def feature_extraction_queue(feature_extractor, image_path, layer_names,
batch_size, num_classes, num_images=300000):
'''
Given a directory containing images, this function extracts features
for all images. The layers to extract features from are specified
as a list of strings. First, we seek for all images in the directory,
sort the list and feed them to the filename queue. Then, batches are
processed and features are stored in a large object `features`.
:param feature_extractor: object, TF feature extractor
:param image_path: str, path to directory containing images
:param layer_names: list of str, list of layer names
:param batch_size: int, batch size
:param num_classes: int, number of classes for ImageNet (1000 or 1001)
:param num_images: int, number of images to process (default=100000)
:return:
'''
# Add a list of images to process, note that the list is ordered.
image_files = utils.find_files_recursive(image_path, "jpg")
num_images = min(len(image_files), num_images)
image_files = image_files[0:num_images]
num_examples = len(image_files)
num_batches = int(np.ceil(num_examples/batch_size))
# Fill-up last batch so it is full (otherwise queue hangs)
utils.fill_last_batch(image_files, batch_size)
print("#"*80)
print("Batch Size: {}".format(batch_size))
print("Number of Examples: {}".format(num_examples))
print("Number of Batches: {}".format(num_batches))
# Add all the images to the filename queue
feature_extractor.enqueue_image_files(image_files)
# Initialize containers for storing processed filenames and features
feature_dataset = {'filenames': []}
for i, layer_name in enumerate(layer_names):
layer_shape = feature_extractor.layer_size(layer_name)
layer_shape[0] = len(image_files) # replace ? by number of examples
feature_dataset[layer_name] = np.zeros(layer_shape, np.float32)
print("Extracting features for layer '{}' with shape {}".format(layer_name, layer_shape))
print("#"*80)
# Perform feed-forward through the batches
for batch_index in range(num_batches):
t1 = time.time()
# Feed-forward one batch through the network
outputs = feature_extractor.feed_forward_batch(layer_names)
for layer_name in layer_names:
start = batch_index*batch_size
end = start+batch_size
feature_dataset[layer_name][start:end] = outputs[layer_name]
# Save the filenames of the images in the batch
feature_dataset['filenames'].extend(outputs['filenames'])
t2 = time.time()
examples_in_queue = outputs['examples_in_queue']
examples_per_second = batch_size/float(t2-t1)
print("[{}] Batch {:04d}/{:04d}, Batch Size = {}, Examples in Queue = {}, Examples/Sec = {:.2f}".format(
datetime.now().strftime("%Y-%m-%d %H:%M"), batch_index+1,
num_batches, batch_size, examples_in_queue, examples_per_second
))
# If the number of pre-processing threads >1 then the output order is
# non-deterministic. Therefore, we order the outputs again by filenames so
# the images and corresponding features are sorted in alphabetical order.
if feature_extractor.num_preproc_threads > 1:
utils.sort_feature_dataset(feature_dataset)
# We cut-off the last part of the final batch since this was filled-up
feature_dataset['filenames'] = feature_dataset['filenames'][0:num_examples]
for layer_name in layer_names:
feature_dataset[layer_name] = feature_dataset[layer_name][0:num_examples]
return feature_dataset
def feature_extraction_queue(feature_extractor, image_path, layer_names,
batch_size, num_classes, num_excels=100000):
'''
给定包含图像的目录，此函数提取特征所有图像。指定要从中提取特征的图层作为字符串列表。首先，我们寻找目录中的所有图像，
对列表进行排序并将其馈送到文件名队列。那么，批次是已处理和特征存储在对象“features”中。
:param feature_extractor: object, TF feature extractor
:param image_path: str, path to directory containing images
:param layer_names: list of str, list of layer names
:param batch_size: int, batch size
:param num_classes: int, number of classes for ImageNet (1000 or 1001)
:param num_images: int, number of images to process (default=100000)
'''
# 处理的文件位置信息
excel_files = utils.find_files(image_path, ("xls", "csv"))
num_excels = min(len(excel_files),num_excels)
excel_files = excel_files[0:num_excels]
num_examples = len(excel_files)
num_batches = int(np.ceil(num_examples/batch_size))
# Fill-up last batch so it is full (otherwise queue hangs)
utils.fill_last_batch(excel_files, batch_size)#批量大小
print("#"*80)
print("Batch Size: {}".format(batch_size))
print("Number of Examples: {}".format(num_examples))
print("Number of Batches: {}".format(num_batches))
# Add all the images to the filename queue
feature_extractor.enqueue_excel_files(excel_files)
# Initialize containers for storing processed filenames and features
feature_dataset = {'filenames': []}
for i, layer_name in enumerate(layer_names):
layer_shape = feature_extractor.layer_size(layer_name)
layer_shape[0] = len(excel_files) # replace ? by number of examples
feature_dataset[layer_name] = np.zeros(layer_shape, np.float32)
print("Extracting features for layer '{}' with shape {}".format(layer_name, layer_shape))
print("#"*80)
# Perform feed-forward through the batches
for batch_index in range(num_batches):
t1 = time.time()
# Feed-forward one batch through the network
outputs = feature_extractor.feed_forward_batch(layer_names)
for layer_name in layer_names:
start = batch_index*batch_size
end = start+batch_size
feature_dataset[layer_name][start:end] = outputs[layer_name]
# Save the filenames of the images in the batch
feature_dataset['filenames'].extend(outputs['filenames'])
t2 = time.time()
examples_in_queue = outputs['examples_in_queue']
examples_per_second = batch_size/float(t2-t1)
print("[{}] Batch {:04d}/{:04d}, Batch Size = {}, Examples in Queue = {}, Examples/Sec = {:.2f}".format(
datetime.now().strftime("%Y-%m-%d %H:%M"), batch_index+1,
num_batches, batch_size, examples_in_queue, examples_per_second
))
# If the number of pre-processing threads >1 then the output order is
# non-deterministic. Therefore, we order the outputs again by filenames so
# the images and corresponding features are sorted in alphabetical order.
if feature_extractor.num_preproc_threads > 1:
utils.sort_feature_dataset(feature_dataset)
#因为已经装满了我们把最后一部分切断了
feature_dataset['filenames'] = feature_dataset['filenames'][0:num_examples]
for layer_name in layer_names:
feature_dataset[layer_name] = feature_dataset[layer_name][0:num_examples]
return feature_dataset