diff --git a/database/create_database_dataframe.py b/database/create_database_dataframe.py
index 05bfedb90ab97bbbf39bf160fb9e4a2831d1d1f9..91304f58035f495c68f4865348c6df6073b6c87a 100644
--- a/database/create_database_dataframe.py
+++ b/database/create_database_dataframe.py
@@ -22,6 +22,7 @@ import argparse
import pandas as pd
from bob.bio.face.annotator import MTCNN
from bob.io.base import load
+import csv
def detect_faces(frame, min_face_size):
"""_summary_
@@ -35,59 +36,197 @@ def detect_faces(frame, min_face_size):
landmarks = landmarks.numpy()
return nb_detected_faces, probs, boxes, landmarks
-def create_metadata_file(frames_list, output_path, metadata_filename, save_mode, min_face_size):
+def create_metadata_file(frames_list, output_path, metadata_filename, min_face_size):
# Check if output path exists
if not os.path.exists(output_path):
os.makedirs(output_path)
+ metadata_bonafide = []
+ metadata_print = []
+ metadata_replay = []
+ metadata_hygmask = []
+
# Open the file containing the list of frames
- my_file = open(frames_list, "r")
- metadata = []
-
- for line in my_file.readlines():
- path = os.path.split(line)[0]
- filename = os.path.split(line)[-1].strip()
- filename_split = filename.split("_")
- subject_id = filename_split[0]
- recording_device = filename_split[1]
- camera = filename_split[2]
- attack_type = filename_split[3]
- scenario = filename_split[4]
- replay_device = filename_split[5]
- source_video = filename_split[6]
- session = filename_split[7]
- frame_nb = filename_split[-1].split(".")[0].split("-")[-1]
- reference_id = '_'.join((filename_split[0])) # Should be replaced by template_id
- frame = load(line)
- nb_detected_faces, probs, boxes, landmarks = detect_faces(frame, min_face_size)
-
- metadata.append(
- {
- 'path': os.path.join(path, filename),
- 'key': os.path.join(subject_id, filename), # the key is used to save files when checkpointing is set to True
- 'filename': filename,
- 'subject_id': int(subject_id),
- 'reference_id': reference_id, # Should be replaced by template_id
- 'camera': camera,
- 'recording_device': recording_device,
- 'replay_device': replay_device,
- 'source_video': source_video,
- 'scenario': scenario,
- 'session': session,
- 'attack_type': attack_type,
- 'frame_nb': frame_nb,
- 'no_faces': nb_detected_faces,
- 'probs_faces': probs,
- 'boxes': boxes,
- 'landmarks': landmarks,
- }
- )
-
- my_file.close()
- # Put your list in a dataframe
- df = pd.DataFrame(metadata)
- # Save the dataframe into pickle
- df.to_pickle(os.path.join(output_path, '.'.join((metadata_filename, 'pkl'))))
+ with open(frames_list, 'r') as my_file:
+ csvreader = csv.reader(my_file)
+ for row in csvreader:
+ row = row[0]
+ # import ipdb; ipdb.set_trace()
+ # Check if the string contains the word bonafide (case insensitive)
+ if "bonafide" in row.lower():
+ metadata_bonafide.append(create_dataframe_bonafide(row, min_face_size))
+ # Check if the string contains the word print (case insensitive)
+ elif "print" in row.lower():
+ metadata_print.append(create_dataframe_print(row, min_face_size))
+ # Check if the string contains the word replay (case insensitive)
+ elif "replay" in row.lower():
+ metadata_replay.append(create_dataframe_replay(row, min_face_size))
+ # Check if the string contains the word hygmask (case insensitive)
+ elif "hyg-mask" in row.lower():
+ metadata_hygmask.append(create_dataframe_hygmask(row, min_face_size))
+ else:
