def main():
# print "generate_distribution_csv_files"
directory_path_list = [
# "./clustering_results/k_means/complete_video/",
# "./clustering_results/k_means/video/",
# "./clustering_results/k_means/shot/",
# "./clustering_results/birch/complete_video/",
# "./clustering_results/birch/video/",
# "./clustering_results/db_scan/complete_video/",
# "./clustering_results/db_scan/video/",
# "./clustering_results/mean_shift/complete_video/",
# "./clustering_results/mean_shift/video/",
"./clustering_results/k-medoid/shot/"
]
clear("./evaluation_results")
for directory_path in directory_path_list:
evaluation_manager = EvaluationManager(directory_path)
clustering_results_file_name_list = utl.get_file_name_list(directory_path)
for file_name in clustering_results_file_name_list:
if file_name != "merged-with-features" \
and file_name != "cluster-per-category" \
and file_name != "category-per-cluster":
print ("generating " + file_name)
evaluation_manager.generate_distribution_files(file_name)
evaluation_manager.generate_precision_files(file_name)
print ("---------------------------------------------------------------")
evaluation_manager.generate_mean_average_precision_files()
evaluation_manager.generate_accuracy_files()
print "--------------------------------------------------------------"
def add_category_to_test_data():
video_with_category_dictionary = get_video_with_category_dictionary("category_with_video.csv")
test_directory_path = "C:/code/features/input/structure_analysis/test/"
for i in range(1, 5):
directory = test_directory_path + str(i) + "/"
file_name_list = utility.get_file_name_list(directory)
iteration = 1
max_value = len(file_name_list)
print ""
print "normalizing " + directory
for file_name in file_name_list:
input_path = directory + file_name
output_path = test_directory_path + "normalized/" + str(i) + "/" + file_name
add_category_to_xml(input_path, output_path, file_name, video_with_category_dictionary)
utility.print_progress_bar(iteration, max_value)
iteration += 1
def generate_complete_video_merged_files_normalized():
features_file_path = "./features/normalized_complete_video_features.csv"
clustering_results_file_path = "./clustering_results/k_means/complete_video/normalized-intra-inter/"
output_file_path = "./mean-average-precision/k_means/complete_video/normalized-intra-inter/merged-with-features/"
clustering_results_file_name_list = utl.get_file_name_list(
clustering_results_file_path)
for clustering_result_file_name in clustering_results_file_name_list:
if clustering_result_file_name != "merged-with-features":
print("generating " + clustering_result_file_name)
merge_features_with_clustering_results_normalized(
features_file_path,
clustering_results_file_path + clustering_result_file_name,
output_file_path + clustering_result_file_name)
print(
"---------------------------------------------------------------"
)
def __generate_mean_average_precision_category_per_cluster_csv(self):
print "generate_mean_average_precision_category_per_cluster_csv..."
