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data_analysis.py
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data_analysis.py
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import argparse
import logging
import os
from pathlib import Path
from typing import List
from utils.utils import get_key_def, read_csv
from utils.geoutils import vector_to_raster
from utils.readers import read_parameters, image_reader_as_array
from utils.verifications import validate_num_classes
import time
import rasterio
import csv
import numpy as np
from collections import OrderedDict
from numpy import genfromtxt
from tqdm import tqdm
import images_to_samples
logging.getLogger(__name__)
def create_csv():
"""
Creates samples from the input images for the pixel_inventory function
"""
prep_csv_path = params['sample']['prep_csv_file']
dist_samples = params['sample']['samples_dist']
sample_size = params['global']['samples_size']
data_path = params['global']['data_path']
Path.mkdir(Path(data_path), exist_ok=True)
num_classes = params['global']['num_classes']
data_prep_csv = read_csv(prep_csv_path)
csv_prop_data = params['global']['data_path'] + '/prop_data.csv'
if os.path.isfile(csv_prop_data):
os.remove(csv_prop_data)
with tqdm(data_prep_csv) as _tqdm:
for info in _tqdm:
_tqdm.set_postfix(OrderedDict(file=f'{info["tif"]}', sample_size=params['global']['samples_size']))
# Validate the number of class in the vector file
validate_num_classes(info['gpkg'], num_classes, info['attribute_name'])
assert os.path.isfile(info['tif']), f"could not open raster file at {info['tif']}"
with rasterio.open(info['tif'], 'r') as raster:
# Burn vector file in a raster file
np_label_raster = vector_to_raster(vector_file=info['gpkg'],
input_image=raster,
attribute_name=info['attribute_name'],
fill=get_key_def('ignore_idx', get_key_def('training', params, {}),
0))
# Read the input raster image
np_input_image = image_reader_as_array(input_image=raster,
aux_vector_file=get_key_def('aux_vector_file', params['global'],
None),
aux_vector_attrib=get_key_def('aux_vector_attrib',
params['global'], None),
aux_vector_ids=get_key_def('aux_vector_ids', params['global'],
None),
aux_vector_dist_maps=get_key_def('aux_vector_dist_maps',
params['global'], True),
aux_vector_dist_log=get_key_def('aux_vector_dist_log',
params['global'], True),
aux_vector_scale=get_key_def('aux_vector_scale',
params['global'], None))
# Mask the zeros from input image into label raster.
if params['sample']['mask_reference']:
np_label_raster = images_to_samples.mask_image(np_input_image, np_label_raster)
np_label_raster = np.reshape(np_label_raster, (np_label_raster.shape[0], np_label_raster.shape[1], 1))
h, w, num_bands = np_input_image.shape
# half tile padding
half_tile = int(sample_size / 2)
pad_label_array = np.pad(np_label_raster, ((half_tile, half_tile), (half_tile, half_tile), (0, 0)),
mode='constant')
for row in range(0, h, dist_samples):
for column in range(0, w, dist_samples):
target = np.squeeze(pad_label_array[row:row + sample_size, column:column + sample_size, :], axis=2)
pixel_inventory(target, sample_size, params['global']['num_classes'] + 1,
params['global']['data_path'], info['dataset'])
def pixel_inventory(sample, sample_size, num_classes, data_path, dataset):
"""
Calculates the proportions of the classes contained in a sample
:param sample: numpy array of the sample
:param sample_size: (int) Size (in pixel) of the samples
:param num_classes: (dict) Number of classes in reference data
:param data_path: (str) path to the samples folder
:param dataset: (str) Type of dataset of the sample. Can be 'trn' or 'val' or 'tst'
"""
sample_total = sample_size ** 2
samples_prop = []
for i in range(0, num_classes):
samples_prop.append(0.0)
if i in np.unique(sample.flatten()):
samples_prop[i] += (round((np.bincount(sample.flatten())[i] / sample_total) * 100, 1))
samples_prop.append(dataset)
f = open(data_path + '/prop_data.csv', 'a')
with f:
writer = csv.writer(f)
writer.writerow(samples_prop)
def minimum_annotated_percent(target_background_percent, min_annotated_percent):
"""
:param target_background_percent: background pixels in sample
:param min_annotated_percent: (int) Minimum % of non background pixels in sample, in order to consider it part of
the dataset
:return: (Bool)
"""
if float(target_background_percent) <= 100 - min_annotated_percent:
return True
return False
def minimum_annotated_percent_search(classes, annotated_p, sampling, sample_data):
keys = ['map', 'std', 'trn_data']
for i in classes:
keys.append('prop' + str(i))
stats_dict = {key: [] for key in keys}
for i in tqdm(annotated_p):
prop_classes = {}
for j, (key, value) in enumerate(sampling.items()):
if j >= 2:
prop_classes.update({key: 0})
numbers_sample = {'trn': 0, 'val': 0, 'tst': 0}
for row in sample_data:
if minimum_annotated_percent(row[0], i):
compute_classes(classes, prop_classes, row, numbers_sample)
if parameters_search_dict(stats_dict, prop_classes, numbers_sample, i) is False:
break
if len(sampling['method']) == 1:
results(classes, stats_dict)
else:
return stats_dict
def class_proportion(row, classes, source):
condition = []
if params['data_analysis']['optimal_parameters_search']:
for i in classes:
if float(row[i]) >= source[i]:
condition.append(1)
if len(condition) == len(classes):
return True
else:
return False
else:
for i in classes:
if float(row[i]) >= source[str(i)]:
condition.append(1)
if len(condition) == len(classes):
return True
else:
return False
def class_proportion_search(classes, sampling, sample_data):
keys = ['combination', 'std', 'trn_data']
for i in classes:
keys.append('prop' + str(i))
stats_dict = {key: [] for key in keys}
prop_classes = {}
threshold = [float(0.0) for i in classes]
for i in classes:
if i != 0:
while threshold[i] < 100.0:
for j, (key, value) in enumerate(sampling.items()):
if j >= 2:
prop_classes.update({key: 0})
numbers_sample = {'trn': 0, 'val': 0, 'tst': 0}
for row in tqdm(sample_data):
if class_proportion(row, classes, threshold):
compute_classes(classes, prop_classes, row, numbers_sample)
if parameters_search_dict(stats_dict, prop_classes, numbers_sample, threshold) is False:
break
else:
val = threshold[i]
threshold[i] = round(val + 0.1, 1)
if len(sampling['method']) == 1:
results(classes, stats_dict)
else:
return stats_dict
def compute_classes(classes, dict_classes, row, numbers_dict):
for i in classes:
dict_classes[str(i)] += int((float(row[i]) / 100) * params['global']['samples_size'] ** 2)
if row[-1] == 'trn':
numbers_dict['trn'] += 1
elif row[-1] == 'val':
numbers_dict['val'] += 1
elif row[-1] == 'tst':
numbers_dict['tst'] += 1
return numbers_dict, dict_classes
def parameters_search_dict(stats_dict, prop_classes, numbers_sample, source):
if all(value == 0 for value in prop_classes.values()):
return False
total_pixel = sum(prop_classes.values())
prop = [round(i / total_pixel * 100, 1) for i in prop_classes.values()]
std = round(np.std(prop), 3)
if std <= stats_dict['std'] or stats_dict['std'] == []:
if params['data_analysis']['sampling']['method'][0] == 'min_annotated_percent':
# adds 'map' value to stats_dict
stats_dict.update(map=source)
elif params['data_analysis']['sampling']['method'][0] == 'class_proportion':
# adds 'combination' value to stats_dict
stats_dict.update(combination=source)
# adds 'std' value to stats_dict
stats_dict.update(std=std)
# adds 'prop_class' value to stats_dict
for i in prop_classes:
stats_dict.update({'prop' + str(i): prop[int(i)]})
# appends 'trn', 'val', 'tst' values to stats_dict
stats_dict.update(trn_data=numbers_sample)
else:
return False
def results(classes, stats_dict):
if len(params['data_analysis']['sampling']['method']) == 1:
if params['data_analysis']['sampling']['method'][0] == 'min_annotated_percent':
logging.info(' ')
logging.info('optimal minimum annotated percent :', stats_dict['map'])
elif params['data_analysis']['sampling']['method'][0] == 'class_proportion':
