本文整理匯總了Python中model.config.cfg.PIXEL_MEANS屬性的典型用法代碼示例。如果您正苦於以下問題：Python cfg.PIXEL_MEANS屬性的具體用法？Python cfg.PIXEL_MEANS怎麽用？Python cfg.PIXEL_MEANS使用的例子？那麽, 這裏精選的屬性代碼示例或許可以為您提供幫助。您也可以進一步了解該屬性所在類model.config.cfg的用法示例。
在下文中一共展示了cfg.PIXEL_MEANS屬性的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _get_image_blob
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _get_image_blob(roidb, scale_inds):
"""Builds an input blob from the images in the roidb at the specified
scales.
"""
num_images = len(roidb)
processed_ims = []
im_scales = []
for i in range(num_images):
im = cv2.imread(roidb[i]['image'])
if roidb[i]['flipped']:
im = im[:, ::-1, :]
target_size = cfg.TRAIN.SCALES[scale_inds[i]]
im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,
cfg.TRAIN.MAX_SIZE)
im_scales.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, im_scales 開發者ID:Sunarker，項目名稱:Collaborative-Learning-for-Weakly-Supervised-Object-Detection，代碼行數:23，代碼來源:minibatch.py
示例2: _get_image_blob
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _get_image_blob(roidb, scale_inds):
"""Builds an input blob from the images in the roidb at the specified
scales.
"""
num_images = len(roidb)
processed_ims = []
im_scales = []
for i in range(num_images):
im = helper.read_rgb_img(roidb[i]['image'])
if roidb[i]['flipped']:
im = im[:, ::-1, :]
target_size = cfg.TRAIN.SCALES[scale_inds[i]]
im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,
cfg.TRAIN.MAX_SIZE)
im_scales.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, im_scales 示例3: _add_image_summary
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _add_image_summary(self, image, boxes):
# add back mean
image += cfg.PIXEL_MEANS
# bgr to rgb (opencv uses bgr)
channels = tf.unstack (image, axis=-1)
image = tf.stack ([channels[2], channels[1], channels[0]], axis=-1)
# dims for normalization
width = tf.to_float(tf.shape(image)[2])
height = tf.to_float(tf.shape(image)[1])
# from [x1, y1, x2, y2, cls] to normalized [y1, x1, y1, x1]
cols = tf.unstack(boxes, axis=1)
boxes = tf.stack([cols[1] / height,
cols[0] / width,
cols[3] / height,
cols[2] / width], axis=1)
# add batch dimension (assume batch_size==1)
assert image.get_shape()[0] == 1
boxes = tf.expand_dims(boxes, dim=0)
image = tf.image.draw_bounding_boxes(image, boxes)
return tf.summary.image('ground_truth', image) 示例4: _add_noise_summary
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _add_noise_summary(self, noise, boxes):
# add back mean
noise += cfg.PIXEL_MEANS
noise_channels = tf.unstack (noise, axis=-1)
noise = tf.stack ([noise_channels[2], noise_channels[1], noise_channels[0]], axis=-1)
# dims for normalization
width = tf.to_float(tf.shape(noise)[2])
height = tf.to_float(tf.shape(noise)[1])
# from [x1, y1, x2, y2, cls] to normalized [y1, x1, y1, x1]
cols = tf.unstack(boxes, axis=1)
boxes = tf.stack([cols[1] / height,
cols[0] / width,
cols[3] / height,
cols[2] / width], axis=1)
# add batch dimension (assume batch_size==1)
assert noise.get_shape()[0] == 1
boxes = tf.expand_dims(boxes, dim=0)
noise = tf.image.draw_bounding_boxes(noise, boxes)
return tf.summary.image('noise', noise) 示例5: _add_image_summary
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _add_image_summary(self, image, boxes):
# add back mean
image += cfg.PIXEL_MEANS
# bgr to rgb (opencv uses bgr)
channels = tf.unstack (image, axis=-1)
image = tf.stack ([channels[2], channels[1], channels[0]], axis=-1)
# dims for normalization
width = tf.to_float(tf.shape(image)[2])
height = tf.to_float(tf.shape(image)[1])
# from [x1, y1, x2, y2, cls] to normalized [y1, x1, y1, x1]
cols = tf.unstack(boxes, axis=1)
boxes = tf.stack([cols[1] / height,
cols[0] / width,
cols[3] / height,
cols[2] / width], axis=1)
# add batch dimension (assume batch_size==1)
assert image.get_shape()[0] == 1
boxes = tf.expand_dims(boxes, dim=0)
image = tf.image.draw_bounding_boxes(image, boxes)
return tf.summary.image('ground_truth', image) 示例6: get_image_blob
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def get_image_blob(roidb, scale_inds, scales, max_scale):
"""Builds an input blob from the images in the roidb at the specified
scales.
