DhanushNehru · ChrisEssomba · Mar 15, 2025 · Mar 31, 2025
diff --git a/Real-Time-Face-Blurring-Tool/main.py b/Real-Time-Face-Blurring-Tool/main.py
@@ -0,0 +1,164 @@
+import os
+import cv2
+import numpy as np
+
+# Load DNN model once to avoid reloading it for each frame
+def load_face_detection_model():
+    """Loads the pre-trained face detection model."""
+    prototxt_path = "./protocol/deploy.prototxt.txt"
+    model_path = "./model/res10_300x300_ssd_iter_140000_fp16.caffemodel"
+    return cv2.dnn.readNetFromCaffe(prototxt_path, model_path)
+
+# Global variable to store the model (avoids reloading it multiple times)
+face_net = load_face_detection_model()
+
+#save video function
+def save_video(video, output_path):
+
+    # Get video properties
+    fps = video.get(cv2.CAP_PROP_FPS)
+    frame_width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    res = (frame_width, frame_height)
+
+    # Define the video writer
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # MP4 codec
+    out = cv2.VideoWriter(output_path, fourcc, fps, res)
+    return  out
+
+def blur_faces(image, confidence_threshold=0.5, blur_strength=61):
+    """
+    Detects and blurs faces in an image.
+
+    Parameters:
+        image (numpy.ndarray): Input image.
+        confidence_threshold (float): Minimum confidence for face detection.
+        blur_strength (int): Kernel size for Gaussian blur (must be odd).
+
+    Returns:
+        numpy.ndarray: Image with blurred faces.
+    """
+    (h, w) = image.shape[:2]
+
+    # Prepare the image for the deep learning model
+    blob = cv2.dnn.blobFromImage(
+        cv2.resize(image, (300, 300)), 
+        1.0, 
+        (300, 300),
+        (104.0, 177.0, 123.0)
+    )
+
+    # Perform detection
+    face_net.setInput(blob)
+    detections = face_net.forward()
+
+    # Process detections
+    for i in range(detections.shape[2]):
+        confidence = detections[0, 0, i, 2]
+
+        if confidence > confidence_threshold:
+            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+            (startX, startY, endX, endY) = box.astype("int")
+
+            # Ensure coordinates stay within image bounds
+            startX, startY = max(0, startX), max(0, startY)
+            endX, endY = min(w, endX), min(h, endY)
+
+            # Extract and blur face ROI
+            face_roi = image[startY:endY, startX:endX]
+            blurred_face = cv2.GaussianBlur(face_roi, (blur_strength, blur_strength), 0)
+            image[startY:endY, startX:endX] = blurred_face
+
+    return image
+
+
+def blur_faces_images(image_path):
+    """
+    Load an image, blurs detected faces in each frame, and saves the output.
+
+    Parameters:
+        image_path (str): Path to the input image.
+    """
+
+    # Load and process the image
+    image = cv2.imread(image_path)
+    blurred_image = blur_faces(image) 
+
+    # Create output folder
+    name = os.path.basename(image_path)
+    output_folder = "./output_images/"
+    os.makedirs(output_folder, exist_ok=True)
+
+    # Save the processed image in the right folder
+    output_path = os.path.join(output_folder, name)
+    cv2.imwrite(output_path, blurred_image) 
+
+def blur_faces_videos(video_path):
+    """
+    Processes a video, blurs detected faces in each frame, and saves the output.
+
+    Parameters:
+        video_path (str): Path to the input video.
+    """
+
+    name = os.path.basename(video_path)
+    output_folder = "./output_videos/"
+    os.makedirs(output_folder, exist_ok=True)
+
+    # Ensure the output file has a valid extension
+    output_path = os.path.join(output_folder, os.path.splitext(name)[0] + "_blurred.mp4")
+
+    video, output_path, out= save_video(video_path)
+
+    while True:
+        ret, frame = video.read()
+        if not ret:
+            break
+
+        blurred_frame = blur_faces(frame)  # Apply face blurring
+        cv2.imshow('Blurred Video', blurred_frame)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break 
+
+        out.write(blurred_frame)  # Save the processed frame
+
+    # Release resources
+    out.release()
+    video.release()
+    cv2.destroyAllWindows()
+    print(f"Video saved at: {output_path}")
+
+
+def blur_face_webcam():
+    """
+    Captures video from the webcam, applies face blurring in real-time, 
+    and allows stopping the recording by pressing 'q'.
+    """
+
+    video = cv2.VideoCapture(0)  # Open webcam
+    output_folder = "./output_videos/"
+    os.makedirs(output_folder, exist_ok=True)
+
+    # Ensure the output file has a valid extension
+    output_path = os.path.join(output_folder, ("webcam_blurred.mp4"))
+
+    out= save_video(video, output_path)
+    while True:
+        ret, frame = video.read()
+        if not ret:
+            break
+
+        blurred_frame = blur_faces(frame)
+        cv2.imshow('Blurred Webcam Feed', blurred_frame)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break 
+        out.write(blurred_frame)  # Save the processed frame
+
+    # Release resources
+    out.release()
+    video.release()
+    cv2.destroyAllWindows()
+    print(f"Video saved at: {output_path}")
+
diff --git a/Real-Time-Face-Blurring-Tool/model/res10_300x300_ssd_iter_140000_fp16.caffemodel b/Real-Time-Face-Blurring-Tool/model/res10_300x300_ssd_iter_140000_fp16.caffemodel