+ raise ValueError("The path does not contain any information about the type of the data: {bonafide, print, replay, hygmask}")
+
+ # Put your bonafide list in a dataframe
+ df = pd.DataFrame(metadata_bonafide)
+ # Save the bonafide dataframe into pickle
+ df.to_pickle(os.path.join(output_path, '.'.join((metadata_filename + "_bonafide", 'pkl'))))
+
+ # Put your print list in a dataframe
+ df = pd.DataFrame(metadata_print)
+ # Save the print dataframe into pickle
+ df.to_pickle(os.path.join(output_path, '.'.join((metadata_filename + "_print", 'pkl'))))
+
+ # Put your replay list in a dataframe
+ df = pd.DataFrame(metadata_replay)
+ # Save the replay dataframe into pickle
+ df.to_pickle(os.path.join(output_path, '.'.join((metadata_filename + "_replay", 'pkl'))))
+
+ # Put your hygmask list in a dataframe
+ df = pd.DataFrame(metadata_hygmask)
+ # Save the hygmask dataframe into pickle
+ df.to_pickle(os.path.join(output_path, '.'.join((metadata_filename + "_hygmask", 'pkl'))))
+
+
+def create_dataframe_bonafide(full_path, min_face_size):
+ path = os.path.split(full_path)[0]
+ filename = os.path.split(full_path)[-1].strip()
+ filename_split = filename.split("_")
+ subject_id = filename_split[0]
+ recording_device = filename_split[1]
+ camera = filename_split[2]
+ attack_type = ""
+ scenario = filename_split[4]
+ session = filename_split[5]
+ frame_nb = filename_split[-1].split(".")[0].split("-")[-1]
+ reference_id = '_'.join((filename_split[0])) # Should be replaced by template_id
+ frame = load(full_path)
+ nb_detected_faces, probs, boxes, landmarks = detect_faces(frame, min_face_size)
+
+ return {
+ 'path': os.path.join(path, filename),
+ 'key': os.path.join(subject_id, filename), # the key is used to save files when checkpointing is set to True
+ 'filename': filename,
+ 'subject_id': int(subject_id),
+ 'reference_id': reference_id, # Should be replaced by template_id
+ 'camera': camera,
+ 'recording_device': recording_device,
+ 'replay_device': "",
+ 'source_video': "",
+ 'scenario': scenario,
+ 'session': session,
+ 'attack_type': attack_type,
+ 'frame_nb': frame_nb,
+ 'no_faces': nb_detected_faces,
+ 'probs_faces': probs,
+ 'boxes': boxes,
+ 'landmarks': landmarks,
+ }
+
+def create_dataframe_print(full_path, min_face_size):
+ path = os.path.split(full_path)[0]
+ filename = os.path.split(full_path)[-1].strip()
+ filename_split = filename.split("_")
+ subject_id = filename_split[0]
+ recording_device = filename_split[1]
+ camera = filename_split[2]
+ attack_type = filename_split[4]
+ frame_nb = filename_split[-1].split(".")[0].split("-")[-1]
+ reference_id = '_'.join((filename_split[0])) # Should be replaced by template_id
+ frame = load(full_path)
+ nb_detected_faces, probs, boxes, landmarks = detect_faces(frame, min_face_size)
+
+ return {
+ 'path': os.path.join(path, filename),
+ 'key': os.path.join(subject_id, filename), # the key is used to save files when checkpointing is set to True
+ 'filename': filename,
+ 'subject_id': int(subject_id),
+ 'reference_id': reference_id, # Should be replaced by template_id
+ 'camera': camera,
+ 'recording_device': recording_device,
+ 'replay_device': "",
+ 'source_video': "",
+ 'scenario': "",
+ 'session': "",
+ 'attack_type': attack_type,
+ 'frame_nb': frame_nb,
+ 'no_faces': nb_detected_faces,
+ 'probs_faces': probs,
+ 'boxes': boxes,
+ 'landmarks': landmarks,
+ }
+
+def create_dataframe_replay(full_path, min_face_size):
+ path = os.path.split(full_path)[0]
