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+ END OF TERMS AND CONDITIONS
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623
+ How to Apply These Terms to Your New Programs
624
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625
+ If you develop a new program, and you want it to be of the greatest
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660
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664
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+ For more information on this, and how to apply and follow the GNU GPL, see
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669
+ The GNU General Public License does not permit incorporating your program
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+ into proprietary programs. If your program is a subroutine library, you
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+ <https://www.gnu.org/licenses/why-not-lgpl.html>.
requirements.txt CHANGED
@@ -1,5 +1,5 @@
1
- ultralytics
2
- opencv-python
3
- numpy
4
- roboflow
5
  gradio
 
1
+ ultralytics
2
+ opencv-python
3
+ numpy
4
+ roboflow
5
  gradio
src/main.py CHANGED
@@ -1,61 +1,61 @@
1
- """ EXAMPLE CODE """
2
- # from models.face_detector import YOLOFaceDetector
3
- # from masking.text import TextCensor
4
- # from masking.emoji import EmojiCensor
5
- # from masking.blur import BlurCensor
6
-
7
- # from processors.stream_processor import LiveStreamProcessor
8
- # from processors.processor import MediaProcessor
9
-
10
- # def main():
11
- # load_dotenv()
12
-
13
- #Initialize detector
14
- # detector = YOLOFaceDetector()
15
-
16
- # Using Text Masking
17
- # text_censor = TextCensor(text="HELLO", # The text to draw on faces
18
- # draw_background=True, # Control whether to draw solid background behind text
19
- # background_color="black", # The color of the solid background
20
- # text_color="green", # The color of the text
21
- # font="arial.ttf", # The path to font to use, defaults to 'arial.ttf'
22
- # scale_factor=0.2 # The text size scaling factor
23
- # )
24
-
25
- # Using Emoji Masking
26
- # emoji_censor = EmojiCensor(
27
- # emoji="😁", # The emoji you want to use to mask faces
28
- # font="seguiemj.ttf", # The path to the emoji font file, by default uses "seguiemj.ttf"
29
- # scale_factor=1.0 # The emoji size scaling factor in percentage
30
- # )
31
-
32
- # Using blur masking
33
- # blur_censor = BlurCensor(
34
- # blur_factor=71 # The strength of the blur effect, defaults to 99
35
- # )
36
-
37
- # Create batch media processor, specifying masking method and detector.
38
- # processor = MediaProcessor(detector, blur_censor)
39
-
40
- # Process image
41
- # processor.process_image("assets/input.jpg", "assets/output_blur.jpg")
42
-
43
- # Process video
44
- # processor.process_video("assets/input.mp4", "assets/output.mp4")
45
-
46
- # Create live stream processor, specifying masking method and detector.
47
- #live_processor = LiveStreamProcessor(detector, blur_censor)
48
-
49
- # Process RTMP stream.
50
- #live_processor.process_stream(
51
- # source="rtmp://localhost/live/livestream", # I used https://github.com/ossrs/srs for testing
52
- # output_type="rtmp",
53
- # output_dest=f"rtmp://jfk.contribute.live-video.net/app/{os.getenv('STREAM_KEY')}" # Tested on twitch
54
- #)
55
-
56
- from ui import FaceCensorUI
57
-
58
- if __name__ == "__main__":
59
- ui = FaceCensorUI()
60
- interface = ui.create_ui()
61
  interface.launch(pwa=True)
 
1
+ """ EXAMPLE CODE """
2
+ # from models.face_detector import YOLOFaceDetector
3
+ # from masking.text import TextCensor
4
+ # from masking.emoji import EmojiCensor
5
+ # from masking.blur import BlurCensor
6
+
7
+ # from processors.stream_processor import LiveStreamProcessor
8
+ # from processors.processor import MediaProcessor
9
+
10
+ # def main():
11
+ # load_dotenv()
12
+
13
+ #Initialize detector
14
+ # detector = YOLOFaceDetector()
15
+
16
+ # Using Text Masking
17
+ # text_censor = TextCensor(text="HELLO", # The text to draw on faces
18
+ # draw_background=True, # Control whether to draw solid background behind text
19
+ # background_color="black", # The color of the solid background
20
+ # text_color="green", # The color of the text
21
+ # font="arial.ttf", # The path to font to use, defaults to 'arial.ttf'
22
+ # scale_factor=0.2 # The text size scaling factor
23
+ # )
24
+
25
+ # Using Emoji Masking
26
+ # emoji_censor = EmojiCensor(
27
+ # emoji="😁", # The emoji you want to use to mask faces
28
+ # font="seguiemj.ttf", # The path to the emoji font file, by default uses "seguiemj.ttf"
29
+ # scale_factor=1.0 # The emoji size scaling factor in percentage
30
+ # )
31
+
32
+ # Using blur masking
33
+ # blur_censor = BlurCensor(
34
+ # blur_factor=71 # The strength of the blur effect, defaults to 99
35
+ # )
36
+
37
+ # Create batch media processor, specifying masking method and detector.
38
+ # processor = MediaProcessor(detector, blur_censor)
39
+
40
+ # Process image
41
+ # processor.process_image("assets/input.jpg", "assets/output_blur.jpg")
42
+
43
+ # Process video
44
+ # processor.process_video("assets/input.mp4", "assets/output.mp4")
45
+
46
+ # Create live stream processor, specifying masking method and detector.
47
+ #live_processor = LiveStreamProcessor(detector, blur_censor)
48
+
49
+ # Process RTMP stream.
