opencvdetectandblurfaceusingdnn.py

opencvdetectandblurfaceusingdnn.py

#    Copyright (c) 2022
#    Author      : Bruno Capuano
#    Create Time : 2022 Feb
#    Change Log  :
#    - Open a camera feed from a local webcam and analyze each frame to detect faces using DNN
#    - When a face is detected, the app will blur the face zone
#    - Download model and prototxt from https://github.com/spmallick/learnopencv/tree/master/FaceDetectionComparison/models
#    - Press [D] to start/stop face detection
#    - Press [Q] to quit the app
#
#    The MIT License (MIT)
#
#    Permission is hereby granted, free of charge, to any person obtaining a copy
#    of this software and associated documentation files (the "Software"), to deal
#    in the Software without restriction, including without limitation the rights
#    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#    copies of the Software, and to permit persons to whom the Software is
#    furnished to do so, subject to the following conditions:
#
#    The above copyright notice and this permission notice shall be included in
#    all copies or substantial portions of the Software.
#
#    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
#    THE SOFTWARE.

import cv2
import time

video_capture = cv2.VideoCapture(0)
time.sleep(2)

# -----------------------------------------------
# Face Detection using DNN Net
# -----------------------------------------------
# detect faces using a DNN model 
# download model and prototxt from https://github.com/spmallick/learnopencv/tree/master/FaceDetectionComparison/models

def detectFaceOpenCVDnn(net, frame, conf_threshold=0.7):
    
    frameHeight = frame.shape[0]
    frameWidth = frame.shape[1]
    blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), [104, 117, 123], False, False,)

    net.setInput(blob)
    detections = net.forward()
    bboxes = []
    for i in range(detections.shape[2]):
        confidence = detections[0, 0, i, 2]
        if confidence > conf_threshold:
            x1 = int(detections[0, 0, i, 3] * frameWidth)
            y1 = int(detections[0, 0, i, 4] * frameHeight)
            x2 = int(detections[0, 0, i, 5] * frameWidth)
            y2 = int(detections[0, 0, i, 6] * frameHeight)
            bboxes.append([x1, y1, x2, y2])
            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight / 150)), 8,)
            
            top=x1
            right=y1
            bottom=x2-x1
            left=y2-y1

            #  blurry rectangle to the detected face
            face = frame[right:right+left, top:top+bottom]
            face = cv2.GaussianBlur(face,(23, 23), 30)
            frame[right:right+face.shape[0], top:top+face.shape[1]] = face

    return frame, bboxes

# load face detection model
modelFile = "models/res10_300x300_ssd_iter_140000_fp16.caffemodel"
configFile = "models/deploy.prototxt"
net = cv2.dnn.readNetFromCaffe(configFile, modelFile)
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

detectionEnabled = False
while True:
    try:
        _, frameOrig = video_capture.read()
        frame = cv2.resize(frameOrig, (640, 480))

        if(detectionEnabled == True):
            outOpencvDnn, bboxes = detectFaceOpenCVDnn(net, frame)

        cv2.imshow('@ElBruno - Face Blur usuing DNN', frame)

    except Exception as e:
        print(f'exc: {e}')
        pass

    # key controller
    key = cv2.waitKey(1) & 0xFF    
    if key == ord("d"):
        detectionEnabled = not detectionEnabled

    if key == ord("q"):
        break

video_capture.release()
cv2.destroyAllWindows()