+ filename = os.path.split(full_path)[-1].strip()
+ filename_split = filename.split("_")
+ subject_id = filename_split[0]
+ recording_device = filename_split[1]
+ camera = filename_split[2]
+ attack_type = filename_split[4]
+ scenario = filename_split[5]
+ replay_device = filename_split[6]
+ source_video = filename_split[7]
+ session = filename_split[8]
+ frame_nb = filename_split[-1].split(".")[0].split("-")[-1]
+ reference_id = '_'.join((filename_split[0])) # Should be replaced by template_id
+ frame = load(full_path)
+ nb_detected_faces, probs, boxes, landmarks = detect_faces(frame, min_face_size)
+
+ return {
+ 'path': os.path.join(path, filename),
+ 'key': os.path.join(subject_id, filename), # the key is used to save files when checkpointing is set to True
+ 'filename': filename,
+ 'subject_id': int(subject_id),
+ 'reference_id': reference_id, # Should be replaced by template_id
+ 'camera': camera,
+ 'recording_device': recording_device,
+ 'replay_device': replay_device,
+ 'source_video': source_video,
+ 'scenario': scenario,
+ 'session': session,
+ 'attack_type': attack_type,
+ 'frame_nb': frame_nb,
+ 'no_faces': nb_detected_faces,
+ 'probs_faces': probs,
+ 'boxes': boxes,
+ 'landmarks': landmarks,
+ }
+
+def create_dataframe_hygmask(full_path, min_face_size):
+ path = os.path.split(full_path)[0]
+ filename = os.path.split(full_path)[-1].strip()
+ filename_split = filename.split("_")
+ subject_id = filename_split[0]
+ recording_device = filename_split[1]
+ camera = filename_split[2]
+ attack_type = filename_split[4]
+ scenario = filename_split[5]
+ frame_nb = filename_split[-1].split(".")[0].split("-")[-1]
+ reference_id = subject_id # Should be replaced by template_id
+ frame = load(full_path)
+ nb_detected_faces, probs, boxes, landmarks = detect_faces(frame, min_face_size)
+
+ return {
+ 'path': os.path.join(path, filename),
+ 'key': os.path.join(subject_id, filename), # the key is used to save files when checkpointing is set to True
+ 'filename': filename,
+ 'subject_id': int(subject_id),
+ 'reference_id': reference_id, # Should be replaced by template_id
+ 'camera': camera,
+ 'recording_device': recording_device,
+ 'replay_device': "",
+ 'source_video': "",
+ 'scenario': scenario,
+ 'session': "",
+ 'attack_type': attack_type,
+ 'frame_nb': frame_nb,
+ 'no_faces': nb_detected_faces,
+ 'probs_faces': probs,
+ 'boxes': boxes,
+ 'landmarks': landmarks,
+ }
+
if __name__ == '__main__':
@@ -102,4 +241,4 @@ if __name__ == '__main__':
# Parse the arguments
args = parser.parse_args()
- create_metadata_file(args.frames_list, args.output_path, args.metadata_filename, args.save_mode, args.min_face_size)
+ create_metadata_file(args.frames_list, args.output_path, args.metadata_filename, args.min_face_size)
diff --git a/frames.csv b/frames.csv
deleted file mode 100644
index 047f6ac245813fa01c4deff25b40b01efd8a03ce..0000000000000000000000000000000000000000
--- a/frames.csv
+++ /dev/null
@@ -1,319 +0,0 @@
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-0.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-1.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-2.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-3.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-4.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-5.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-6.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-7.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-8.