'all'

schemeB.py

@@ -0,0 +1,69 @@
+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)