50
+ #live_processor.process_stream(
51
+ # source="rtmp://localhost/live/livestream", # I used https://github.com/ossrs/srs for testing
52
+ # output_type="rtmp",
53
+ # output_dest=f"rtmp://jfk.contribute.live-video.net/app/{os.getenv('STREAM_KEY')}" # Tested on twitch
54
+ #)
55
+
56
+ from ui import FaceCensorUI
57
+
58
+ if __name__ == "__main__":
59
+ ui = FaceCensorUI()
60
+ interface = ui.create_ui()
61
  interface.launch(pwa=True)
src/masking/base.py CHANGED
@@ -1,6 +1,6 @@
1
- from abc import ABC, abstractmethod
2
-
3
- class CensoringMethod(ABC):
4
- @abstractmethod
5
- def apply(self, frame, bbox):
6
  pass
 
1
+ from abc import ABC, abstractmethod
2
+
3
+ class CensoringMethod(ABC):
4
+ @abstractmethod
5
+ def apply(self, frame, bbox):
6
  pass
src/masking/blur.py CHANGED
@@ -1,18 +1,18 @@
1
- import cv2
2
- from .base import CensoringMethod
3
-
4
- class BlurCensor(CensoringMethod):
5
- def __init__(self, blur_factor=99):
6
- self.blur_factor = blur_factor
7
-
8
- if self.blur_factor < 1 or self.blur_factor % 2 == 0:
9
- raise ValueError("blur_factor must be a positive and odd number.")
10
-
11
- def apply(self, frame, bbox):
12
- x1, y1, x2, y2 = bbox[:4]
13
- roi = frame[y1:y2, x1:x2]
14
-
15
- blurred = cv2.GaussianBlur(roi, (self.blur_factor, self.blur_factor), 0)
16
- frame[y1:y2, x1:x2] = blurred
17
- return frame
18
 
 
1
+ import cv2
2
+ from .base import CensoringMethod
3
+
4
+ class BlurCensor(CensoringMethod):
5
+ def __init__(self, blur_factor=99):
6
+ self.blur_factor = blur_factor
7
+
8
+ if self.blur_factor < 1 or self.blur_factor % 2 == 0:
9
+ raise ValueError("blur_factor must be a positive and odd number.")
10
+
11
+ def apply(self, frame, bbox):
12
+ x1, y1, x2, y2 = bbox[:4]
13
+ roi = frame[y1:y2, x1:x2]
14
+
15
+ blurred = cv2.GaussianBlur(roi, (self.blur_factor, self.blur_factor), 0)
16
+ frame[y1:y2, x1:x2] = blurred
17
+ return frame
18
 
src/masking/emoji.py CHANGED
@@ -1,25 +1,25 @@
1
- import cv2
2
- from .text import TextCensor
3
- from PIL import Image, ImageDraw, ImageFont
4
- import numpy as np
5
-
6
- class EmojiCensor(TextCensor):
7
- def __init__(self, emoji, font="seguiemj.ttf", scale_factor=1.0):
8
- super().__init__(
9
- text=emoji,
10
- font=font,
11
- scale_factor=scale_factor,
12
- text_color=None,
13
- draw_background=False
14
- )
15
-
16
- def draw_overlay(self, draw, x, y, font):
17
- draw.text(
18
- xy=(x, y),
19
- text=self.text,
20
- anchor="mm",
21
- font=font,
22
- embedded_color=True
23
- )
24
-
25
-
 
1
+ import cv2
2
+ from .text import TextCensor
3
+ from PIL import Image, ImageDraw, ImageFont
4
+ import numpy as np
5
+
6
+ class EmojiCensor(TextCensor):
7
+ def __init__(self, emoji, font="seguiemj.ttf", scale_factor=1.0):
8
+ super().__init__(
9
+ text=emoji,
10
+ font=font,
11
+ scale_factor=scale_factor,
12
+ text_color=None,
13
+ draw_background=False
14
+ )
15
+
16
+ def draw_overlay(self, draw, x, y, font):
17
+ draw.text(
18
+ xy=(x, y),
19
+ text=self.text,
20
+ anchor="mm",
21
+ font=font,
22
+ embedded_color=True
23
+ )
24
+
25
+
src/masking/text.py CHANGED
@@ -1,53 +1,53 @@
1
- from .base import CensoringMethod
2
- from PIL import Image, ImageDraw, ImageFont
3
- import numpy as np
4
- import cv2
5
-
6
- class TextCensor(CensoringMethod):
7
- def __init__(self,
8
- text,
9
- font="arial.ttf",
10
- scale_factor=0.5,
11
- text_color="white",
12
- background_color=None,
13
- draw_background=True):
14
-
15
- self.text = text
16
- self.font_path = font
17
- self.scale_factor = scale_factor
18
- self.text_color = text_color
19
- self.background_color = background_color
20
- self.draw_background = draw_background
21
-
22
- def apply(self, frame, bbox):
23
- x1, y1, x2, y2 = bbox[:4]
24
- center_x = (x1 + x2) // 2
25
- center_y = (y1 + y2) // 2
26
-
27
- font_size = int(max(x2-x1, y2-y1) * self.scale_factor)
28
-
29
- try:
30
- font = ImageFont.truetype(self.font_path, font_size)
31
- except OSError as e:
32
- raise ValueError(f"Could not open file at {self.font_path}")
33
-
34
- rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
35
- pil_frame = Image.fromarray(rgb_frame)
36
- draw = ImageDraw.Draw(pil_frame)
37
-
38
- if self.draw_background and self.background_color:
39
- draw.rectangle([x1, y1, x2, y2], fill=self.background_color)
40
-
41
- self.draw_overlay(draw, center_x, center_y, font)
42
-
43
- out_image = cv2.cvtColor(np.array(pil_frame), cv2.COLOR_RGB2BGR)
44
- return out_image
45
-
46
- def draw_overlay(self, draw, x, y, font):
47
- draw.text(
48
- xy=(x, y),
49
- text=self.text,
50
- anchor="mm",
51
- font=font,
52
- fill=self.text_color
53
  )
 
1
+ from .base import CensoringMethod
2
+ from PIL import Image, ImageDraw, ImageFont
3
+ import numpy as np
