Created
January 1, 2017 08:15
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顔識別器
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import cv2 | |
import sys | |
import os | |
import os.path | |
import numpy as np | |
np.random.seed(1337) | |
from keras.preprocessing.image import ImageDataGenerator | |
from keras.models import Sequential | |
from keras.layers import Dense, Dropout, Activation, Flatten | |
from keras.layers import Convolution2D, MaxPooling2D | |
from keras.utils import np_utils | |
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img | |
from PIL import Image | |
label = ["Meguru", "Nene", "no_face", "other", "Touko", "Tsumugi", "Wakana"] | |
def cnn(): | |
model = Sequential() | |
model.add(Convolution2D(32, 3, 3, border_mode='same', | |
input_shape=(32, 32, 3))) | |
model.add(Activation('relu')) | |
model.add(Convolution2D(32, 3, 3)) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Dropout(0.25)) | |
model.add(Convolution2D(64, 3, 3, border_mode='same')) | |
model.add(Activation('relu')) | |
model.add(Convolution2D(64, 3, 3)) | |
model.add(Activation('relu')) | |
model.add(MaxPooling2D(pool_size=(2, 2))) | |
model.add(Dropout(0.25)) | |
model.add(Flatten()) | |
model.add(Dense(512)) | |
model.add(Activation('relu')) | |
model.add(Dropout(0.5)) | |
model.add(Dense(7)) | |
model.add(Activation('softmax')) | |
return model | |
model = cnn() | |
model.compile(loss='categorical_crossentropy', | |
optimizer='rmsprop', | |
metrics=['accuracy']) | |
model.load_weights('sanoba_cnn.hdf5') | |
def predict(img): | |
im = cv2.resize(img, (32, 32)) | |
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB).astype(np.float32) | |
# im = im.transpose((2, 0, 1)) # for theano | |
im = im.reshape((1,) + im.shape) | |
im /= 255 | |
classes = model.predict_classes(im) | |
return label[classes[0]] | |
def detect(filename, cascade_file = "./lbpcascade_animeface.xml"): | |
if not os.path.isfile(cascade_file): | |
raise RuntimeError("%s: not found" % cascade_file) | |
cascade = cv2.CascadeClassifier(cascade_file) | |
image = cv2.imread(filename) | |
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
gray = cv2.equalizeHist(gray) | |
faces = cascade.detectMultiScale(gray, | |
# detector options | |
scaleFactor = 1.05, | |
minNeighbors = 5, | |
minSize = (32, 32)) | |
for (x, y, w, h) in faces: | |
name = predict(image[y:y+h, x:x+w]) | |
if name != "no_face": | |
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2) | |
cv2.putText(image, name, (x, y), cv2.FONT_HERSHEY_PLAIN, 3, (255, 255, 0), 3) | |
cv2.imshow(name, image) | |
cv2.waitKey(0) | |
cv2.imwrite("out.png", image) | |
if len(sys.argv) != 2: | |
sys.stderr.write("usage: detect.py <filename>\n") | |
sys.exit(-1) | |
detect(sys.argv[1]) |
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