added aditional arguments to auto_blur_image and video

.gitignore

@@ -1,2 +1,3 @@
 .vscode
-*.pyc
+*.pyc
+.DS_Store

src/auto_blur_image.py

@@ -6,7 +6,7 @@
 from DetectorAPI import Detector
 
 
-def blurBoxes(image, boxes):
+def blurBoxes(image, boxes, blur_strength, extend_selection):
     """
     Argument:
     image -- the image that will be edited as a matrix
@@ -21,11 +21,18 @@ def blurBoxes(image, boxes):
         x1, y1 = box["x1"], box["y1"]
         x2, y2 = box["x2"], box["y2"]
 
+        height, width, _ = image.shape
+
+        x1 = max(0, x1 - extend_selection)
+        x2 = min(width, x2 + extend_selection)
+        y1 = max(0, y1 - extend_selection)
+        y2 = min(height, y2 + extend_selection)
+
         # crop the image due to the current box
         sub = image[y1:y2, x1:x2]
 
         # apply GaussianBlur on cropped area
-        blur = cv2.blur(sub, (25, 25))
+        blur = cv2.blur(sub, (blur_strength, blur_strength))
 
         # paste blurred image on the original image
         image[y1:y2, x1:x2] = blur
@@ -37,6 +44,8 @@ def main(args):
     # assign model path and threshold
     model_path = args.model_path
     threshold = args.threshold
+    blur_strength = args.blur_strength
+    extend_selection = args.extend_selection
 
     # create detection object
     detector = Detector(model_path=model_path, name="detection")
@@ -48,7 +57,7 @@ def main(args):
     faces = detector.detect_objects(image, threshold=threshold)
 
     # apply blurring
-    image = blurBoxes(image, faces)
+    image = blurBoxes(image, faces, blur_strength, extend_selection)
 
     # show image
     cv2.imshow('blurred', image)
@@ -89,6 +98,16 @@ def main(args):
                         help='Face detection confidence',
                         default=0.7,
                         type=float)
+    parser.add_argument('-s',
+                        '--blur_strength',
+                        help='Blur strength, default 25',
+                        default=25,
+                        type=int)
+    parser.add_argument('-e',
+                        '--extend_selection',
+                        help='Extend the selected area by x amount of pixels',
+                        default=0,
+                        type=int)
     args = parser.parse_args()
     print(args)
     # if input image path is invalid then stop

src/auto_blur_video.py

@@ -6,7 +6,7 @@
 from DetectorAPI import Detector
 
 
-def blurBoxes(image, boxes):
+def blurBoxes(image, boxes, blur_strength, extend_selection):
     """
     Argument:
     image -- the image that will be edited as a matrix
@@ -21,11 +21,18 @@ def blurBoxes(image, boxes):
         x1, y1 = box["x1"], box["y1"]
         x2, y2 = box["x2"], box["y2"]
 
+        height, width, _ = image.shape
+
+        x1 = max(0, x1 - extend_selection)
+        x2 = min(width, x2 + extend_selection)
+        y1 = max(0, y1 - extend_selection)
+        y2 = min(height, y2 + extend_selection)
+
         # crop the image due to the current box
         sub = image[y1:y2, x1:x2]
 
         # apply GaussianBlur on cropped area
-        blur = cv2.blur(sub, (25, 25))
+        blur = cv2.blur(sub, (blur_strength, blur_strength))
 
         # paste blurred image on the original image
         image[y1:y2, x1:x2] = blur
@@ -37,6 +44,8 @@ def main(args):
     # assign model path and threshold
     model_path = args.model_path
     threshold = args.threshold
+    blur_strength = args.blur_strength
+    extend_selection = args.extend_selection
 
     # create detection object
     detector = Detector(model_path=model_path, name="detection")
@@ -46,12 +55,14 @@ def main(args):
 
     # video width = capture.get(3)
     # video height = capture.get(4)
-    # video fps = capture.get(5)
+    fps = capture.get(5)
 
     if args.output_video:
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-        output = cv2.VideoWriter(args.output_video, fourcc,
-                                 20.0, (int(capture.get(3)), int(capture.get(4))))
+        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        output = cv2.VideoWriter(
+            args.output_video, fourcc, fps, (int(
+                capture.get(3)), int(capture.get(4)))
+        )
 
     frame_counter = 0
     while True:
@@ -70,12 +81,12 @@ def main(args):
         faces = detector.detect_objects(frame, threshold=threshold)
 
         # apply blurring
-        frame = blurBoxes(frame, faces)
+        frame = blurBoxes(frame, faces, blur_strength, extend_selection)
 
         # show image
         cv2.imshow('blurred', frame)
 
-    # if image will be saved then save it
+        # if image will be saved then save it
         if args.output_video:
             output.write(frame)
             print('Blurred video has been saved successfully at',
@@ -110,6 +121,16 @@ def main(args):
                         help='Face detection confidence',
                         default=0.7,
                         type=float)
+    parser.add_argument('-s',
+                        '--blur_strength',
+                        help='Blur strength, default 25',
+                        default=25,
+                        type=int)
+    parser.add_argument('-e',
+                        '--extend_selection',
+                        help='Extend the selected area by x amount of pixels',
+                        default=0,
+                        type=int)
     args = parser.parse_args()
 
     # if input image path is invalid then stop