def run(modules_config, image_folder, segmentation_path, output_filename):
# Load all objects we will need throughout the run process
# (segmentation data, recognition/postprocess modules, printer, and dataset (image loader))
with open(segmentation_path) as f:
segmentation = [row.split('\t') for row in f.read().split('\n') if row != '']
segmentation = segmentation[1:]
modules = load_modules.load(modules_config)
printer = Printer(output_filename, [module['field_name'] for module in modules])
printer.write_header()
dataset = FullDataset(image_folder, segmentation)
dataloader = DataLoader(dataset, batch_size=8, shuffle=False, num_workers=0, collate_fn=collate)
prev_img_name = None
count = 0
line_number = 1
for i, batch in enumerate(dataloader):
img_names = batch['img_names']
if prev_img_name is None:
prev_img_name = img_names[0]
line_number = 1
images = batch['fields']
line_output = {}
# warn possible segmentation errors, but don't auto fail, just warn
if len(images) != len(modules):
sys.stderr.write("number of fields != expected number of fields for images " +
img_names + "line # " + str(line_number) + "\n")
# Read each field on this line into line_output
for i in range(min(len(images), len(modules))):
module = modules[i]
image = images[i]
rectified_img = module['preprocessing'].batch_preprocess(image)
pred = module['recognition'].batch_run(rectified_img)
corrected_pred = module['postprocessing'].batch_postprocess(pred)
line_output[module['field_name']] = corrected_pred
# output this batch's labels
line_ids = []
for i, img_nm in enumerate(img_names):
if img_nm != prev_img_name:
prev_img_name = img_nm
line_number = 1
count += 1
# log number of pages read
print(count)
lnnm = str(line_number)
lnnm = lnnm if len(lnnm) == 2 else '0' + lnnm
idnt = img_nm.split('/')[-1].split('.')[0] + '_' + lnnm
line_ids.append(idnt)
line_number += 1
printer.write_batch(line_ids, line_output)
printer.close()
def run(modules_config, image_folder, segmentation_path, output_filename):
# Load all objects we will need throughout the run process
# (segmentation data, recognition/postprocess modules, printer, and dataset (image loader))
with open(segmentation_path) as f:
segmentation = [
row.split('\t') for row in f.read().split('\n') if row != ''
]
segmentation = segmentation[1:]
modules = load_modules.load(modules_config)
printer = Printer(output_filename,
[module['field_name'] for module in modules])
printer.write_header()
dataset = FullDataset(image_folder, segmentation)
prev_img_name = None
count = 0
line_number = 1
for census_line in dataset:
if prev_img_name is None or census_line['image_name'] != prev_img_name:
prev_img_name = census_line['image_name']
line_number = 1
count += 1
# print number of census pages read so far
print(count)
img_name = census_line['image_name']
images = census_line['fields']
line_output = {}
# warn possible segmentation errors, but don't auto fail, just warn
if len(images) != len(modules):
sys.stderr.write(
"number of fields != expected number of fields for image " +
img_name + "line # " + str(line_number) + "\n")
# Read each field on this line into line_output
for i in range(min(len(images), len(modules))):
module = modules[i]
image = images[i]
rectified_img = module['preprocessing'].preprocess(image)
pred = module['recognition'].run(rectified_img)
corrected_pred = module['postprocessing'].postprocess(pred)
line_output[module['field_name']] = corrected_pred
# output this line's labels
line_print = str(line_number)
line_print = line_print if len(line_print) == 2 else '0' + line_print
line_id = img_name.split('/')[-1].split('.')[0] + '_' + line_print
printer.write_line(line_id, line_output)
line_number += 1
printer.close()
def printReportMethod(self):
# Return if none of the options are enabled
if not self.plotGraph and not self.printReport:
return
dirPath = self.dirPath
globalProducts = self.readDir(self.printReport)
if self.printReport:
# Print the messages to console
consolePrinter = Printer(ConsolePrinter())
consolePrinter.printTopNByAverage(globalProducts, 3)
consolePrinter.printTopNByReviews(globalProducts, 3)
consolePrinter.printRatingFrequency(globalProducts, 3)
consolePrinter.close()
# Print the messages to file
filePrinter = Printer(FilePrinter('TestReport.txt'))
filePrinter.printTopNByAverage(globalProducts, 3)
filePrinter.printTopNByReviews(globalProducts, 3)
filePrinter.printRatingFrequency(globalProducts, 3)
filePrinter.close()
