'all'

schemeA.py

@@ -0,0 +1,93 @@
+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)