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1_generate_csv_from_motion_area.py
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1_generate_csv_from_motion_area.py
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import os
import glob
import pandas as pd
# module level variables ##############################################################################################
# train and test directories
TRAINING_IMAGES_DIR = os.getcwd() + "/training_images/"
TEST_IMAGES_DIR = os.getcwd() + "/test_images/"
MIN_NUM_IMAGES_REQUIRED_FOR_TRAINING = 10
MIN_NUM_IMAGES_SUGGESTED_FOR_TRAINING = 100
MIN_NUM_IMAGES_REQUIRED_FOR_TESTING = 3
# output .csv file names/locations
TRAINING_DATA_DIR = os.getcwd() + "/" + "training_data"
TRAIN_CSV_FILE = "train_labels-%s.csv"
EVAL_CSV_FILE = "eval_labels-%s.csv"
#######################################################################################################################
def main():
# for path in glob.glob('/home/ralf/git/github.com/zwotzie/Raspberry-Motion-OpenCV/training_images/*'):
# classifiction = path.split('/')[-1]
# convert training xml data to a single .csv file
print("converting xml training data . . .")
path_to_csv(TRAINING_IMAGES_DIR, TRAIN_CSV_FILE)
print("training xml to .csv conversion successful, saved result to " + TRAINING_DATA_DIR)
# convert test xml data to a single .csv file
print("converting xml test data . . .")
path_to_csv(TEST_IMAGES_DIR, EVAL_CSV_FILE)
print("test xml to .csv conversion successful, saved result to " + TRAINING_DATA_DIR)
# end main
#######################################################################################################################
def path_to_csv(path, to_csv_pattern):
column_name = ['classification', 'filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax']
attribute_list = []
for file in glob.glob(path + '/*/*.jpg'):
basepath, basename = os.path.split(file)
basepath, classification = os.path.split(basepath)
# https://motion-project.github.io/motion_config.html#conversion_specifiers
# %w width of the image
# %h height of the image
# %i width of motion area
# %J height of motion area
# %K X coordinates of motion center
# %L Y coordinates of motion center
# expect filename like: 'Camera1_10-51-53.02.192.132.178.582.815.jpg' (%Y-%m-%d/%{dbeventid}/%H-%M-%S.%q.%{dbeventid}.%i.%J.%K.%L)
# 10-59-20.02.231.1124.810.424.793.jpg
# 0 1 2 3 4 5 6
# => ,1600,1200,mouse,-138,231,986,1355
splits = basename.split('.')
# hour = int(splits[0])
# frame = int(splits[1])
# event_id = int(splits[2])
area_width = int(splits[3])
area_height = int(splits[4])
center_x = int(splits[5])
center_y = int(splits[6])
xmin = int(center_x - area_width / 2)
xmax = int(center_x + area_width / 2)
ymin = int(center_y - area_height / 2)
ymax = int(center_y + area_height / 2)
# motion sometimes produces negative values:
if xmin < 0:
xmin = 0
if xmax < 0:
xmax = 0
if ymin < 0:
ymin = 0
if ymax < 0:
ymax = 0
# fix setting...
width = 1600
height = 1200
attribute_list.append((classification, classification+'/'+basename, width, height, classification, xmin, ymin, xmax, ymax))
# end for
pre_file_df = pd.DataFrame(attribute_list, columns=column_name)
for classification in pre_file_df.classification.unique():
print("CLASS=%s" % classification)
file_df = pre_file_df.loc[pre_file_df['classification'] == classification]
file_df = file_df.drop(['classification'], axis=1)
csv_filename = to_csv_pattern % classification
csv_filename = os.path.join(TRAINING_DATA_DIR, csv_filename)
file_df.to_csv(csv_filename, index=None)
# end function
#######################################################################################################################
if __name__ == "__main__":
main()