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real-time-ethnicity-prediction.py
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real-time-ethnicity-prediction.py
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import numpy as np
import cv2
from keras.models import Model, Sequential
from keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation
from PIL import Image
from keras.preprocessing.image import load_img, save_img, img_to_array
from keras.applications.imagenet_utils import preprocess_input
from keras.preprocessing import image
import matplotlib.pyplot as plt
from os import listdir
#-----------------------
color = (67,67,67) #gray
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
def loadVggFaceModel():
model = Sequential()
model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(256, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(ZeroPadding2D((1,1)))
model.add(Convolution2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Convolution2D(4096, (7, 7), activation='relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(4096, (1, 1), activation='relu'))
model.add(Dropout(0.5))
model.add(Convolution2D(2622, (1, 1)))
model.add(Flatten())
model.add(Activation('softmax'))
"""
#you can download pretrained weights from https://drive.google.com/file/d/1CPSeum3HpopfomUEK1gybeuIVoeJT_Eo/view?usp=sharing
from keras.models import model_from_json
model.load_weights('C:/Users/IS96273/Desktop/vgg_face_weights.h5')
"""
vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)
return vgg_face_descriptor
#------------------------
model = loadVggFaceModel()
base_model_output = Sequential()
base_model_output = Convolution2D(6, (1, 1), name='predictions')(model.layers[-4].output)
base_model_output = Flatten()(base_model_output)
base_model_output = Activation('softmax')(base_model_output)
race_model = Model(inputs=model.input, outputs=base_model_output)
#pre-trained race and ethnicity prediction model weights can be found here: https://drive.google.com/file/d/1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj/view?usp=sharing
race_model.load_weights('weights/race_model_single_batch.h5')
#------------------------
races = ['Asian', 'Indian', 'Black', 'White', 'Middle Eastern', 'Latino_Hispanic']
#------------------------
cap = cv2.VideoCapture(0) #webcam
while(True):
ret, img = cap.read()
#img = cv2.resize(img, (640, 360))
faces = face_cascade.detectMultiScale(img, 1.3, 5)
for (x,y,w,h) in faces:
if w > 130:
cv2.rectangle(img,(x,y),(x+w,y+h),(67, 67, 67),1) #draw rectangle to main image
detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face
#add margin
margin_rate = 30
try:
margin_x = int(w * margin_rate / 100)
margin_y = int(h * margin_rate / 100)
detected_face = img[int(y-margin_y):int(y+h+margin_y), int(x-margin_x):int(x+w+margin_x)]
detected_face = cv2.resize(detected_face, (224, 224)) #resize to 224x224
#display margin added face
#cv2.rectangle(img,(x-margin_x,y-margin_y),(x+w+margin_x,y+h+margin_y),(67, 67, 67),1)
except Exception as err:
#print("margin cannot be added (",str(err),")")
detected_face = img[int(y):int(y+h), int(x):int(x+w)]
detected_face = cv2.resize(detected_face, (224, 224))
#print("shape: ",detected_face.shape)
if detected_face.shape[0] > 0 and detected_face.shape[1] > 0 and detected_face.shape[2] >0: #sometimes shape becomes (264, 0, 3)
img_pixels = image.img_to_array(detected_face)
img_pixels = np.expand_dims(img_pixels, axis = 0)
img_pixels /= 255
prediction_proba = race_model.predict(img_pixels)
prediction = np.argmax(prediction_proba)
if False: # activate to dump
for i in range(0, len(races)):
if np.argmax(prediction_proba) == i:
print("* ", end='')
print(races[i], ": ", prediction_proba[0][i])
print("----------------")
race = races[prediction]
#--------------------------
#background
overlay = img.copy()
opacity = 0.4
cv2.rectangle(img,(x+w+10,y-50),(x+w+170,y+15),(64,64,64),cv2.FILLED)
cv2.addWeighted(overlay, opacity, img, 1 - opacity, 0, img)
color = (255,255,255)
proba = round(100*prediction_proba[0, prediction], 2)
if proba >= 51:
label = str(race+" ("+str(proba)+"%)")
cv2.putText(img, label, (int(x+w+25), int(y-12)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
#connect face and text
cv2.line(img,(int((x+x+w)/2),y+15),(x+w,y-20),(67, 67, 67),1)
cv2.line(img,(x+w,y-20),(x+w+10,y-20),(67, 67, 67),1)
cv2.imshow('img',img)
if cv2.waitKey(1) & 0xFF == ord('q'): #press q to quit
break
#kill open cv things
cap.release()
cv2.destroyAllWindows()