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-9.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-10.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-11.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-12.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-13.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-14.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-15.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-16.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-17.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-18.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/1/1_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-19-15_frame-19.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/2/2_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-21-40_frame-0.jpg
-/idiap/temp/akomaty/trash/data/hyg_mask_attack/2/2_Redmi6pro_Front_ATTACK_hyg-mask_set-4_2023-02-41T15-21-40_frame-1.jpg
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diff --git a/pipeline_vuln.py b/pipeline_vuln.py
index 71ad2c172012ced63a642fbc215f945bb50613a7..56c6341604d94461e84d3b1437b4a0f4dd6badb4 100644
--- a/pipeline_vuln.py
+++ b/pipeline_vuln.py
@@ -29,11 +29,11 @@ def run_pipeline(root, bonafide_annotations_path, print_path, replay_path, hyg_a
# Create a references dataframe containing only files from the first session, the main camera only, and the first scenario: indoor normal light
session_id = "s1"
- scene_id = 0
+ scene = "indoor-normal-light"
nb_of_frames = 4 # the number of frames to be selected
- protocol_references_dev = '((session==@session_id) & (frame_nb < @nb_of_frames) & (scenario_id == @scene_id) & (with_mask==False) &(subject_id <= 70))'
- protocol_references_eval = '((session==@session_id) & (frame_nb < @nb_of_frames) & (scenario_id == @scene_id) & (with_mask==False) &(subject_id > 69))'
+ protocol_references_dev = '((session==@session_id) & (frame_nb < @nb_of_frames) & (scenario == @scene) &(subject_id <= 3))'
+ protocol_references_eval = '((session==@session_id) & (frame_nb < @nb_of_frames) & (scenario == @scene) &(subject_id > 3))'
## Dataframes for references, dev and eval
references_df_dev=df.query(protocol_references_dev)
@@ -42,8 +42,8 @@ def run_pipeline(root, bonafide_annotations_path, print_path, replay_path, hyg_a
# Create a probes dataframe containing only files from the second session, the main camera only, and the first scenario: indoor normal light
session_id = "s2"
- protocol_probes_dev = '((session==@session_id) & (frame_nb < @nb_of_frames) & (scenario_id == @scene_id) & (with_mask==False) & (subject_id <= 70))'
- protocol_probes_eval = '((session==@session_id) & (frame_nb < @nb_of_frames) & (scenario_id == @scene_id) & (with_mask==False) & (subject_id > 69))'
+ protocol_probes_dev = '((session==@session_id) & (frame_nb < @nb_of_frames) & (scenario == @scene) & (subject_id <= 3))'
+ protocol_probes_eval = '((session==@session_id) & (frame_nb < @nb_of_frames) & (scenario == @scene) & (subject_id > 3))'
probes_df_dev=df.query(protocol_probes_dev)
probes_df_eval=df.query(protocol_probes_eval)
@@ -58,8 +58,8 @@ def run_pipeline(root, bonafide_annotations_path, print_path, replay_path, hyg_a
# Add the print attack frames to the probes dataframe
df_print = load_annotations(print_path)
- protocol_probes_dev_print = '((frame_nb < @nb_of_frames) & (subject_id <= 70))'
- protocol_probes_eval_print = '((frame_nb < @nb_of_frames) & (subject_id > 69))'