4
+ import cv2
5
+
6
+ class TextCensor(CensoringMethod):
7
+ def __init__(self,
8
+ text,
9
+ font="arial.ttf",
10
+ scale_factor=0.5,
11
+ text_color="white",
12
+ background_color=None,
13
+ draw_background=True):
14
+
15
+ self.text = text
16
+ self.font_path = font
17
+ self.scale_factor = scale_factor
18
+ self.text_color = text_color
19
+ self.background_color = background_color
20
+ self.draw_background = draw_background
21
+
22
+ def apply(self, frame, bbox):
23
+ x1, y1, x2, y2 = bbox[:4]
24
+ center_x = (x1 + x2) // 2
25
+ center_y = (y1 + y2) // 2
26
+
27
+ font_size = int(max(x2-x1, y2-y1) * self.scale_factor)
28
+
29
+ try:
30
+ font = ImageFont.truetype(self.font_path, font_size)
31
+ except OSError as e:
32
+ raise ValueError(f"Could not open file at {self.font_path}")
33
+
34
+ rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
35
+ pil_frame = Image.fromarray(rgb_frame)
36
+ draw = ImageDraw.Draw(pil_frame)
37
+
38
+ if self.draw_background and self.background_color:
39
+ draw.rectangle([x1, y1, x2, y2], fill=self.background_color)
40
+
41
+ self.draw_overlay(draw, center_x, center_y, font)
42
+
43
+ out_image = cv2.cvtColor(np.array(pil_frame), cv2.COLOR_RGB2BGR)
44
+ return out_image
45
+
46
+ def draw_overlay(self, draw, x, y, font):
47
+ draw.text(
48
+ xy=(x, y),
49
+ text=self.text,
50
+ anchor="mm",
51
+ font=font,
52
+ fill=self.text_color
53
  )
src/models/base.py CHANGED
@@ -1,6 +1,6 @@
1
- from abc import ABC, abstractmethod
2
-
3
- class DetectionModel(ABC):
4
- @abstractmethod
5
- def detect(self, frame):
6
  pass
 
1
+ from abc import ABC, abstractmethod
2
+
3
+ class DetectionModel(ABC):
4
+ @abstractmethod
5
+ def detect(self, frame):
6
  pass
src/models/face_detector.py CHANGED
@@ -1,16 +1,16 @@
1
- from ultralytics import YOLO
2
- from .base import DetectionModel
3
-
4
- class YOLOFaceDetector(DetectionModel):
5
- def __init__(self, model_path="src/data/models/face_detection_model.pt"):
6
- self.model = YOLO(model_path)
7
-
8
- def detect(self, frame):
9
- results = self.model(frame, verbose=False)[0]
10
- detections = []
11
-
12
- for box in results.boxes:
13
- x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
14
- conf = box.conf[0].cpu().numpy()
15
- detections.append([int(x1), int(y1), int(x2), int(y2), conf])
16
  return detections
 
1
+ from ultralytics import YOLO
2
+ from .base import DetectionModel
3
+
4
+ class YOLOFaceDetector(DetectionModel):
5
+ def __init__(self, model_path="src/data/models/face_detection_model.pt"):
6
+ self.model = YOLO(model_path)
7
+
8
+ def detect(self, frame):
9
+ results = self.model(frame, verbose=False)[0]
10
+ detections = []
11
+
12
+ for box in results.boxes:
13
+ x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
14
+ conf = box.conf[0].cpu().numpy()
15
+ detections.append([int(x1), int(y1), int(x2), int(y2), conf])
16
  return detections
src/processors/processor.py CHANGED
@@ -1,50 +1,50 @@
1
- import cv2
2
-
3
- class MediaProcessor:
4
- def __init__(self, detector, censor):
5
- self.detector = detector
6
- self.censor = censor
7
-
8
- def process_image(self, image_path, output_path, conf_thresh = 0.6):
9
- image = cv2.imread(image_path)
10
- if image is None:
11
- raise ValueError(f"Could not read image at {image_path}")
12
-
13
- detections = self.detector.detect(image)
14
-
15
- for bbox in detections:
16
- if bbox[4] >= conf_thresh:
17
- image = self.censor.apply(image, bbox)
18
-
19
- cv2.imwrite(output_path, image)
20
- return output_path
21
-
22
- def process_video(self, video_path, output_path, conf_thresh = 0.6):
23
- capture = cv2.VideoCapture(video_path)
24
- if not capture.isOpened():
25
- raise ValueError(f"Could not open video at {video_path}")
26
-
27
- fps = int(capture.get(cv2.CAP_PROP_FPS))
28
- width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
29
- height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
30
-
31
- fourcc = cv2.VideoWriter_fourcc(*"mp4v")
32
- out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
33
-
34
- try:
35
- while capture.isOpened():
36
- ret, frame = capture.read()
37
- if not ret: break
38
-
39
- detections = self.detector.detect(frame)
40
-
41
- for bbox in detections:
42
- if bbox[4] >= conf_thresh:
43
- frame = self.censor.apply(frame, bbox)
44
-
45
- out.write(frame)
46
- finally:
47
- capture.release()
48
- out.release()
49
-
50
  return output_path
 
1
+ import cv2
2
+
3
+ class MediaProcessor:
4
+ def __init__(self, detector, censor):
5
+ self.detector = detector
6
+ self.censor = censor
7
+
8
+ def process_image(self, image_path, output_path, conf_thresh = 0.6):
9
+ image = cv2.imread(image_path)
10
+ if image is None:
11
+ raise ValueError(f"Could not read image at {image_path}")
