Code for How to Blur Faces in Images using OpenCV in Python Tutorial

blur_faces.py

import cv2
import numpy as np

# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
prototxt_path = "weights/deploy.prototxt.txt"
# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel 
model_path = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"

# load Caffe model
model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)

# read the desired image
image = cv2.imread("father-and-daughter.jpg")
# get width and height of the image
h, w = image.shape[:2]
# gaussian blur kernel size depends on width and height of original image
kernel_width = (w // 7) | 1
kernel_height = (h // 7) | 1
# preprocess the image: resize and performs mean subtraction
blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))
# set the image into the input of the neural network
model.setInput(blob)
# perform inference and get the result
output = np.squeeze(model.forward())
for i in range(0, output.shape[0]):
    confidence = output[i, 2]
    # get the confidence
    # if confidence is above 40%, then blur the bounding box (face)
    if confidence > 0.4:
        # get the surrounding box cordinates and upscale them to original image
        box = output[i, 3:7] * np.array([w, h, w, h])
        # convert to integers
        start_x, start_y, end_x, end_y = box.astype(np.int)
        # get the face image
        face = image[start_y: end_y, start_x: end_x]
        # apply gaussian blur to this face
        face = cv2.GaussianBlur(face, (kernel_width, kernel_height), 0)
        # put the blurred face into the original image
        image[start_y: end_y, start_x: end_x] = face
cv2.imshow("image", image)
cv2.waitKey(0)
cv2.imwrite("image_blurred.jpg", image)

blur_faces_live.py

import cv2
import numpy as np
import time

# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
prototxt_path = "weights/deploy.prototxt.txt"
# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel 
model_path = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"

# load Caffe model
model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)

cap = cv2.VideoCapture(0)
while True:
    start = time.time()
    _, image = cap.read()
    # get width and height of the image
    h, w = image.shape[:2]
    kernel_width = (w // 7) | 1
    kernel_height = (h // 7) | 1
    # preprocess the image: resize and performs mean subtraction
    blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))
    # set the image into the input of the neural network
    model.setInput(blob)
    # perform inference and get the result
    output = np.squeeze(model.forward())
    for i in range(0, output.shape[0]):
        confidence = output[i, 2]
        # get the confidence
        # if confidence is above 40%, then blur the bounding box (face)
        if confidence > 0.4:
            # get the surrounding box cordinates and upscale them to original image
            box = output[i, 3:7] * np.array([w, h, w, h])
            # convert to integers
            start_x, start_y, end_x, end_y = box.astype(np.int)
            # get the face image
            face = image[start_y: end_y, start_x: end_x]
            # apply gaussian blur to this face
            face = cv2.GaussianBlur(face, (kernel_width, kernel_height), 0)
            # put the blurred face into the original image
            image[start_y: end_y, start_x: end_x] = face
    cv2.imshow("image", image)
    if cv2.waitKey(1) == ord("q"):
        break
    time_elapsed = time.time() - start
    fps = 1 / time_elapsed
    print("FPS:", fps)

cv2.destroyAllWindows()
cap.release()

blur_faces_video.py

import cv2
import numpy as np
import time
import sys

# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
prototxt_path = "weights/deploy.prototxt.txt"
# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel 
model_path = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"

# load Caffe model
model = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)
# get video file from command line
video_file = sys.argv[1]
# capture frames from video
cap = cv2.VideoCapture(video_file)
fourcc = cv2.VideoWriter_fourcc(*"XVID")
_, image = cap.read()
print(image.shape)
out = cv2.VideoWriter("output.avi", fourcc, 20.0, (image.shape[1], image.shape[0]))
while True:
    start = time.time()
    captured, image = cap.read()
    # get width and height of the image
    if not captured:
        break
    h, w = image.shape[:2]
    kernel_width = (w // 7) | 1
    kernel_height = (h // 7) | 1
    # preprocess the image: resize and performs mean subtraction
    blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))
    # set the image into the input of the neural network
    model.setInput(blob)
    # perform inference and get the result
    output = np.squeeze(model.forward())
    for i in range(0, output.shape[0]):
        confidence = output[i, 2]
        # get the confidence
        # if confidence is above 40%, then blur the bounding box (face)
        if confidence > 0.4:
            # get the surrounding box cordinates and upscale them to original image
            box = output[i, 3:7] * np.array([w, h, w, h])
            # convert to integers
            start_x, start_y, end_x, end_y = box.astype(np.int)
            # get the face image
            face = image[start_y: end_y, start_x: end_x]
            # apply gaussian blur to this face
            face = cv2.GaussianBlur(face, (kernel_width, kernel_height), 0)
            # put the blurred face into the original image
            image[start_y: end_y, start_x: end_x] = face
    cv2.imshow("image", image)
    if cv2.waitKey(1) == ord("q"):
        break
    time_elapsed = time.time() - start
    fps = 1 / time_elapsed
    print("FPS:", fps)
    out.write(image)

cv2.destroyAllWindows()
cap.release()
out.release()