+ protocol_probes_dev_print = '((frame_nb < @nb_of_frames) & (subject_id <= 3))'
+ protocol_probes_eval_print = '((frame_nb < @nb_of_frames) & (subject_id > 3))'
probes_df_dev_print=df_print.query(protocol_probes_dev_print)
probes_df_eval_print=df_print.query(protocol_probes_eval_print)
@@ -79,8 +79,8 @@ def run_pipeline(root, bonafide_annotations_path, print_path, replay_path, hyg_a
rep_att_scene = '1'
- protocol_probes_dev_replay = '((frame_nb < @nb_of_frames) & (rep_att_scenario == @rep_att_scene) & (subject_id <= 70))'
- protocol_probes_eval_replay = '((frame_nb < @nb_of_frames) & (rep_att_scenario == @rep_att_scene) & (subject_id > 69))'
+ protocol_probes_dev_replay = '((frame_nb < @nb_of_frames) & (scenario == @rep_att_scene) & (subject_id <= 3))'
+ protocol_probes_eval_replay = '((frame_nb < @nb_of_frames) & (scenario == @rep_att_scene) & (subject_id > 3))'
probes_df_dev_replay=df_replay.query(protocol_probes_dev_replay)
probes_df_eval_replay=df_replay.query(protocol_probes_eval_replay)
@@ -98,8 +98,8 @@ def run_pipeline(root, bonafide_annotations_path, print_path, replay_path, hyg_a
# Add the hygienic mask attack frames to the probes dataframe
df_hyg = load_annotations(hyg_attack_path)
- protocol_probes_dev_hyg = '(subject_id <= 70)'
- protocol_probes_eval_hyg = '(subject_id > 69)'
+ protocol_probes_dev_hyg = '(subject_id <= 3)'
+ protocol_probes_eval_hyg = '(subject_id > 3)'
probes_df_dev_hyg=df_hyg.query(protocol_probes_dev_hyg)
probes_df_eval_hyg=df_hyg.query(protocol_probes_eval_hyg)
@@ -168,7 +168,6 @@ def run_pipeline(root, bonafide_annotations_path, print_path, replay_path, hyg_a
matching_dev=matching_dev,
original_directory=root)
- from bob.pipelines.config.distributed.sge_demanding import dask_client
from bob.extension.log import setup, set_verbosity_level
logger = setup('bob')
@@ -176,8 +175,7 @@ def run_pipeline(root, bonafide_annotations_path, print_path, replay_path, hyg_a
pipeline = iresnet_template()
- # dask_client = "single-threaded"
- bob.bio.base.pipelines.entry_points.execute_pipeline_simple(pipeline, soteria_db, dask_client = dask_client, groups=["dev", "eval"], output=OUTPUT_PATH, dask_n_partitions=None, write_metadata_scores=True, checkpoint=True, dask_partition_size=16, dask_n_workers=None)
+ bob.bio.base.pipelines.entry_points.execute_pipeline_simple(pipeline, soteria_db, dask_client = "single-threaded", groups=["dev", "eval"], output=OUTPUT_PATH, dask_n_partitions=None, write_metadata_scores=True, checkpoint=True, dask_partition_size=16, dask_n_workers=None)
if __name__ == '__main__':
diff --git a/preprocessor/extract_frames.py b/preprocessor/extract_frames.py
index 30b0cd3d32f121e42d7d2f54abb65809814ad428..3dd74a586a736ae8a8104967e6b0845da50bbb0a 100644
--- a/preprocessor/extract_frames.py
+++ b/preprocessor/extract_frames.py
@@ -24,7 +24,7 @@ from bob.bio.video import VideoAsArray
from bob.io.image import to_matplotlib
def extract_frames(video_list, output_path, max_number_of_frames):
-
+ frames_list = []
with open(video_list, 'r') as fp:
for video_path in fp:
video_path = video_path.strip()
@@ -38,8 +38,14 @@ def extract_frames(video_list, output_path, max_number_of_frames):
index = 0
for frame in myvid:
dest_file_path = os.path.join(id_path, filename + "_frame-" + str(index)+ '.jpg')
+ frames_list.append(dest_file_path)
matplotlib.image.imsave(dest_file_path, to_matplotlib(frame))
index +=1
+ # save the frames list to a csv file