12
+
13
+ detections = self.detector.detect(image)
14
+
15
+ for bbox in detections:
16
+ if bbox[4] >= conf_thresh:
17
+ image = self.censor.apply(image, bbox)
18
+
19
+ cv2.imwrite(output_path, image)
20
+ return output_path
21
+
22
+ def process_video(self, video_path, output_path, conf_thresh = 0.6):
23
+ capture = cv2.VideoCapture(video_path)
24
+ if not capture.isOpened():
25
+ raise ValueError(f"Could not open video at {video_path}")
26
+
27
+ fps = int(capture.get(cv2.CAP_PROP_FPS))
28
+ width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
29
+ height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
30
+
31
+ fourcc = cv2.VideoWriter_fourcc(*"mp4v")
32
+ out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
33
+
34
+ try:
35
+ while capture.isOpened():
36
+ ret, frame = capture.read()
37
+ if not ret: break
38
+
39
+ detections = self.detector.detect(frame)
40
+
41
+ for bbox in detections:
42
+ if bbox[4] >= conf_thresh:
43
+ frame = self.censor.apply(frame, bbox)
44
+
45
+ out.write(frame)
46
+ finally:
47
+ capture.release()
48
+ out.release()
49
+
50
  return output_path
src/processors/stream_processor.py CHANGED
@@ -1,81 +1,81 @@
1
- import cv2
2
- from .processor import MediaProcessor
3
- import subprocess
4
-
5
- class LiveStreamProcessor:
6
- def __init__(self, detector, censor):
7
- self.detector = detector
8
- self.censor = censor
9
- self.processor = MediaProcessor(detector, censor)
10
-
11
- def process_stream(self,
12
- source,
13
- output_type=None,
14
- output_dest=None,
15
- conf_thresh=0.4,
16
- preview_stream=False):
17
-
18
- capture = cv2.VideoCapture(source)
19
- if not capture.isOpened():
20
- raise ValueError(f"Could not open video source {source}")
21
-
22
- width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
23
- height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
24
- fps = int(capture.get(cv2.CAP_PROP_FPS))
25
-
26
- output = self.initialize_output(output_type, output_dest, width, height, fps)
27
-
28
- try:
29
- while True:
30
- ret, frame = capture.read()
31
- if not ret:
32
- break
33
-
34
- detections = self.detector.detect(frame)
35
- for bbox in detections:
36
- if bbox[4] >= conf_thresh:
37
- frame = self.censor.apply(frame, bbox)
38
-
39
- if output_type == "rtmp":
40
- output.stdin.write(frame.tobytes())
41
- else:
42
- raise ValueError(f"Invalid output_type: {output_type}, try 'rtmp'?")
43
-
44
- if preview_stream:
45
- cv2.imshow("Stream Preview", frame)
46
- if cv2.waitKey(1) & 0xFF == ord("q"):
47
- break
48
-
49
- finally:
50
- capture.release()
51
- if output:
52
- self.cleanup_output(output, output_type)
53
- cv2.destroyAllWindows()
54
-
55
- def initialize_output(self, output_type, output_dest, width, height, fps):
56
- if output_dest == None:
57
- raise ValueError("output_dest cannot be None, provide valid output destination.")
58
-
59
- if output_type == "rtmp":
60
- command = [
61
- "ffmpeg",
62
- "-y", # Overwrite output files
63
- "-f", "rawvideo",
64
- "-vcodec", "rawvideo",
65
- "-pix_fmt", "bgr24",
66
- "-s", f"{width}x{height}",
67
- "-r", str(fps),
68
- "-i", "-", # Input from pipe
69
- "-c:v", "libx264",
70
- "-pix_fmt", "yuv420p",
71
- "-preset", "ultrafast",
72
- "-f", "flv",
73
- output_dest # RTMP URL
74
- ]
75
- return subprocess.Popen(command, stdin=subprocess.PIPE)
76
- return None
77
-
78
- def cleanup_output(self, output, output_type):
79
- if output_type == "rtmp":
80
- output.stdin.close()
81
- output.wait()
 
1
+ import cv2
2
+ from .processor import MediaProcessor
3
+ import subprocess
4
+
5
+ class LiveStreamProcessor:
6
+ def __init__(self, detector, censor):
7
+ self.detector = detector
8
+ self.censor = censor
9
+ self.processor = MediaProcessor(detector, censor)
10
+
11
+ def process_stream(self,
12
+ source,
13
+ output_type=None,
14
+ output_dest=None,
15
+ conf_thresh=0.4,
16
+ preview_stream=False):
17
+
18
+ capture = cv2.VideoCapture(source)
19
+ if not capture.isOpened():
20
+ raise ValueError(f"Could not open video source {source}")
21
+
22
+ width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
23
+ height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
24
+ fps = int(capture.get(cv2.CAP_PROP_FPS))
25
+
26
+ output = self.initialize_output(output_type, output_dest, width, height, fps)
27
+
28
+ try:
29
+ while True:
30
+ ret, frame = capture.read()
31
+ if not ret:
32
+ break
33
+
34
+ detections = self.detector.detect(frame)
35
+ for bbox in detections:
36
+ if bbox[4] >= conf_thresh:
37
+ frame = self.censor.apply(frame, bbox)
38
+
39
+ if output_type == "rtmp":
40
+ output.stdin.write(frame.tobytes())
41
+ else:
42
+ raise ValueError(f"Invalid output_type: {output_type}, try 'rtmp'?")