"""
num_images = len(roidb)
processed_ims = []
im_scales = []
for i in range(num_images):
im = cv2.imread(roidb[i]['image'])
if roidb[i]['flipped']:
im = im[:, ::-1, :]
target_size = scales[scale_inds[i]]
im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,
max_scale)
im_scales.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, im_scales 示例7: _get_image_blob
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _get_image_blob(roidb, scale_inds):
"""Builds an input blob from the images in the roidb at the specified
scales.
"""
num_images = len(roidb)
processed_ims = []
im_scales = []
for i in range(num_images):
im = cv2.imread(roidb[i]['image'])
if roidb[i]['flipped']:
im = im[:, ::-1, :]
target_size = cfg.TRAIN.SCALES[scale_inds[i]]
im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,
cfg.TRAIN.MAX_SIZE)
im_scales.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, im_scales 示例8: _add_gt_image
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _add_gt_image(self):
# add back mean
image = self._image_gt_summaries['image'] + cfg.PIXEL_MEANS
image = imresize(image[0], self._im_info[:2] / self._im_info[2])
# BGR to RGB (opencv uses BGR)
self._gt_image = image[np.newaxis, :,:,::-1].copy(order='C') 開發者ID:Sunarker，項目名稱:Collaborative-Learning-for-Weakly-Supervised-Object-Detection，代碼行數:8，代碼來源:network.py
示例9: _get_image_blob
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _get_image_blob(im):
"""Converts an image into a network input.
Arguments:
im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
in the image pyramid
"""
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
im_shape = im_orig.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
processed_ims = []
im_scale_factors = []
for target_size in cfg.TEST.SCALES:
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
im_scale_factors.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, np.array(im_scale_factors) 開發者ID:Sunarker，項目名稱:Collaborative-Learning-for-Weakly-Supervised-Object-Detection，代碼行數:35，代碼來源:test.py
示例10: _add_gt_image
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _add_gt_image(self):
# add back mean
image = self._image + cfg.PIXEL_MEANS
# BGR to RGB (opencv uses BGR)
resized = tf.image.resize_bilinear(image, tf.to_int32(self._im_info[:2] / self._im_info[2]))
self._gt_image = tf.reverse(resized, axis=[-1]) 示例11: _get_image_blob
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _get_image_blob(im):
"""Converts an image into a network input.
Arguments:
im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
in the image pyramid
"""
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
im_shape = im_orig.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
processed_ims = []
im_scale_factors = []
for target_size in cfg.TEST.SCALES:
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
im_scale_factors.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, np.array(im_scale_factors) 示例12: _get_image_blobs
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _get_image_blobs(im):
"""Converts an image into a network input for faster version.
Arguments:
im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
in the image pyramid
"""
im_orig = im.astype(np.float32, copy=True)
im_scale_factors = []
h, w = im.shape[:2]
im_size_min = np.min(im.shape[0:2])
im_size_max = np.max(im.shape[0:2])
im_scale = float(cfg.TEST.SCALES[0]) / float(im_size_min)
if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
im_scale_factors.append(im_scale)
target_size = (int(im_scale * w), int(im_scale * h))
im = cv2.resize(im_orig, target_size)
im -= cfg.PIXEL_MEANS
# Create a blob to hold the input images
blob = im[np.newaxis, :, :, :]
return blob, np.array(im_scale_factors) 示例13: _get_image_blob
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _get_image_blob(im):
"""Converts an image into a network input.
Arguments:
im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
in the image pyramid
"""
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
im_shape = im_orig.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
# print(">>>>>>>>", im_shape[0], im_shape[1])
processed_ims = []
im_scale_factors = []
for target_size in cfg.TEST.SCALES:
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
im_scale_factors.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
print(">>>>>>>>", im.shape[0], im.shape[1])
blob = im_list_to_blob(processed_ims)
return blob, np.array(im_scale_factors) 示例14: _add_gt_image
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _add_gt_image(self):
# add back mean
image = self._image + cfg.PIXEL_MEANS
# BGR to RGB (opencv uses BGR)
resized = tf.image.resize_bilinear(image, tf.to_int32(self._im_info[:2] / self._im_info[2]))
self._gt_image = tf.reverse(resized, axis=[-1]) 示例15: _add_gt_image
# 需要導入模塊: from model.config import cfg [as 別名]
# 或者: from model.config.cfg import PIXEL_MEANS [as 別名]
def _add_gt_image(self):
# add back mean
image = self._image + cfg.PIXEL_MEANS
# BGR to RGB (opencv uses BGR)
self._gt_image = tf.reverse(image, axis=[-1])