def make_multi_crop_batch(image_data, coder):
"""Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
"""
image_data[:, :, 2] = np.ones([image_data.shape[0], image_data.shape[1]
]) * 50 / 255.0
image = image_data
crops = []
print('Running multi-cropped image')
h = image.shape[0]
w = image.shape[1]
hl = h - RESIZE_FINAL
wl = w - RESIZE_FINAL
crop = tf.image.resize_images(image, (RESIZE_FINAL, RESIZE_FINAL))
crops.append(standardize_image(crop))
crops.append(standardize_image(tf.image.flip_left_right(crop)))
corners = [(0, 0), (0, wl), (hl, 0), (hl, wl), (int(hl / 2), int(wl / 2))]
for corner in corners:
ch, cw = corner
cropped = tf.image.crop_to_bounding_box(image, ch, cw, RESIZE_FINAL,
RESIZE_FINAL)
crops.append(standardize_image(cropped))
flipped = standardize_image(tf.image.flip_left_right(cropped))
crops.append(standardize_image(flipped))
image_batch = tf.stack(crops)
return image_batch
def make_batch(filename, coder, multicrop):
"""Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, image height in pixels.
width: integer, image width in pixels.
"""
# Read the image file.
with tf.gfile.FastGFile(filename, 'rb') as f:
image_data = f.read()
# Convert any PNG to JPEG's for consistency.
if _is_png(filename):
print('Converting PNG to JPEG for %s' % filename)
image_data = coder.png_to_jpeg(image_data)
image = coder.decode_jpeg(image_data)
crops = []
if multicrop is False:
print('Running a single image')
crop = tf.image.resize_images(image, (RESIZE_FINAL, RESIZE_FINAL))
image = standardize_image(crop)
crops.append(image)
else:
print('Running multi-cropped image')
h = image.shape[0]
w = image.shape[1]
hl = h - RESIZE_FINAL
wl = w - RESIZE_FINAL
crop = tf.image.resize_images(image, (RESIZE_FINAL, RESIZE_FINAL))
crops.append(standardize_image(crop))
crops.append(tf.image.flip_left_right(crop))
corners = [(0, 0), (0, wl), (hl, 0), (hl, wl),
(int(hl / 2), int(wl / 2))]
for corner in corners:
ch, cw = corner
cropped = tf.image.crop_to_bounding_box(image, ch, cw,
RESIZE_FINAL, RESIZE_FINAL)
crops.append(standardize_image(cropped))
flipped = tf.image.flip_left_right(cropped)
crops.append(standardize_image(flipped))
image_batch = tf.stack(crops)
print("一共多少张图片", image_batch.shape)
return image_batch
def make_batch(filename, coder, multicrop):
"""Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
height: integer, image height in pixels.
width: integer, image width in pixels.
"""
# Read the image file.
with tf.gfile.FastGFile(filename, 'r') as f:
image_data = f.read()
# Convert any PNG to JPEG's for consistency.
if _is_png(filename):
print('Converting PNG to JPEG for %s' % filename)
image_data = coder.png_to_jpeg(image_data)
image = coder.decode_jpeg(image_data)
crops = []
if multicrop is False:
print('Running a single image')
crop = tf.image.resize_images(image, (RESIZE_FINAL, RESIZE_FINAL))
image = standardize_image(crop)
crops.append(image)
else:
print('Running multi-cropped image')
h = image.shape[0]
w = image.shape[1]
hl = h - RESIZE_FINAL
wl = w - RESIZE_FINAL
crop = tf.image.resize_images(image, (RESIZE_FINAL, RESIZE_FINAL))
crops.append(standardize_image(crop))
crops.append(tf.image.flip_left_right(crop))
corners = [ (0, 0), (0, wl), (hl, 0), (hl, wl), (int(hl/2), int(wl/2))]
for corner in corners:
ch, cw = corner
cropped = tf.image.crop_to_bounding_box(image, ch, cw, RESIZE_FINAL, RESIZE_FINAL)
crops.append(standardize_image(cropped))
flipped = tf.image.flip_left_right(cropped)
crops.append(standardize_image(flipped))
image_batch = tf.stack(crops)
return image_batch
def make_multi_image_batch(filenames, coder):
"""Process a multi-image batch, each with a single-look
Args:
filenames: list of paths
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
"""
images = []
for filename in filenames:
with tf.gfile.FastGFile(filename, 'rb') as f:
image_data = f.read()
# Convert any PNG to JPEG's for consistency.
if _is_png(filename):
print('Converting PNG to JPEG for %s' % filename)
image_data = coder.png_to_jpeg(image_data)
image = coder.decode_jpeg(image_data)
crop = tf.image.resize_images(image, (RESIZE_FINAL, RESIZE_FINAL))
image = standardize_image(crop)
images.append(image)
image_batch = tf.stack(images)
return image_batch
def make_multi_crop_batch(filename, coder):
"""Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
coder: instance of ImageCoder to provide TensorFlow image coding utils.
Returns:
image_buffer: string, JPEG encoding of RGB image.
"""
# Read the image file.
with tf.gfile.FastGFile(filename, 'rb') as f:
image_data = f.read()
# Convert any PNG to JPEG's for consistency.
if _is_png(filename):
print('Converting PNG to JPEG for %s' % filename)
image_data = coder.png_to_jpeg(image_data)
image = coder.decode_jpeg(image_data) # （256,256,3）
crops = []
print('Running multi-cropped image')
h = image.shape[0]
w = image.shape[1]
hl = h - RESIZE_FINAL # RESIZE_FINAL=227，h1=29
wl = w - RESIZE_FINAL
crop = tf.image.resize_images(image, (RESIZE_FINAL, RESIZE_FINAL))
# 三维矩阵中的数字均值变为0，方差变为1
crops.append(standardize_image(crop))
# 左右翻转
crops.append(standardize_image(tf.image.flip_left_right(crop)))
corners = [(0, 0), (0, wl), (hl, 0), (hl, wl), (int(hl / 2), int(wl / 2))]
for corner in corners:
ch, cw = corner
cropped = tf.image.crop_to_bounding_box(image, ch, cw, RESIZE_FINAL, RESIZE_FINAL)
crops.append(standardize_image(cropped))
flipped = standardize_image(tf.image.flip_left_right(cropped))
crops.append(standardize_image(flipped))
image_batch = tf.stack(crops)
return image_batch
def decode(self, raw_bytes):
dec = tf.image.decode_jpeg(raw_bytes, channels=3)
res = tf.image.resize_images(dec, (RESIZE_FINAL, RESIZE_FINAL))
stand = standardize_image(res)
return stand