43
+
44
+ if preview_stream:
45
+ cv2.imshow("Stream Preview", frame)
46
+ if cv2.waitKey(1) & 0xFF == ord("q"):
47
+ break
48
+
49
+ finally:
50
+ capture.release()
51
+ if output:
52
+ self.cleanup_output(output, output_type)
53
+ cv2.destroyAllWindows()
54
+
55
+ def initialize_output(self, output_type, output_dest, width, height, fps):
56
+ if output_dest == None:
57
+ raise ValueError("output_dest cannot be None, provide valid output destination.")
58
+
59
+ if output_type == "rtmp":
60
+ command = [
61
+ "ffmpeg",
62
+ "-y", # Overwrite output files
63
+ "-f", "rawvideo",
64
+ "-vcodec", "rawvideo",
65
+ "-pix_fmt", "bgr24",
66
+ "-s", f"{width}x{height}",
67
+ "-r", str(fps),
68
+ "-i", "-", # Input from pipe
69
+ "-c:v", "libx264",
70
+ "-pix_fmt", "yuv420p",
71
+ "-preset", "ultrafast",
72
+ "-f", "flv",
73
+ output_dest # RTMP URL
74
+ ]
75
+ return subprocess.Popen(command, stdin=subprocess.PIPE)
76
+ return None
77
+
78
+ def cleanup_output(self, output, output_type):
79
+ if output_type == "rtmp":
80
+ output.stdin.close()
81
+ output.wait()
src/training/train.py CHANGED
@@ -1,71 +1,71 @@
1
- from ultralytics import YOLO
2
- from roboflow import Roboflow
3
- import logging
4
- from pathlib import Path
5
- import os
6
- from dotenv import load_dotenv
7
-
8
- class ModelTrainer:
9
- def __init__(self, roboflow_api_key):
10
- self.rf = Roboflow(api_key=roboflow_api_key)
11
- self.setup_logging()
12
-
13
- def setup_logging(self):
14
- logging.basicConfig(
15
- level=logging.INFO,
16
- format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
17
- )
18
- self.logger = logging.getLogger(__name__)
19
-
20
- def download_dataset(self, project_name, project_version, workspace):
21
- self.logger.info(f"Downloading dataset: {project_name} v{project_version}")
22
- project = self.rf.workspace(workspace).project(project_name)
23
- dataset = project.version(project_version).download("yolov8", "data\\raw")
24
- print(dataset.location)
25
- return dataset.location
26
-
27
- def train(
28
- self,
29
- data_yaml,
30
- model_type = "yolov8n.pt",
31
- epochs = 30,
32
- imgsz = 640,
33
- batch_size = 16,
34
- device = "0"
35
- ):
36
- self.logger.info("Starting training process")
37
-
38
- model = YOLO(model_type)
39
-
40
- results = model.train(
41
- data = data_yaml,
42
- epochs = epochs,
43
- imgsz = imgsz,
44
- batch = batch_size,
45
- device = device,
46
- project = "runs/train",
47
- name = "face_detection"
48
- )
49
-
50
- self.logger.info("Training Complete")
51
- return results
52
-
53
- def main():
54
- load_dotenv()
55
- trainer = ModelTrainer(os.getenv("ROBOFLOW_API_KEY"))
56
-
57
- data_path = trainer.download_dataset(
58
- workspace="large-benchmark-datasets",
59
- project_name="wider-face-ndtcz",
60
- project_version=1
61
- )
62
-
63
- trainer.train(
64
- data_yaml=f"{data_path}\\data.yaml",
65
- epochs=20,
66
- batch_size=16,
67
- device="0"
68
- )
69
-
70
- if __name__ == "__main__":
71
  main()
 
1
+ from ultralytics import YOLO
2
+ from roboflow import Roboflow
3
+ import logging
4
+ from pathlib import Path
5
+ import os
6
+ from dotenv import load_dotenv
7
+
8
+ class ModelTrainer:
9
+ def __init__(self, roboflow_api_key):
10
+ self.rf = Roboflow(api_key=roboflow_api_key)
11
+ self.setup_logging()
12
+
13
+ def setup_logging(self):
14
+ logging.basicConfig(
15
+ level=logging.INFO,
16
+ format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
17
+ )
18
+ self.logger = logging.getLogger(__name__)
19
+
20
+ def download_dataset(self, project_name, project_version, workspace):
21
+ self.logger.info(f"Downloading dataset: {project_name} v{project_version}")
22
+ project = self.rf.workspace(workspace).project(project_name)
23
+ dataset = project.version(project_version).download("yolov8", "data\\raw")
24
+ print(dataset.location)
25
+ return dataset.location
26
+
27
+ def train(
28
+ self,
29
+ data_yaml,
30
+ model_type = "yolov8n.pt",
31
+ epochs = 30,
32
+ imgsz = 640,
33
+ batch_size = 16,
34
+ device = "0"
35
+ ):
36
+ self.logger.info("Starting training process")
37
+
38
+ model = YOLO(model_type)
39
+
40
+ results = model.train(
41
+ data = data_yaml,
42
+ epochs = epochs,
43
+ imgsz = imgsz,
44
+ batch = batch_size,
45
+ device = device,
46
+ project = "runs/train",
47
+ name = "face_detection"
48
+ )
49
+
50
+ self.logger.info("Training Complete")
51
+ return results
52
+
53
+ def main():
54
+ load_dotenv()
55
+ trainer = ModelTrainer(os.getenv("ROBOFLOW_API_KEY"))
56
+
57
+ data_path = trainer.download_dataset(
58
+ workspace="large-benchmark-datasets",
59
+ project_name="wider-face-ndtcz",
60
+ project_version=1
61
+ )
62
+
63
+ trainer.train(
64
+ data_yaml=f"{data_path}\\data.yaml",
65
+ epochs=20,
66
+ batch_size=16,
67
+ device="0"
68
+ )
69
+
70
+ if __name__ == "__main__":
71
  main()
src/ui.py CHANGED
@@ -1,248 +1,248 @@
1
- from dataclasses import dataclass
2