logging.info(' ')
logging.info('optimal class threshold combination :', stats_dict['combination'])
elif len(params['data_analysis']['sampling']['method']) == 2:
if params['data_analysis']['sampling']['method'][1] == 'min_annotated_percent':
logging.info('optimal minimum annotated percent :', stats_dict['combination'])
elif params['data_analysis']['sampling']['method'][1] == 'class_proportion':
logging.info('optimal class threshold combination :', stats_dict['map'])
for i in classes:
logging.info('Pixels from class ', i, ' : ', stats_dict['prop' + str(i)], '%')
logging.info(stats_dict['trn_data'])
def parameters_search(sampling, sample_data, classes):
annotated_p = [i for i in range(0, 101)]
if sampling['method'][0] == 'min_annotated_percent':
stats = minimum_annotated_percent_search(classes, annotated_p, sampling, sample_data)
if len(sampling['method']) == 2:
sample = []
for row in sample_data:
if minimum_annotated_percent(row[0], stats['map']):
sample.append(row)
res = class_proportion_search(classes, sampling, sample)
logging.info('optimal minimum annotated percent :', stats['map'])
results(classes, res)
elif sampling['method'][0] == 'class_proportion':
stats = class_proportion_search(classes, sampling, sample_data)
if len(sampling['method']) == 2:
sample = []
for row in sample_data:
if class_proportion(row, classes, stats['combination']):
sample.append(row)
res = minimum_annotated_percent_search(classes, annotated_p, sampling, sample)
logging.info('optimal class threshold combination :', stats['combination'])
results(classes, res)
def main(params): # TODO: test this.
number_samples = {'trn': 0, 'val': 0, 'tst': 0}
prop_csv = params['global']['data_path'] + '/prop_data.csv'
sampling = params['data_analysis']['sampling_method']
if params['data_analysis']['create_csv']:
create_csv()
sample_data = genfromtxt(prop_csv, delimiter=',', dtype='|U8')
# creates pixel_classes dict and keys
classes = []
if "class_proportion" in sampling.keys():
pixel_classes = {key: 0 for key in sampling["class_proportion"].keys()}
classes = [int(key) for key in sampling["class_proportion"].keys()]
if params['data_analysis']['optimal_parameters_search']:
parameters_search(sampling, sample_data, classes)
else:
for row in sample_data:
if len(list(sampling.keys())) == 1:
if list(sampling.keys())[0] == 'min_annotated_percent':
if minimum_annotated_percent(row[0], sampling['map']):
# adds pixel count to pixel_classes dict for each class in the image
compute_classes(classes, pixel_classes, row, number_samples)
elif list(sampling.keys())[0] == 'class_proportion':
if class_proportion(row, classes, sampling):
# adds pixel count to pixel_classes dict for each class in the image
compute_classes(classes, pixel_classes, row, number_samples)
elif len(list(sampling.keys())) == 2:
if list(sampling.keys())[0] == 'min_annotated_percent':
if minimum_annotated_percent(row[0], sampling['map']):
if list(sampling.keys())[1] == 'class_proportion':
if class_proportion(row, classes, sampling):
compute_classes(classes, pixel_classes, row, number_samples)
elif list(sampling.keys())[0] == 'class_proportion':
if class_proportion(row, classes, sampling):
if list(sampling.keys())[1] == 'min_annotated_percent':
if minimum_annotated_percent(row[0], sampling['map']):
compute_classes(classes, pixel_classes, row, number_samples)
total_pixel = 0
for i in pixel_classes:
total_pixel += pixel_classes[i]
for i in pixel_classes:
logging.info('Pixels from class ', i, ' :', round(pixel_classes[i] / total_pixel * 100, 1), ' %')
logging.info(number_samples)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Sample preparation')
parser.add_argument('ParamFile', metavar='DIR',
help='Path to training parameters stored in yaml')
args = parser.parse_args()
params = read_parameters(args.ParamFile)
start_time = time.time()
debug = True if params['global']['debug_mode'] else False
main(params)
print("Elapsed time:{}".format(time.time() - start_time))