Gender-detection-model/detect.py at master · wingFire-29/Gender-detection-model · GitHub

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import cv2
import argparse

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

    net.setInput(blob)
    detections = net.forward()
    faceBoxes = []
    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)
            faceBoxes.append([x1, y1, x2, y2])
            cv2.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight / 150)), 8)
    return frameOpencvDnn, faceBoxes

parser = argparse.ArgumentParser()
parser.add_argument('--image')

args = parser.parse_args()

faceProto = "opencv_face_detector.pbtxt"
faceModel = "opencv_face_detector_uint8.pb"
genderProto = "gender_deploy.prototxt"
genderModel = "gender_net.caffemodel"

MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
genderList = ['Male', 'Female']

faceNet = cv2.dnn.readNet(faceModel, faceProto)
genderNet = cv2.dnn.readNet(genderModel, genderProto)

video = cv2.VideoCapture(args.image if args.image else 0)


# for the opening of the phone camera
# video = cv2.VideoCapture('http://100.82.5.63:8080/video')
# video.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
 # Set width


padding = 20

while cv2.waitKey(1) < 0:
    hasFrame, frame = video.read()
    if not hasFrame:
        cv2.waitKey()
        break

    resultImg, faceBoxes = highlightFace(faceNet, frame)
    if not faceBoxes:
        print("No face detected")

    for faceBox in faceBoxes:
        face = frame[max(0, faceBox[1] - padding):min(faceBox[3] + padding, frame.shape[0] - 1),
                     max(0, faceBox[0] - padding):min(faceBox[2] + padding, frame.shape[1] - 1)]

        blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
        genderNet.setInput(blob)
        genderPreds = genderNet.forward()

        # Get predicted gender and probability
        genderIndex = genderPreds[0].argmax()
        gender = genderList[genderIndex]
        probability = genderPreds[0][genderIndex] * 100  # Convert to percentage

        print(f'Gender: {gender}, Probability: {probability:.2f}%')

        # Draw the rectangle and put the gender and probability text
        cv2.putText(resultImg, f'{gender} ({probability:.2f}%)',
                    (faceBox[0], faceBox[1] - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)

        cv2.imshow("Detecting Gender", resultImg)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

# Release video capture and close windows
video.release()
cv2.destroyAllWindows()