- from typing import Optional
3
- from pathlib import Path
4
- import tempfile
5
- import gradio as gr
6
-
7
- from models.face_detector import YOLOFaceDetector
8
- from masking.text import TextCensor
9
- from masking.emoji import EmojiCensor
10
- from masking.blur import BlurCensor
11
- from processors.processor import MediaProcessor
12
-
13
- @dataclass
14
- class CensorSettings:
15
- type: Optional[str] = None
16
- # Blur settings
17
- blur_factor: Optional[int] = None
18
- # Emoji settings
19
- emoji: Optional[str] = None
20
- emoji_scale: Optional[float] = None
21
- # Text settings
22
- text: Optional[str] = None
23
- text_scale: Optional[float] = None
24
- draw_background: Optional[bool] = None
25
- background_color: Optional[str] = None
26
- text_color: Optional[str] = None
27
- font: Optional[str] = None
28
-
29
-
30
- class FaceCensorUI:
31
- CENSOR_TYPES = ["Blur", "Text", "Emoji"]
32
- FONTS = ["arial.ttf", "times.ttf"]
33
- DEFAULT_SETTINGS = {
34
- "blur_factor": 99,
35
- "emoji": "😁",
36
- "emoji_scale": 1.0,
37
- "text": "Hello",
38
- "text_scale": 0.2,
39
- "draw_background": True,
40
- "background_color": "#000000",
41
- "text_color": "#00FF00",
42
- "font": "arial.ttf"
43
- }
44
-
45
- def __init__(self):
46
- self.detector = YOLOFaceDetector()
47
-
48
- def create_censor(self, settings):
49
- # Map censor name to censor object
50
- censor_map = {
51
- "Blur": lambda: BlurCensor(blur_factor=settings.blur_factor),
52
- "Emoji": lambda: EmojiCensor(
53
- emoji=settings.emoji,
54
- scale_factor=settings.emoji_scale,
55
- font="seguiemj.ttf"
56
- ),
57
- "Text": lambda: TextCensor(
58
- text=settings.text,
59
- scale_factor=settings.text_scale,
60
- draw_background=settings.draw_background,
61
- background_color=settings.background_color,
62
- text_color=settings.text_color,
63
- font=settings.font or "arial.ttf"
64
- )
65
- }
66
-
67
- if settings.type not in censor_map:
68
- raise ValueError(f"Unknown censor type: {settings.type}")
69
-
70
- return censor_map[settings.type]()
71
-
72
- def process_file(self, input_media, settings_dict):
73
- try:
74
- settings = CensorSettings(**settings_dict)
75
- censor = self.create_censor(settings)
76
- processor = MediaProcessor(self.detector, censor)
77
-
78
- temp_dir = Path(tempfile.mkdtemp())
79
- input_path = Path(input_media.name)
80
-
81
- if input_path.suffix.lower() in ('.mp4', '.avi', '.mov'):
82
- output_path = temp_dir / "output.mp4"
83
- processor.process_video(str(input_path), str(output_path))
84
- else:
85
- output_path = temp_dir / "output.jpg"
86
- processor.process_image(str(input_path), str(output_path))
87
-
88
- return str(output_path)
89
-
90
- except Exception as e:
91
- return f"Error processing media: {str(e)}"
92
-
93
- def create_ui(self):
94
- with gr.Blocks(title="Face Censor") as ui:
95
- gr.Markdown("# Censor Faces Automatically!")
96
-
97
- with gr.Tab("File Processing"):
98
- self._create_media_section()
99
- self._create_settings_section()
100
-
101
- with gr.Row():
102
- gr.Markdown(
103
- """
104
- ## [View source code on GitHub](https://github.com/Spring-0/face-censor)
105
- *⭐ the repo to show support*
106
- """
107
- )
108
-
109
- return ui
110
-
111
- def _create_media_section(self):
112
- with gr.Row():
113
- self.input_media = gr.File(label="Upload Image/Video")
114
- self.output_media = gr.File(label="Processed Output")
115
-
116
- def _create_settings_section(self):
117
- self.censor_type = gr.Dropdown(
118
- choices=self.CENSOR_TYPES,
119
- value=self.CENSOR_TYPES[0],
120
- label="Censoring Method"
121
- )
122
-
123
- self.blur_settings = self._create_blur_settings()
124
- self.emoji_settings = self._create_emoji_settings()
125
- self.text_settings = self._create_text_settings()
126
-
127
- self.process_btn = gr.Button("Process")
128
-
129
- self.censor_type.change(
130
- self._update_settings_visibility,
131
- inputs=[self.censor_type],
132
- outputs=[self.blur_settings, self.emoji_settings, self.text_settings]
133
- )
134
-
135
- self.process_btn.click(
136
- self._handle_processing,
137
- inputs=self._get_all_inputs(),
138
- outputs=[self.output_media]
139
- )
140
-
141
- def _create_blur_settings(self):
142
- with gr.Row(visible=True) as section:
143
- self.blur_factor = gr.Slider(
144
- minimum=1,
145
- maximum=199,
146
- value=self.DEFAULT_SETTINGS["blur_factor"],
147
- step=2,
148
- label="Blur Factor"
149
- )
150
-
151
- return section
152
-
153
- def _create_emoji_settings(self):
154
- with gr.Row(visible=False) as section:
155
- self.emoji = gr.Textbox(
156
- value=self.DEFAULT_SETTINGS["emoji"],
157
- label="Emoji"
158
- )
159
-
160
- self.emoji_scale = gr.Slider(
161
- minimum=0.1,
162
- maximum=2.0,
163
- value=self.DEFAULT_SETTINGS["emoji_scale"],
164
- step=0.1,
165
- label="Emoji Scale Factor"
166
- )
167
-
168
- return section
169
-
170
- def _create_text_settings(self):
171
- with gr.Column(visible=False) as section:
