genderClassificationForImage.py

# Importing required packages
from keras.models import load_model
import numpy as np
import dlib
import cv2

def shapePoints(shape):
	coords = np.zeros((68, 2), dtype="int")
	for i in range(0, 68):
		coords[i] = (shape.part(i).x, shape.part(i).y)
	return coords


def rectPoints(rect):
	x = rect.left()
	y = rect.top()
	w = rect.right() - x
	h = rect.bottom() - y
	return (x, y, w, h)



genderModelPath = 'models\genderModel_VGG16.hdf5'
genderClassifier = load_model(genderModelPath, compile=False)
genderTargetSize = genderClassifier.input_shape[1:3]

genders =  {
	0: { "label": "Female", "color": (245,215,130) },
	1: { "label": "Male", "color": (148,181,192) },
}


# pre-trained model
modelFile = "faceDetection/models/dnn/res10_300x300_ssd_iter_140000.caffemodel"
# prototxt has the information of where the training data is located.
configFile = "faceDetection/models/dnn/deploy.prototxt"
net = cv2.dnn.readNetFromCaffe(configFile, modelFile)

def detectFacesWithDNN(frame):
	# A neural network that really supports the input value
	size = (300,300)

	# After executing the average reduction, the image needs to be scaled
	scalefactor = 1.0

	# These are our mean subtraction values. They can be a 3-tuple of the RGB means or
	# they can be a single value in which case the supplied value is subtracted from every
	# channel of the image.
	swapRB = (104.0, 117.0, 123.0)

	height, width = frame.shape[:2]
	resizedFrame = cv2.resize(frame, size)
	blob = cv2.dnn.blobFromImage(resizedFrame, scalefactor, size, swapRB)
	net.setInput(blob)
	dnnFaces = net.forward()
	for i in range(dnnFaces.shape[2]):
		confidence = dnnFaces[0, 0, i, 2]
		if confidence > 0.5:
			box = dnnFaces[0, 0, i, 3:7] * np.array([width, height, width, height])
			(x, y, x1, y1) = box.astype("int")
			resized = frame[y: y1, x:x1]
			try:
				frame_resize = cv2.resize(resized, genderTargetSize)
			except:
				continue

			frame_resize = frame_resize.astype("float32")
			frame_scaled = frame_resize/255.0
			frame_reshape = np.reshape(frame_scaled,(1, 100, 100 ,3))
			frame_vstack = np.vstack([frame_reshape])
			gender_prediction = genderClassifier.predict(frame_vstack)
			gender_probability = np.max(gender_prediction)
			color = (255,255,255)
			if(gender_probability > 0.4):

				gender_label = np.argmax(gender_prediction)
				gender_result = genders[gender_label]["label"]
				color = genders[gender_label]["color"]
				cv2.putText(frame, gender_result , (x+5, y1-5),
				cv2.FONT_HERSHEY_SIMPLEX, 1 , color, 2,   cv2.LINE_AA)
				cv2.rectangle(frame, (x, y), (x1, y1), color, 2)
			else:
				cv2.rectangle(frame, (x, y), (x1, y1), color, 2)
	return frame

photo = cv2.imread("images/photo1.jpg")
photo = cv2.resize(photo, (1080,720))
frame = detectFacesWithDNN(photo)
cv2.imshow("Gender Classification", frame)
cv2.waitKey(0)