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label_test2.py
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label_test2.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Jan 14 23:41:03 2018
@author: KEEL
"""
import os
import numpy as np
import cv2
import tensorflow as tf
import glob
IMG_SIZE = 28
IMG_PIXELS = IMG_SIZE*IMG_SIZE*3
#----------------------------------------------------------------------
#画像をNumpy配列に変換する
# 画像のあるディレクトリ
train_img_dirs = ['KizunaAI','MiraiAkari','Nekomasu','Shiro','KaguyaLuna','Cafeno-Zombiko','DD','Fuji-Aoi',
'Fujisaki-Yua','hoonie','Kurumi-chan','MIDI','Miial','Mochi-Hiyoko','Moscowmule','MyuMyu',
'Neets','Nemu','Nora-cat','Raiden-Kasuka', 'Suzuki-Secil', 'Todoki-Uka', 'Tokinosora', 'Umakoshi-Kentaro']
CLASSES_NUM = len(train_img_dirs)
xml_path = "./lbpcascade_animeface.xml"
out_path = "./face/"
def faceDetect(img_path):
classifier = cv2.CascadeClassifier(xml_path)
img_count = 1
face_imgs = []
#for img_path in img_list:
org_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
gray_img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
face_points = classifier.detectMultiScale(gray_img, \
scaleFactor=1.2, minNeighbors=2, minSize=(1,1))
for points in face_points:
x, y, width, height = points
dst_img = org_img[y:y+height, x:x+width]
dst_img = cv2.resize(dst_img, (IMG_SIZE,IMG_SIZE))
face_imgs.append(dst_img)
face_img = cv2.rectangle(org_img, (x,y), (x+width,y+height), (0, 0, 0), 2)
new_img_name = out_path + str(img_count) + 'face.jpg'
cv2.imwrite(new_img_name, face_img)
img_count += 1
print(img_count)
if img_count != 1:
return face_points[0]
def inference(images_placeholder, keep_prob):
""" モデルを作成する関数
引数:
images_placeholder: inputs()で作成した画像のplaceholder
keep_prob: dropout率のplace_holder
返り値:
cross_entropy: モデルの計算結果
"""
W_conv1 = tf.Variable(tf.truncated_normal([5, 5, 3, 32], stddev=0.1),name='W_conv1')
b_conv1 = tf.Variable(tf.constant(0.1, shape=[32]),name='b_conv1')
x_image = tf.reshape(images_placeholder, [-1,IMG_SIZE,IMG_SIZE,3])
h_conv1 = tf.nn.relu(tf.nn.conv2d(x_image, W_conv1, strides=[1, 1, 1, 1], padding='SAME') + b_conv1)
h_pool1 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
x_image = tf.reshape(images_placeholder, [-1,IMG_SIZE,IMG_SIZE,3])
W_conv2 = tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.1),name='W_conv2')
b_conv2 = tf.Variable(tf.constant(0.1, shape=[64]),name='b_conv2')
h_conv2 = tf.nn.relu(tf.nn.conv2d(h_pool1, W_conv2, strides=[1, 1, 1, 1], padding='SAME') + b_conv2)
h_pool2 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
W_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1024], stddev=0.1),name='W_fc1')#変更元は7*7*64
b_fc1 = tf.Variable(tf.constant(0.1, shape=[1024]),name='b_fc1')
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])#変更元は7*7*64
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = tf.Variable(tf.truncated_normal([1024, CLASSES_NUM], stddev=0.1),name='W_fc2')
b_fc2 = tf.Variable(tf.constant(0.1, shape=[CLASSES_NUM]),name='b_fc2')
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
#cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
return y_conv
images_placeholder = tf.placeholder("float", shape=(None, IMG_PIXELS))
keep_prob = tf.placeholder("float")
init = tf.global_variables_initializer()
logits = inference(images_placeholder, keep_prob)
sess = tf.InteractiveSession()
sess.run(init)
saver = tf.train.Saver()
saver = tf.train.import_meta_graph('./Model/model.ckpt.meta')#注意
saver.restore(sess, "./Model/model.ckpt")
input_path = './test_img/'
filename = glob.glob(input_path + '*.jpg')
for img_path in input_path:
frame = cv2.imread(img_path)
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
#顔の検出
dets = faceDetect(filename)
if not isinstance(dets,type(None)):
x, y, width, height = dets
image = frame[y:y+height, x:x+width]
cv2.rectangle(frame, (x,y), (x+width, y+height), (0, 0, 0), 4)
cv2.imwrite(filename, frame)
cv2.imshow("1",image)
cv2.waitKey(0)
img = cv2.resize(img.copy(), (28, 28))
ximage = []
ximage.append(img.flatten().astype(np.float32)/255.0)
ximage = np.asarray(ximage)
print(ximage.shape)
pred = np.argmax(logits.eval(session=sess,feed_dict={ images_placeholder : ximage, keep_prob: 1.0 }))
print(pred)