172
- with gr.Row(equal_height=True):
173
- self.text = gr.Textbox(
174
- value=self.DEFAULT_SETTINGS["text"],
175
- label="Text"
176
- )
177
-
178
- self.text_scale = gr.Slider(
179
- minimum=0.1,
180
- maximum=2.0,
181
- value=self.DEFAULT_SETTINGS["text_scale"],
182
- step=0.1,
183
- label="Text Scale Factor"
184
- )
185
-
186
- with gr.Row(equal_height=True):
187
- self.draw_background = gr.Checkbox(
188
- value=self.DEFAULT_SETTINGS["draw_background"],
189
- label="Draw Background",
190
- )
191
-
192
- self.background_color = gr.ColorPicker(
193
- value=self.DEFAULT_SETTINGS["background_color"],
194
- label="Background Color"
195
- )
196
-
197
- with gr.Row(equal_height=True):
198
- self.text_color = gr.ColorPicker(
199
- value=self.DEFAULT_SETTINGS["text_color"],
200
- label="Text Color"
201
- )
202
-
203
- self.font = gr.Dropdown(
204
- choices=self.FONTS,
205
- value=self.DEFAULT_SETTINGS["font"],
206
- label="Font"
207
- )
208
-
209
- return section
210
-
211
- def _update_settings_visibility(self, censor_choice):
212
- return [
213
- gr.Row(visible=censor_choice=="Blur"),
214
- gr.Row(visible=censor_choice=="Emoji"),
215
- gr.Row(visible=censor_choice=="Text")
216
- ]
217
-
218
- def _get_all_inputs(self):
219
- return [
220
- self.input_media,
221
- self.censor_type,
222
- self.blur_factor,
223
- self.emoji,
224
- self.emoji_scale,
225
- self.text,
226
- self.text_scale,
227
- self.draw_background,
228
- self.background_color,
229
- self.text_color,
230
- self.font
231
- ]
232
-
233
- def _handle_processing(self, *args):
234
- input_media = args[0]
235
- settings_dict = {
236
- "type": args[1],
237
- "blur_factor": args[2],
238
- "emoji": args[3],
239
- "emoji_scale": args[4],
240
- "text": args[5],
241
- "text_scale": args[6],
242
- "draw_background": args[7],
243
- "background_color": args[8],
244
- "text_color": args[9],
245
- "font": args[10]
246
- }
247
-
248
- return self.process_file(input_media, settings_dict)
 
1
+ from dataclasses import dataclass
2
+ from typing import Optional
3
+ from pathlib import Path
4
+ import tempfile
5
+ import gradio as gr
6
+
7
+ from models.face_detector import YOLOFaceDetector
8
+ from masking.text import TextCensor
9
+ from masking.emoji import EmojiCensor
10
+ from masking.blur import BlurCensor
11
+ from processors.processor import MediaProcessor
12
+
13
+ @dataclass
14
+ class CensorSettings:
15
+ type: Optional[str] = None
16
+ # Blur settings
17
+ blur_factor: Optional[int] = None
18
+ # Emoji settings
19
+ emoji: Optional[str] = None
20
+ emoji_scale: Optional[float] = None
21
+ # Text settings
22
+ text: Optional[str] = None
23
+ text_scale: Optional[float] = None
24
+ draw_background: Optional[bool] = None
25
+ background_color: Optional[str] = None
26
+ text_color: Optional[str] = None
27
+ font: Optional[str] = None
28
+
29
+
30
+ class FaceCensorUI:
31
+ CENSOR_TYPES = ["Blur", "Text", "Emoji"]
32
+ FONTS = ["arial.ttf", "times.ttf"]
33
+ DEFAULT_SETTINGS = {
34
+ "blur_factor": 99,
35
+ "emoji": "😁",
36
+ "emoji_scale": 1.0,
37
+ "text": "Hello",
38
+ "text_scale": 0.2,
39
+ "draw_background": True,
40
+ "background_color": "#000000",
41
+ "text_color": "#00FF00",
42
+ "font": "arial.ttf"
43
+ }
44
+
45
+ def __init__(self):
46
+ self.detector = YOLOFaceDetector()
47
+
48
+ def create_censor(self, settings):
49
+ # Map censor name to censor object
50
+ censor_map = {
51
+ "Blur": lambda: BlurCensor(blur_factor=settings.blur_factor),
52
+ "Emoji": lambda: EmojiCensor(
53
+ emoji=settings.emoji,
54
+ scale_factor=settings.emoji_scale,
55
+ font="seguiemj.ttf"
56
+ ),
57
+ "Text": lambda: TextCensor(
58
+ text=settings.text,
59
+ scale_factor=settings.text_scale,
60
+ draw_background=settings.draw_background,
61
+ background_color=settings.background_color,
62
+ text_color=settings.text_color,
63
+ font=settings.font or "arial.ttf"
64
+ )
65
+ }
66
+
67
+ if settings.type not in censor_map:
68
+ raise ValueError(f"Unknown censor type: {settings.type}")
69
+
70
+ return censor_map[settings.type]()
71
+
72
+ def process_file(self, input_media, settings_dict):
73
+ try:
74
+ settings = CensorSettings(**settings_dict)
75
+ censor = self.create_censor(settings)
76
+ processor = MediaProcessor(self.detector, censor)
77
+
78
+ temp_dir = Path(tempfile.mkdtemp())
79
+ input_path = Path(input_media.name)
80
+
81
+ if input_path.suffix.lower() in ('.mp4', '.avi', '.mov'):
82
+ output_path = temp_dir / "output.mp4"
83
+ processor.process_video(str(input_path), str(output_path))
84
+ else:
85
+ output_path = temp_dir / "output.jpg"
86
+ processor.process_image(str(input_path), str(output_path))
87
+
88
+ return str(output_path)
89
+
90
+ except Exception as e:
91
+ return f"Error processing media: {str(e)}"
92
+
93
+ def create_ui(self):
94
+ with gr.Blocks(title="Face Censor") as ui:
95
+ gr.Markdown("# Censor Faces Automatically!")
