yukpiz · February 7, 2019 06:50 · yukpiz · Feb 5, 2019
diff --git a/zombie.py b/zombie.py
 # codeing:utf-8

 import cv2
 import os
 import glob
 import imghdr

 import tensorflow as tf
 from tensorflow import keras

 import numpy as np
 import matplotlib.pyplot as plt


 PROC_PATHS = [
    "data/dst/face/zombi1.mp4/",
    "data/dst/face/zombi2.mp4/",
    "data/dst/face/zombi3.mp4/",
 ]

 IMG_DIR_NAMES = [
    "0_sakura_minamoto",
    "1_saki_nikaido",
    "2_ai_mizuno",
    "3_junko_konno",
    "4_yugiri",
    "5_lily_hoshikawa",
    "6_tae_yamada",
    "7_kotaro_tatsumi",
 ]

 CLASS_NAMES = [
    "Sakura Minamoto",
    "Saki Nikaido",
    "Ai Mizuno",
    "Junko Konno",
    "Yugiri",
    "Lily Hoshikawa",
    "Tae Yamada",
    "Kotaro Tatsumi",
 ]

 FIXED_IMAGE_SIZE = 28


 class NotMatchCountError(Exception):
    def __init__(self, message):
        self.message = message


 def main(proc_paths, img_dir_names):
    image_bytes = []
    image_labels = []
    image_count = 0
    for proc_path in proc_paths:

        label_index = 0
        for img_dir_name in img_dir_names:

            target = os.path.join(proc_path, img_dir_name)
            for image_path in glob.glob('{}/*.*'.format(target), recursive=False):

                # 画像ファイル以外はスキップ
                if imghdr.what(image_path) is None:
                    continue
                image_count += 1

                image_byte = cv2.imread(image_path, cv2.IMREAD_COLOR)
                image_byte = cv2.resize(
                    image_byte, (FIXED_IMAGE_SIZE, FIXED_IMAGE_SIZE))

                image_bytes.append(image_byte)
                image_labels.append(label_index)

            label_index += 1
    print("Image Count: %d" % len(image_bytes))
    print("Label Count: %d" % len(image_labels))
    return train_model(image_bytes, image_labels)


 def train_model(image_bytes, image_labels):
    if len(image_bytes) != len(image_labels):
        raise NotMatchCountError("画像枚数とラベル数が一致しません")

    # NumPy配列化
    train_images = np.array(image_bytes)/255.0
    train_labels = np.array(image_labels)
    print(train_images.shape)
    print(train_labels.shape)

    # ???????
    model = keras.Sequential([
        keras.layers.Flatten(input_shape=(
            FIXED_IMAGE_SIZE, FIXED_IMAGE_SIZE, 3)),
        keras.layers.Dense(256, activation=tf.nn.relu),
        keras.layers.Dense(20, activation=tf.nn.softmax)
    ])
    model.compile(optimizer=tf.train.AdamOptimizer(),
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])

    model.fit(train_images, train_labels, epochs=10)
    return model


 def test(model, *image_paths):
    image_bytes = []
    for image_path in image_paths:
        image_byte = cv2.imread(image_path, cv2.IMREAD_COLOR)
        image_byte = cv2.resize(
            image_byte, (FIXED_IMAGE_SIZE, FIXED_IMAGE_SIZE))

        image_bytes.append(image_byte)

    test_images = np.array(image_bytes)/255.0
    predictions = model.predict(test_images)
    return predictions


 if __name__ == "__main__":
    print("===> START")
    model = main(PROC_PATHS, IMG_DIR_NAMES)

    predictions = test(model, "270face.jpg")
    # plot_image(predictions[0])

    print("===> FINISH")
	# codeing:utf-8

	import cv2
	import os
	import glob
	import imghdr

	import tensorflow as tf
	from tensorflow import keras

	import numpy as np
	import matplotlib.pyplot as plt


	PROC_PATHS = [
	"data/dst/face/zombi1.mp4/",
	"data/dst/face/zombi2.mp4/",
	"data/dst/face/zombi3.mp4/",
	]

	IMG_DIR_NAMES = [
	"0_sakura_minamoto",
	"1_saki_nikaido",
	"2_ai_mizuno",
	"3_junko_konno",
	"4_yugiri",
	"5_lily_hoshikawa",
	"6_tae_yamada",
	"7_kotaro_tatsumi",
	]

	CLASS_NAMES = [
	"Sakura Minamoto",
	"Saki Nikaido",
	"Ai Mizuno",
	"Junko Konno",
	"Yugiri",
	"Lily Hoshikawa",
	"Tae Yamada",
	"Kotaro Tatsumi",
	]

	FIXED_IMAGE_SIZE = 28


	class NotMatchCountError(Exception):
	def __init__(self, message):
	self.message = message


	def main(proc_paths, img_dir_names):
	image_bytes = []
	image_labels = []
	image_count = 0
	for proc_path in proc_paths:

	label_index = 0
	for img_dir_name in img_dir_names:

	target = os.path.join(proc_path, img_dir_name)
	for image_path in glob.glob('{}/.'.format(target), recursive=False):

	# 画像ファイル以外はスキップ
	if imghdr.what(image_path) is None:
	continue
	image_count += 1

	image_byte = cv2.imread(image_path, cv2.IMREAD_COLOR)
	image_byte = cv2.resize(
	image_byte, (FIXED_IMAGE_SIZE, FIXED_IMAGE_SIZE))

	image_bytes.append(image_byte)
	image_labels.append(label_index)

	label_index += 1
	print("Image Count: %d" % len(image_bytes))
	print("Label Count: %d" % len(image_labels))
	return train_model(image_bytes, image_labels)


	def train_model(image_bytes, image_labels):
	if len(image_bytes) != len(image_labels):
	raise NotMatchCountError("画像枚数とラベル数が一致しません")

	# NumPy配列化
	train_images = np.array(image_bytes)/255.0
	train_labels = np.array(image_labels)
	print(train_images.shape)
	print(train_labels.shape)

	# ???????
	model = keras.Sequential([
	keras.layers.Flatten(input_shape=(
	FIXED_IMAGE_SIZE, FIXED_IMAGE_SIZE, 3)),
	keras.layers.Dense(256, activation=tf.nn.relu),
	keras.layers.Dense(20, activation=tf.nn.softmax)
	])
	model.compile(optimizer=tf.train.AdamOptimizer(),
	loss='sparse_categorical_crossentropy',
	metrics=['accuracy'])

	model.fit(train_images, train_labels, epochs=10)
	return model


	def test(model, *image_paths):
	image_bytes = []
	for image_path in image_paths:
	image_byte = cv2.imread(image_path, cv2.IMREAD_COLOR)
	image_byte = cv2.resize(
	image_byte, (FIXED_IMAGE_SIZE, FIXED_IMAGE_SIZE))

	image_bytes.append(image_byte)

	test_images = np.array(image_bytes)/255.0
	predictions = model.predict(test_images)
	return predictions


	if __name__ == "__main__":
	print("===> START")
	model = main(PROC_PATHS, IMG_DIR_NAMES)

	predictions = test(model, "270face.jpg")
	# plot_image(predictions[0])

	print("===> FINISH")