precision_category_per_cluster_directory = self.__input_directory_path.replace("clustering_results",
"evaluation_results") \
+ "precision_category_per_cluster/"
file_name_list = utl.get_file_name_list(precision_category_per_cluster_directory)
accuracy_list = {}
max_value = len(file_name_list)
for file_name in file_name_list:
key = file_name.replace(".csv", "") \
.replace("_k_means", "") \
.replace("_birch", "") \
.replace("_mean_shift", "") \
.replace("_db_scan", "").replace("k_medoids_results_", "")
category_per_cluster_csv = precision_category_per_cluster_directory + file_name
accuracy_list[int(key)] = EvaluationManager.__get_mean_average_precision(category_per_cluster_csv)
output_path = precision_category_per_cluster_directory.replace("precision_category_per_cluster/", "")
with open(output_path + "mean_average_precision_category_per_cluster.csv", 'wb') as f:
the_writer = csv.writer(f)
headers = [
"file_name",
"map",
]
the_writer.writerow(headers)
iteration = 1
accuracy_list = collections.OrderedDict(
sorted(accuracy_list.items()))
for key, accuracy in accuracy_list.iteritems():
vector = [key, accuracy]
the_writer.writerow(vector)
utl.print_progress_bar(iteration, max_value)
iteration += 1
f.close()
print("")
print("csv file has been created successfully")
f_name = f_name.replace(substring_to_remove, "")
return f_name
def rename_file(directory, old_name, new_name):
old_file = os.path.join(directory, old_name)
new_file = os.path.join(directory, new_name)
os.rename(directory + str(old_name), directory + str(new_name))
# for folder in folders:
#
# print ("rename: " + folder)
#
# file_name_list = utility.get_file_name_list(folder)
# for old_name in file_name_list:
# print ("old name: " + old_name)
# new_name = rename(str(old_name))
# print ("new name: " + new_name)
# rename_file(folder, old_name, new_name)
# print "-----------------------------------------"
folder = "./"
file_name_list = utility.get_file_name_list(folder)
for old_name in file_name_list:
print ("old name: " + old_name)
new_name = rename(str(old_name))
print ("new name: " + new_name)
rename_file(folder, old_name, new_name)
print "-----------------------------------------"
import utility
import csv
from feature_manager import FeatureManager
directory_path = "C:/code/features/output/video/structure-analysis-video-dev/"
feature_files = utility.get_file_name_list(directory_path)
feature_vector_list = []
iteration = 1
max_value = len(feature_files)
for file_name in feature_files:
fm = FeatureManager(directory_path, file_name)
feature_vector_list = feature_vector_list + fm.get_feature_vector_list()
utility.print_progress_bar(iteration, max_value)
iteration += 1
print "start csv"
with open('complete_video_features.csv', 'wb') as f:
the_writer = csv.writer(f)
headers = [
"video", "shot_number", "category", "interactions_number_speakers_2",
"interactions_number_speakers_3", "interactions_number_speakers_4",
"interactions_number_speakers_4+", "intervention_short",
"intervention_long", "speakers_type_ponctuel",
"speakers_type_localise", "speakers_type_present",
"speakers_type_regulier", "speakers_type_important",
"speaker_distribution", "mean_number_of_faces", "std_number_of_faces",
"inter_intensity_variation1", "inter_intensity_variation2",
"inter_intensity_variation3", "inter_intensity_variation4",
"inter_intensity_variation5", "inter_intensity_variation6",
"inter_intensity_variation7", "inter_intensity_variation8",
"inter_intensity_variation9", "intra_intensity_variation1",
"intra_intensity_variation2", "intra_intensity_variation3",
import combineLowLevelFeatures as combineLowLevelFeatures
import utility
input_directory_path = "C:/code/features/input/structure_analysis/test/1/"
output_directory_path = "C:/code/features/output/video/structure-analysis-video-test/"
shots_directory_path = "C:/code/features/input/shots_files/test_normalized/"
shot_file_name_list = utility.get_file_name_list(shots_directory_path)
generated_files = utility.get_file_name_list(output_directory_path)
feature_files = utility.get_file_name_list(input_directory_path)
print input_directory_path
i = 1
l = len(feature_files)
for shot_file_name in shot_file_name_list:
shot_list = utility.get_shots(shots_directory_path + shot_file_name)
if len(
shot_list
) > 0 and shot_file_name not in generated_files and shot_file_name in feature_files:
input_file_path = input_directory_path + shot_file_name
output_file_path = output_directory_path + shot_file_name
combineLowLevelFeatures.generate_percentage_descriptors(
input_file_path, output_file_path)
utility.print_progress_bar(i, l)
i += 1
# max_shot_duration: 11367.0
# max_shot_word_length: 7640
# 255
import utility
output_directory_path = "C:/features/structure-analysis-shots-test/"
output_file_name_list = utility.get_file_name_list(output_directory_path)
max_shot_duration = 0
max_shot_word_length = 0
i = 0
l = len(output_file_name_list)
for file_name in output_file_name_list:
i = i + 1
utility.print_progress_bar(i, l)
