merge_dict:
-        if merge_dict[key] == []:
-            empty_delete.append(key)
-    
-    for element in empty_delete:
-        merge_dict.pop(element,None)
+import os
+import shutil
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+# Function to copy images to respective folders based on their labels
+def copy_image(file_info):
+    src, dst_folder = file_info
+    dst = os.path.join(dst_folder, os.path.basename(src))
+    try:
+        shutil.copy2(src, dst)  # Copy file to destination folder
+    except Exception as e:
+        print(f"Error copying {src}: {e}")
+
+# Function to organize images into good and bad folders
+def organize_images(image_folder, labels, destination_folder, num_threads=100):
+    # Create destination directories if they don't exist
+    good_folder = os.path.join(destination_folder, 'good')
+    bad_folder = os.path.join(destination_folder, 'bad')
+    os.makedirs(good_folder, exist_ok=True)
+    os.makedirs(bad_folder, exist_ok=True)
+
+    file_info_list = []
+    
+    # Iterate over the labels and create file_info for each image
+    for image_name, label in labels.items():
+        src = os.path.join(image_folder, image_name)
+        if label == "good":
+            dst_folder = good_folder
+        else:
+            dst_folder = bad_folder
         
-    for key in merge_dict:
-        for element in merge_dict[key]:
-            pred_segm[key]+=pred_segm[element]
-            
-    except_elem = list(set(to_delete))
+        file_info_list.append((src, dst_folder))
     
-    new_indexes = list(range(len(pred_segm)))
-    for elem in except_elem:
-        new_indexes.remove(elem)
+    # Use ThreadPoolExecutor to copy files in parallel
+    with ThreadPoolExecutor(max_workers=num_threads) as executor:
+        futures = [executor.submit(copy_image, file_info) for file_info in file_info_list]
         
-    return pred_segm[new_indexes]
-
-    
-def inference(image):
-    img = np.array(image)
-    outputs_damage = predictor_damage(img)
-    outputs_parts = predictor_parts(img)
-    outputs_scratch = predictor_scratches(img)
-    out_dict = outputs_damage["instances"].to("cpu").get_fields()
-    merged_damage_masks = merge_segment(out_dict['pred_masks'])
-    scratch_data = outputs_scratch["instances"].get_fields()
-    scratch_masks = scratch_data['pred_masks']
-    damage_data = outputs_damage["instances"].get_fields()
-    damage_masks = damage_data['pred_masks']
-    parts_data = outputs_parts["instances"].get_fields()
-    parts_masks = parts_data['pred_masks']
-    parts_classes = parts_data['pred_classes']
-    new_inst = detectron2.structures.Instances((1024,1024))
-    new_inst.set('pred_masks',merge_segment(out_dict['pred_masks']))
-    
-    parts_damage_dict = {}
-    parts_list_damages = []
-    for part in parts_classes:
-        parts_damage_dict[metadata_parts.thing_classes[part]] = []
-    for mask in scratch_masks:
-        for i in range(len(parts_masks)):
-            if torch.sum(parts_masks[i]*mask)>0:
-                parts_damage_dict[metadata_parts.thing_classes[parts_classes[i]]].append('scratch')
-                parts_list_damages.append(f'{metadata_parts.thing_classes[parts_classes[i]]} has scratch')              
-                print(f'{metadata_parts.thing_classes[parts_classes[i]]} has scratch')
-    for mask in merged_damage_masks:
-        for i in range(len(parts_masks)):
-            if torch.sum(parts_masks[i]*mask)>0:
-                parts_damage_dict[metadata_parts.thing_classes[parts_classes[i]]].append('damage')
-                parts_list_damages.append(f'{metadata_parts.thing_classes[parts_classes[i]]} has damage')
-                print(f'{metadata_parts.thing_classes[parts_classes[i]]} has damage')
+        # Optional: Track the progress
+        for future in as_completed(futures):
+            future.result()  # Wait for all threads to complete
 
-    v_d = Visualizer(img[:, :, ::-1],
-                   metadata=metadata_damage, 
-                   scale=0.5, 
-                   instance_mode=ColorMode.SEGMENTATION   # remove the colors of unsegmented pixels. This option is only available for segmentation models
-    )
-    #v_d = Visualizer(img,scale=1.2)
-    #print(outputs["instances"].to('cpu'))
-    out_d = v_d.draw_instance_predictions(new_inst)
-    img1 = out_d.get_image()[:, :, ::-1]
-
-    v_s = Visualizer(img[:, :, ::-1],
-                   metadata=metadata_scratch, 
-                   scale=0.5, 
-                   instance_mode=ColorMode.SEGMENTATION   # remove the colors of unsegmented pixels. This option is only available for segmentation models
-    )
-    #v_s = Visualizer(img,scale=1.2)
-    out_s = v_s.draw_instance_predictions(outputs_scratch["instances"])
-    img2 = out_s.get_image()[:, :, ::-1]
-
-    v_p = Visualizer(img[:, :, ::-1],
-                   metadata=metadata_parts, 
-                   scale=0.5, 
-                   instance_mode=ColorMode.SEGMENTATION   # remove the colors of unsegmented pixels. This option is only available for segmentation models
-    )
-    #v_p = Visualizer(img,scale=1.2)
-    out_p = v_p.draw_instance_predictions(outputs_parts["instances"])
-    img3 = out_p.get_image()[:, :, ::-1]
-    
-    return img1, img2, img3, parts_list_damages
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("## Inputs")
-            image = gr.Image(type="pil",label="Input")
-            submit_button = gr.Button(value="Submit", label="Submit")
-        with gr.Column():
-            gr.Markdown("## Outputs")
-            with gr.Tab('Image of damages'):
-                im1 = gr.Image(type='numpy',label='Image of damages')
-            with gr.Tab('Image of scratches'):
-                im2 = gr.Image(type='numpy',label='Image of scratches')
-            with gr.Tab('Image of parts'):
-                im3 = gr.Image(type='numpy',label='Image of car parts')
-            with gr.Tab('Information about damaged parts'):
-                intersections = gr.Textbox(label='Information about type of damages on each part')
-    
-    #actions
-    submit_button.click(
-        fn=inference,
-        inputs = [image],
-        outputs = [im1,im2,im3,intersections]
-    )
-        
 if __name__ == "__main__":
-    demo.launch()
+    # Define your image folder and destination folder
+    image_folder = "/path/to/your/image_folder"
+    destination_folder = "/path/to/your/destination_folder"
+    
+    # Your labels dictionary (image_name: label)
+    labels = {
+        "image1.jpg": "good",
+        "image2.jpg": "bad",
+        "image3.jpg": "good",
+        # Add the rest of your image labels here (1M entries)
+    }
+
+    # Organize images using 100 threads
+    organize_images(image_folder, labels, destination_folder, num_threads=100)