96
+
97
+ with gr.Tab("File Processing"):
98
+ self._create_media_section()
99
+ self._create_settings_section()
100
+
101
+ with gr.Row():
102
+ gr.Markdown(
103
+ """
104
+ ## [View source code on GitHub](https://github.com/Spring-0/face-censor)
105
+ *⭐ the repo to show support*
106
+ """
107
+ )
108
+
109
+ return ui
110
+
111
+ def _create_media_section(self):
112
+ with gr.Row():
113
+ self.input_media = gr.File(label="Upload Image/Video")
114
+ self.output_media = gr.File(label="Processed Output")
115
+
116
+ def _create_settings_section(self):
117
+ self.censor_type = gr.Dropdown(
118
+ choices=self.CENSOR_TYPES,
119
+ value=self.CENSOR_TYPES[0],
120
+ label="Censoring Method"
121
+ )
122
+
123
+ self.blur_settings = self._create_blur_settings()
124
+ self.emoji_settings = self._create_emoji_settings()
125
+ self.text_settings = self._create_text_settings()
126
+
127
+ self.process_btn = gr.Button("Process")
128
+
129
+ self.censor_type.change(
130
+ self._update_settings_visibility,
131
+ inputs=[self.censor_type],
132
+ outputs=[self.blur_settings, self.emoji_settings, self.text_settings]
133
+ )
134
+
135
+ self.process_btn.click(
136
+ self._handle_processing,
137
+ inputs=self._get_all_inputs(),
138
+ outputs=[self.output_media]
139
+ )
140
+
141
+ def _create_blur_settings(self):
142
+ with gr.Row(visible=True) as section:
143
+ self.blur_factor = gr.Slider(
144
+ minimum=1,
145
+ maximum=199,
146
+ value=self.DEFAULT_SETTINGS["blur_factor"],
147
+ step=2,
148
+ label="Blur Factor"
149
+ )
150
+
151
+ return section
152
+
153
+ def _create_emoji_settings(self):
154
+ with gr.Row(visible=False) as section:
155
+ self.emoji = gr.Textbox(
156
+ value=self.DEFAULT_SETTINGS["emoji"],
157
+ label="Emoji"
158
+ )
159
+
160
+ self.emoji_scale = gr.Slider(
161
+ minimum=0.1,
162
+ maximum=2.0,
163
+ value=self.DEFAULT_SETTINGS["emoji_scale"],
164
+ step=0.1,
165
+ label="Emoji Scale Factor"
166
+ )
167
+
168
+ return section
169
+
170
+ def _create_text_settings(self):
171
+ with gr.Column(visible=False) as section:
172
+ with gr.Row(equal_height=True):
173
+ self.text = gr.Textbox(
174
+ value=self.DEFAULT_SETTINGS["text"],
175
+ label="Text"
176
+ )
177
+
178
+ self.text_scale = gr.Slider(
179
+ minimum=0.1,
180
+ maximum=2.0,
181
+ value=self.DEFAULT_SETTINGS["text_scale"],
182
+ step=0.1,
183
+ label="Text Scale Factor"
184
+ )
185
+
186
+ with gr.Row(equal_height=True):
187
+ self.draw_background = gr.Checkbox(
188
+ value=self.DEFAULT_SETTINGS["draw_background"],
189
+ label="Draw Background",
190
+ )
191
+
192
+ self.background_color = gr.ColorPicker(
193
+ value=self.DEFAULT_SETTINGS["background_color"],
194
+ label="Background Color"
195
+ )
196
+
197
+ with gr.Row(equal_height=True):
198
+ self.text_color = gr.ColorPicker(
199
+ value=self.DEFAULT_SETTINGS["text_color"],
200
+ label="Text Color"
201
+ )
202
+
203
+ self.font = gr.Dropdown(
204
+ choices=self.FONTS,
205
+ value=self.DEFAULT_SETTINGS["font"],
206
+ label="Font"
207
+ )
208
+
209
+ return section
210
+
211
+ def _update_settings_visibility(self, censor_choice):
212
+ return [
213
+ gr.Row(visible=censor_choice=="Blur"),
214
+ gr.Row(visible=censor_choice=="Emoji"),
215
+ gr.Row(visible=censor_choice=="Text")
216
+ ]
217
+
218
+ def _get_all_inputs(self):
219
+ return [
220
+ self.input_media,
221
+ self.censor_type,
222
+ self.blur_factor,
223
+ self.emoji,
224
+ self.emoji_scale,
225
+ self.text,
226
+ self.text_scale,
227
+ self.draw_background,
228
+ self.background_color,
229
+ self.text_color,
230
+ self.font
231
+ ]
232
+
233
+ def _handle_processing(self, *args):
234
+ input_media = args[0]
235
+ settings_dict = {
236
+ "type": args[1],
237
+ "blur_factor": args[2],
238
+ "emoji": args[3],
239
+ "emoji_scale": args[4],
240
+ "text": args[5],
241
+ "text_scale": args[6],
242
+ "draw_background": args[7],
243
+ "background_color": args[8],
244
+ "text_color": args[9],
245
+ "font": args[10]
246
+ }
247
+
248
+ return self.process_file(input_media, settings_dict)