+ with open(os.path.join(output_path, "frames.csv"), 'w') as f:
+ for item in frames_list:
+ f.write("%s\n" % item)
+
if __name__ == '__main__':
# Create an ArgumentParser object
diff --git a/videos.txt b/test_videos.txt
similarity index 77%
rename from videos.txt
rename to test_videos.txt
index 91e2d14905ec5cfa974960b6d67b456984a933c9..f67d05384a01be4f53d87f4b1bebfac75776fd4e 100644
--- a/videos.txt
+++ b/test_videos.txt
@@ -11,6 +11,10 @@
/idiap/resource/database/PHyMAtt/attack/replay_attack/3_iPhone6s_front_ATTACK_replay-attack_1_iPhone12_Redmi9A_s1.mp4
/idiap/resource/database/PHyMAtt/attack/replay_attack/4_iPhone12_front_ATTACK_replay-attack_1_iPhone6s_Redmi9A_s1.mp4
/idiap/resource/database/PHyMAtt/bonafide/1_iPhone12_Front_BONAFIDE_indoor-normal-light_s1_2022-09-09T12-07-58.mp4
+/idiap/resource/database/PHyMAtt/bonafide/1_iPhone12_Front_BONAFIDE_indoor-normal-light_s2_2022-10-25T12-29-24.mp4
/idiap/resource/database/PHyMAtt/bonafide/2_iPhone12_Front_BONAFIDE_indoor-normal-light_s1_2022-09-09T12-21-16.mp4
+/idiap/resource/database/PHyMAtt/bonafide/2_iPhone12_Front_BONAFIDE_indoor-normal-light_s2_2022-10-25T12-15-40.mp4
/idiap/resource/database/PHyMAtt/bonafide/3_iPhone12_Front_BONAFIDE_indoor-normal-light_s1_2022-09-09T12-33-49.mp4
-/idiap/resource/database/PHyMAtt/bonafide/4_iPhone12_Front_BONAFIDE_indoor-normal-light_s1_2022-09-12T07-20-18.mp4
\ No newline at end of file
+/idiap/resource/database/PHyMAtt/bonafide/3_iPhone12_Front_BONAFIDE_indoor-normal-light_s2_2022-10-25T12-29-44.mp4
+/idiap/resource/database/PHyMAtt/bonafide/4_iPhone12_Front_BONAFIDE_indoor-normal-light_s1_2022-09-12T07-20-18.mp4
+/idiap/resource/database/PHyMAtt/bonafide/4_iPhone12_Front_BONAFIDE_indoor-normal-light_s2_2022-10-04T14-15-33.mp4
\ No newline at end of file
diff --git a/utils/_utils.py b/utils/_utils.py
index d9cb8abcb94a6e647b9db498575dd712bff93483..d9bd5851d67c0d3ba2f212b7e934a8ba32408e85 100644
--- a/utils/_utils.py
+++ b/utils/_utils.py
@@ -141,7 +141,8 @@ def create_matchings_for_attacks(probes_df):
probes (pandas dataframe): _description_
references (set of reference_ids (template_ids)): _description_
"""
- id = probes_df['filename'].str.split('_').str[0] + '_s1'
+ # id = probes_df['filename'].str.split('_').str[0] + '_s1'
+ id = probes_df['filename'].str.split('_').str[0]
matching = probes_df['filename']
matching = pd.DataFrame(matching)
matching.columns = ["probe_reference_id"]
diff --git a/utils/load_annotations.py b/utils/load_annotations.py
index da498ba384c0d8c4c1ce8b86fd7def3c11563102..0a12d4d6429c846a9b124c2b0d4e1d6b0ae61b18 100644
--- a/utils/load_annotations.py
+++ b/utils/load_annotations.py
@@ -11,10 +11,18 @@ from utils import _utils
import pandas as pd
def load_annotations(annotations_path):
- frames = [ _utils.process_df(os.path.join(annotations_path, f)) for f in os.listdir(annotations_path)]
- df = pd.concat(frames)
- df['frame_nb'] = df['frame_nb'].astype(int)
- return df
+ # check if annoation_path is a file or a directory
+ if os.path.isfile(annotations_path):
+ df = _utils.process_df(annotations_path)
+ df['frame_nb'] = df['frame_nb'].astype(int)
+ return df
+ elif os.path.isdir(annotations_path):
+ frames = [ _utils.process_df(os.path.join(annotations_path, f)) for f in os.listdir(annotations_path)]
+ df = pd.concat(frames)
+ df['frame_nb'] = df['frame_nb'].astype(int)
+ return df
+ else:
+ raise ValueError("The annotations_path is neither a file nor a directory.")
if __name__ == '__main__':