diff --git a/.gitignore b/.gitignore
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+/__pycache__/
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diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1 +1,24 @@
-First Commit
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+### Start
+
+See the `conf.py`:
+```
+scheme = 'B'
+
+schemeA = {
+    'image_name': 'd1.jpg',
+    'is_lama': False,
+    'inpaint_radius': 3,
+    'is_gaussianblur': True,
+    'gaussian_radius': 9 # Odd number
+}
+
+schemeB = {
+    'image_name': 'd1.jpg',
+    'gaussian_radius': 51, # Odd number
+    'is_use_fill_color': False,
+    'fill_color': [227, 234, 244]
+}
+```
+You can choose scheme whcih you want.
+The A is useof openCV inpaint or model(LaMa); And B is useof openCV but no inpaint.
+When you finished the conf, run `python main.py`.
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diff --git a/conf.py b/conf.py
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+++ b/conf.py
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+scheme = 'B'
+
+schemeA = {
+    'image_name': 'd1.jpg',
+    'is_lama': False,
+    'inpaint_radius': 3,
+    'is_gaussianblur': True,
+    'gaussian_radius': 9 # Odd number
+}
+
+schemeB = {
+    'image_name': 'd2.jpg',
+    'gaussian_radius': 51, # Odd number
+    'is_use_fill_color': False,
+    'fill_color': [227, 234, 244]
+}
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diff --git a/images/d1.jpg b/images/d1.jpg
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diff --git a/images/d2.jpg b/images/d2.jpg
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diff --git a/images/d3.jpg b/images/d3.jpg
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diff --git a/main.py b/main.py
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+++ b/main.py
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+import conf
+import subprocess
+
+
+scheme = conf.scheme
+
+
+def run_script(script_name):
+    try:
+        subprocess.run(['python', script_name], check=True)
+        print(f"{script_name} success！")
+
+    except subprocess.CalledProcessError as e:
+        print(f'Run {script_name} error: {e}')
+
+    except FileNotFoundError:
+        print(f'Can not found: {script_name}')
+
+
+if __name__ == '__main__':
+    if scheme == 'A':
+        run_script("schemeA.py")
+
+    elif scheme == 'B':
+        run_script("schemeB.py")
+
+    else:
+        print('请检查配置文件conf.py中的scheme字段')
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diff --git a/outputs/output_A_d1.jpg b/outputs/output_A_d1.jpg
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diff --git a/outputs/output_A_d2.jpg b/outputs/output_A_d2.jpg
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diff --git a/outputs/output_B_d1.jpg b/outputs/output_B_d1.jpg
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diff --git a/outputs/output_B_d2.jpg b/outputs/output_B_d2.jpg
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diff --git a/outputs/output_lama_A_d1.jpg b/outputs/output_lama_A_d1.jpg
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diff --git a/outputs/output_lama_A_d2.jpg b/outputs/output_lama_A_d2.jpg
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diff --git a/requirements.txt b/requirements.txt
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+++ b/requirements.txt
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+numpy==2.2.4
+opencv_python==4.11.0.86
+opencv_python_headless==4.11.0.86
+Pillow==9.5.0
+Pillow==11.1.0
+simple_lama_inpainting==0.1.2
+ultralytics==8.3.85
diff --git a/schemeA.py b/schemeA.py
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+++ b/schemeA.py
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+import cv2
+from PIL import Image
+import numpy as np
+from ultralytics import YOLO
+from simple_lama_inpainting import SimpleLama
+import conf
+
+
+image_name = conf.schemeA['image_name']
+is_lama = conf.schemeA['is_lama']
+inpaint_radius = conf.schemeA['inpaint_radius']
+is_gaussianblur = conf.schemeA['is_gaussianblur']
+gaussian_radius = conf.schemeA['gaussian_radius']
+
+
+model = YOLO('yolov5s.pt')
+
+
+if is_lama == True:
+    simple_lama = SimpleLama()
+
+
+def imwrite(image):
+    output_path = 'outputs/' + 'output_lama_A_' + image_name if is_lama == True else 'outputs/' + 'output_A_' + image_name
+    cv2.imwrite(output_path, image)
