def write_examples_to_dataset_file(example_list, categories, width, height, use_bgr_ordering, output_dataset):
"""
Saves an array of examples to a dataset file.
"""
print("Processing {} examples, using image size {}x{}, bgr={}".format(len(example_list), width, height, use_bgr_ordering))
with open(output_dataset, 'w') as dataset_file:
# Write header
if categories:
dataset_file.write("# Category labels\n")
for i, category in enumerate(categories):
dataset_file.write("# {} : {}\n".format(i, category))
for example in example_list:
# Try to read this as an image
image = cv2.imread(example[1])
if image is not None:
# Write label
dataset_file.write("{}".format(example[0]))
print("Processing {0[0]} | {0[1]}".format(example))
resized = modelHelpers.prepare_image_for_model(image, width, height, not use_bgr_ordering)
# Write image data
valuesWritten = 0
for value in resized:
dataset_file.write("\t{}".format(value))
valuesWritten = valuesWritten + 1
# Write label, source file as comment
dataset_file.write("\t# class={0[2]}, source={0[1]}".format(example))
dataset_file.write("\n")
print(" Wrote {} data values".format(valuesWritten))
else:
print("Skipping {}, could not open as an image".format(example[1]))
print()
print("Wrote {} examples to {}".format(len(example_list), output_dataset))
def write_examples_to_dataset_file(example_list,
categories,
width,
height,
use_bgr_ordering,
output_dataset,
verbose=False):
"""
Saves an array of examples to a dataset file.
"""
print("Processing {} examples, using image size {}x{}, bgr={}".format(
len(example_list), width, height, use_bgr_ordering))
with open(output_dataset, 'w') as dataset_file:
# Write header
if categories:
dataset_file.write("# Category labels\n")
for i, category in enumerate(categories):
dataset_file.write("# {} : {}\n".format(i, category))
count = 0
for example in example_list:
# Try to read this as an image
image = cv2.imread(example[1])
if image is not None:
if verbose:
print("Processing {0[0]} | {0[1]}".format(example))
# Write label
dataset_file.write("{}".format(example[0]))
# Write image data
resized = modelHelpers.prepare_image_for_model(
image,
width,
height,
not use_bgr_ordering,
convert_to_float=False,
preprocess_tag=None)
dataset_file.write("\t")
resized.tofile(dataset_file, sep="\t", format="%s")
# Write label, source file as comment
dataset_file.write(
"\t# class={0[2]}, source={0[1]}".format(example))
dataset_file.write("\n")
if verbose:
print(" Wrote {} data values".format(len(resized)))
else:
if (count + 1) % 1000 == 0:
print(".", sep="", end="")
count += 1
else:
print("Skipping {}, could not open as an image".format(
example[1]))
print()
print("Wrote {} examples to {}".format(len(example_list), output_dataset))
def write_examples_to_dataset_file(example_list, width, height,
use_bgr_ordering, output_dataset):
"""
Saves an array of examples to a dataset file.
"""
print("Processing {} examples, using image size {}x{}, bgr={}".format(
len(example_list), width, height, use_bgr_ordering))
with open(output_dataset, 'w') as dataset_file:
for example in example_list:
# Write label
dataset_file.write("{}".format(example[0]))
print("Processing {} | {}".format(example[0], example[1]))
image = cv2.imread(example[1])
resized = modelHelpers.prepare_image_for_model(
image, width, height, not use_bgr_ordering)
# Write image data
valuesWritten = 0
for value in resized:
dataset_file.write("\t{}".format(value))
valuesWritten = valuesWritten + 1
dataset_file.write("\n")
print(" Wrote {} data values".format(valuesWritten))