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app.py

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+import cv2 as cv
+import mediapipe as mp
+import numpy as np
+import gradio as gr
+
+
+def blur_faces_image(image):
+    image = cv.cvtColor(image, cv.COLOR_RGB2BGR)
+    H, W = image.shape[:2]
+
+    mp_face_detection = mp.solutions.face_detection
+
+    with mp_face_detection.FaceDetection(min_detection_confidence=0.5, model_selection=0) as face_detection:
+        img_rgb = cv.cvtColor(image, cv.COLOR_BGR2RGB)
+        out = face_detection.process(img_rgb)
+
+        if out.detections is not None:
+            for detection in out.detections:
+                location_data = detection.location_data
+                bbox = location_data.relative_bounding_box
+
+                x1, y1, w, h = bbox.xmin, bbox.ymin, bbox.width, bbox.height
+
+                x1 = int(x1 * W)
+                y1 = int(y1 * H)
+                w = int(w * W)
+                h = int(h * H)
+                # Apply blur to the face region
+                face_roi = image[y1:y1 + h, x1:x1 + w:]
+                blurred_face = cv.blur(face_roi, (50, 50))
+                image[y1:y1 + h, x1:x1 + w] = blurred_face
+
+    return cv.cvtColor(image, cv.COLOR_BGR2RGB)  # Convert BGR back to RGB for Gradio
+
+def blur_faces_video(video_path, output_path="blurred_output.mp4"):
+    cap = cv.VideoCapture(video_path)
+    fps = cap.get(cv.CAP_PROP_FPS)
+    width = int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
+
+    # Define video writer
+    fourcc = cv.VideoWriter_fourcc(*'mp4v')
+    out = cv.VideoWriter(output_path, fourcc, fps, (width, height))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame_rgb = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
+        blurred_frame = blur_faces_image(frame_rgb)
+        frame_bgr = cv.cvtColor(blurred_frame, cv.COLOR_RGB2BGR)
+
+        out.write(frame_bgr)
+
+    cap.release()
+    out.release()
+
+    return output_path
+def process_video_interface(video_file):
+    video_path = video_file["name"] if isinstance(video_file, dict) else video_file
+    return blur_faces_video(video_path)
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🕵️‍♂️ Face Blur App")
+
+    with gr.Tab("Image Face Blur"):
+        with gr.Row():
+            image_input = gr.Image(label="Upload Image")
+            image_output = gr.Image(label="Blurred Output")
+
+        image_btn = gr.Button("Blur Faces")
+        image_btn.click(fn=blur_faces_image, inputs=image_input, outputs=image_output)
+        gr.Examples(
+            examples=["examples/testImg.png"],
+            inputs=image_input,
+            label="Try Example Image"
+        )
+    with gr.Tab("Video Face Blur"):
+        with gr.Row():
+            video_input = gr.Video(label="Upload Video")
+            video_output = gr.Video(label="Blurred Video Output")
+
+        video_btn = gr.Button("Blur Faces in Video")
+        video_btn.click(fn=process_video_interface, inputs=video_input, outputs=video_output)
+        gr.Examples(
+            examples=["examples/testVideo.mp4"],
+            inputs=video_input,
+            label="Try Example Video"
+        )
+demo.launch()

requirements.txt

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+mediapipe
+opencv-python
+numpy
+gradio