+    print(f'Person has been removed, and the processed image has been saved in ./{output_path}')
+
+
+def use_lama(box, image, mask):
+    if is_gaussianblur == True:
+        mask_blurred = cv2.GaussianBlur(mask, (gaussian_radius, gaussian_radius), 0)
+        inpainted_image = simple_lama(image, mask_blurred)
+    else:
+        inpainted_image = simple_lama(image, mask)
+
+    if isinstance(inpainted_image, Image.Image):
+        inpainted_image = np.array(inpainted_image)  # make PIL.Image to numpy
+
+    inpainted_image_bgr = cv2.cvtColor(inpainted_image, cv2.COLOR_RGB2BGR) # RGB to BGR
+    imwrite(inpainted_image_bgr)
+
+    
+def use_opencv(box, image, mask):
+    if is_gaussianblur == True:
+        mask_blurred = cv2.GaussianBlur(mask, (gaussian_radius, gaussian_radius), 0)
+
+        inpainted_image = cv2.inpaint(image, mask_blurred, inpaintRadius=inpaint_radius, flags=cv2.INPAINT_TELEA)
+
+        mask_edges = cv2.Canny(mask_blurred, 100, 200)
+        inpainted_image = cv2.inpaint(inpainted_image, mask_edges, inpaintRadius=inpaint_radius, flags=cv2.INPAINT_TELEA)
+    else:
+        inpainted_image = cv2.inpaint(image, mask, inpaintRadius=inpaint_radius, flags=cv2.INPAINT_TELEA)
+
+    imwrite(inpainted_image)
+
+
+def inpaint_mask(box, image, mask):
+    if is_lama == True:
+        use_lama(box, image, mask)
+    else:
+        use_opencv(box, image, mask)
+
+
+def get_mask(box):
+    x1, y1, x2, y2 = map(int, box.xyxy[0])
+    mask = np.zeros(image.shape[:2], dtype=np.uint8)
+    mask[y1:y2, x1:x2] = 255
+    return mask
+
+
+def detect_person(image, box):
+    if model.names[int(box.cls)] == 'person':
+        mask = get_mask(box)
+        inpaint_mask(box, image, mask)
+
+
+def detect_results(results, image):
+    for result in results:
+        boxes = result.boxes
+
+        for box in boxes:
+            detect_person(image, box)
+
+
+if __name__ == '__main__':
+    image = cv2.imread('./images/' + image_name)
+    if is_lama == True:
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # SimpleLama need to RGB
+        results = model(image)
+    else:
+        results = model(image)
+
+    detect_results(results, image)
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diff --git a/schemeB.py b/schemeB.py
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+++ b/schemeB.py
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+import cv2
+import numpy as np
+from ultralytics import YOLO
+import conf
+
+
+image_name = conf.schemeB['image_name']
+gaussian_radius = conf.schemeB['gaussian_radius']
+is_use_fill_color = conf.schemeB['is_use_fill_color']
+fill_color_c = conf.schemeB['fill_color']
+
+model = YOLO('yolov5s.pt')
+
+
+def imwrite(image):
+    output_path = 'outputs/output_B_' + image_name
+    cv2.imwrite(output_path, image)
+
+    print(f'Person has been removed, and the processed image has been saved in ./{output_path}')
+
+
+def get_blended_image(image, blurred_mask):
+    fill_color = None
+    if is_use_fill_color:
+        fill_color = fill_color_c
+    else:
+        mean_color = np.mean(image, axis=0)
+        fill_color = mean_color
+
+    fill_image = np.zeros_like(image)
+    fill_image[:] = fill_color
+
+    blended_image = (image * (1 - blurred_mask[..., np.newaxis]) + 
+                    fill_image * blurred_mask[..., np.newaxis]).astype(np.uint8)
+
+    return blended_image
+
+
+def detect_person(image, box):
+    if model.names[int(box.cls)] == 'person':
+        x1, y1, x2, y2 = map(int, box.xyxy[0])
+
+        mask = np.zeros(image.shape[:2], dtype=np.uint8)
+        mask[y1:y2, x1:x2] = 255
+
+        rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # BGR to RGB
+
+        blurred_mask = cv2.GaussianBlur(mask, (gaussian_radius, gaussian_radius), 0)
+        blurred_mask = blurred_mask / 255.0
+        blended_image = get_blended_image(rgb_image, blurred_mask)
+
+        bgr_image = cv2.cvtColor(blended_image, cv2.COLOR_RGB2BGR) # RGB to BGR
+
+        imwrite(bgr_image)
+
+
+def detect_results(results, image):
+    for result in results:
+        boxes = result.boxes
+
+        for box in boxes:
+            detect_person(image, box)
+
+
+if __name__ == '__main__':
+    image = cv2.imread('./images/' + image_name)
+    results = model(image)
+
+    detect_results(results, image)
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diff --git a/yolov5su.pt b/yolov5su.pt
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