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emojify.py
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emojify.py
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import cv2
import numpy as np
import dlib, os
from imutils import face_utils
from imutils.face_utils import FaceAligner
from keras.models import load_model
from preprocess_img import create_mask, get_bounding_rect
from blend import blend
CNN_MODEL = 'cnn_model_keras.h5'
SHAPE_PREDICTOR_68 = "shape_predictor_68_face_landmarks.dat"
cnn_model = load_model(CNN_MODEL)
shape_predictor_68 = dlib.shape_predictor(SHAPE_PREDICTOR_68)
detector = dlib.get_frontal_face_detector()
cam = cv2.VideoCapture(1)
if cam.read()[0]==False:
cam = cv2.VideoCapture(0)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
fa = FaceAligner(shape_predictor_68, desiredFaceWidth=250)
def get_emojis():
emojis_folder = 'emojis/'
emojis = []
for emoji in range(len(os.listdir(emojis_folder))):
print(emoji)
emojis.append(cv2.imread(emojis_folder+str(emoji)+'.png', -1))
return emojis
def get_image_size():
img = cv2.imread('dataset/0/100.jpg', 0)
return img.shape
image_x, image_y = get_image_size()
def keras_process_image(img):
img = cv2.resize(img, (image_x, image_y))
img = np.array(img, dtype=np.float32)
img = np.reshape(img, (1, image_x, image_y, 1))
return img
def keras_predict(model, image):
processed = keras_process_image(image)
pred = model.predict(processed)
pred_probab = pred[0]
pred_class = list(pred_probab).index(max(pred_probab))
return max(pred_probab), pred_class
def fun_util():
emojis = get_emojis()
disp_probab, disp_class = 0, 0
while True:
img = cam.read()[1]
img = cv2.flip(img, 1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = detector(gray)
if len(faces) > 0:
for i, face in enumerate(faces):
shape_68 = shape_predictor_68(img, face)
shape = face_utils.shape_to_np(shape_68)
mask = create_mask(shape, img)
masked = cv2.bitwise_and(gray, mask)
maskAligned = fa.align(mask, gray, face)
faceAligned = fa.align(masked, gray, face)
(x0, y0, x1, y1) = get_bounding_rect(maskAligned)
faceAligned = faceAligned[y0:y1, x0:x1]
faceAligned = cv2.resize(faceAligned, (100, 100))
(x, y, w, h) = face_utils.rect_to_bb(face)
#cv2.rectangle(img, (x, y), (x+w, y+h), (255, 255, 0), 2)
cv2.imshow('faceAligned', faceAligned)
cv2.imshow('face #{}'.format(i), img[y:y+h, x:x+w])
pred_probab, pred_class = keras_predict(cnn_model, faceAligned)
img = blend(img, emojis[pred_class], (x, y, w, h))
cv2.imshow('img', img)
if cv2.waitKey(1) == ord('q'):
break
keras_predict(cnn_model, np.zeros((100, 100, 1), dtype=np.uint8))
fun_util()