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prepare_adience.py
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prepare_adience.py
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import argparse
import os
from collections import defaultdict
from typing import Dict, List, Optional
import cv2
import pandas as pd
import tqdm
from mivolo.data.data_reader import PictureInfo, get_all_files
from mivolo.model.yolo_detector import Detector, PersonAndFaceResult
from preparation_utils import get_additional_bboxes, get_main_face, save_annotations
def read_adience_annotations(annotations_files):
annotations_per_image = {}
stat_per_fold = defaultdict(int)
cols = ["user_id", "original_image", "face_id", "age", "gender"]
for file in annotations_files:
fold_name = os.path.basename(file).split(".")[0]
df = pd.read_csv(file, sep="\t", usecols=cols)
for index, row in df.iterrows():
face_id, img_name, user_id = row["face_id"], row["original_image"], row["user_id"]
aligned_face_path = f"faces/{user_id}/coarse_tilt_aligned_face.{face_id}.{img_name}"
age, gender = row["age"], row["gender"]
gender = gender.upper() if isinstance(gender, str) and gender != "u" else None
age = age if isinstance(age, str) else None
annotations_per_image[aligned_face_path] = {"age": age, "gender": gender, "fold": fold_name}
stat_per_fold[fold_name] += 1
print(f"Per fold images: {stat_per_fold}")
return annotations_per_image
def read_data(images_dir, annotations_files, data_dir) -> List[PictureInfo]:
dataset_pictures: List[PictureInfo] = []
all_images = get_all_files(images_dir)
annotations_per_file = read_adience_annotations(annotations_files)
total, missed = 0, 0
stat_per_gender: Dict[str, int] = defaultdict(int)
missed_gender, missed_age, missed_gender_and_age = 0, 0, 0
stat_per_ages: Dict[str, int] = defaultdict(int)
# final age classes: '0;2', "4;6", "8;12", "15;20", "25;32", "38;43", "48;53", "60;100"
age_map = {
"2": "(0, 2)",
"3": "(0, 2)",
"13": "(8, 12)",
"(8, 23)": "(8, 12)",
"22": "(15, 20)",
"23": "(25, 32)",
"29": "(25, 32)",
"(27, 32)": "(25, 32)",
"32": "(25, 32)",
"34": "(25, 32)",
"35": "(25, 32)",
"36": "(38, 43)",
"(38, 42)": "(38, 43)",
"(38, 48)": "(38, 43)",
"42": "(38, 43)",
"45": "(38, 43)",
"46": "(48, 53)",
"55": "(48, 53)",
"56": "(48, 53)",
"57": "(60, 100)",
"58": "(60, 100)",
}
for image_path in all_images:
total += 1
relative_path = image_path.replace(f"{data_dir}/", "")
if relative_path not in annotations_per_file:
missed += 1
print("Can not find annotation for ", relative_path)
else:
annot = annotations_per_file[relative_path]
age, gender = annot["age"], annot["gender"]
if gender is None and age is not None:
missed_gender += 1
elif age is None and gender is not None:
missed_age += 1
elif gender is None and age is None:
missed_gender_and_age += 1
# skip such image
continue
if gender is not None:
stat_per_gender[gender] += 1
if age is not None:
age = age_map[age] if age in age_map else age
stat_per_ages[age] += 1
dataset_pictures.append(PictureInfo(image_path, age, gender))
print(f"Missed annots for images: {missed}/{total}")
print(f"Missed genders: {missed_gender}")
print(f"Missed ages: {missed_age}")
print(f"Missed ages and gender: {missed_gender_and_age}")
print(f"\nPer gender images: {stat_per_gender}")
ages = list(stat_per_ages.keys())
print(f"Per ages categories ({len(ages)} cats) :")
ages = sorted(ages, key=lambda x: int(x.split("(")[-1].split(",")[0].strip()))
for age in ages:
print(f"Age: {age} Count: {stat_per_ages[age]}")
return dataset_pictures
def main(faces_dir: str, annotations: List[str], data_dir: str, detector_cfg: dict = None):
"""
Generate a .txt annotation file with columns:
["img_name", "age", "gender",
"face_x0", "face_y0", "face_x1", "face_y1",
"person_x0", "person_y0", "person_x1", "person_y1"]
All person bboxes here will be set to [-1, -1, -1, -1]
If detector_cfg is set, for each face bbox will be refined using detector.
Also, other detected faces wil be written to txt file (needed for further preprocessing)
"""
# out directory for annotations
out_dir = os.path.join(data_dir, "annotations")
os.makedirs(out_dir, exist_ok=True)
# load annotations
images: List[PictureInfo] = read_data(faces_dir, annotations, data_dir)
if detector_cfg:
# detect faces with yolo detector
faces_not_found, images_with_other_faces = 0, 0
other_faces: List[PictureInfo] = []
detector_weights, device = detector_cfg["weights"], detector_cfg["device"]
detector = Detector(detector_weights, device, verbose=False, conf_thresh=0.1, iou_thresh=0.2)
for image_info in tqdm.tqdm(images, desc="Detecting faces: "):
cv_im = cv2.imread(image_info.image_path)
im_h, im_w = cv_im.shape[:2]
detected_objects: PersonAndFaceResult = detector.predict(cv_im)
main_bbox, other_bboxes_inds = get_main_face(detected_objects)
if main_bbox is None:
# use a full image as face bbox
faces_not_found += 1
image_info.bbox = [0, 0, im_w, im_h]
else:
image_info.bbox = main_bbox
if len(other_bboxes_inds):
images_with_other_faces += 1
additional_faces = get_additional_bboxes(detected_objects, other_bboxes_inds, image_info.image_path)
other_faces.extend(additional_faces)
print(f"Faces not detected: {faces_not_found}/{len(images)}")
print(f"Images with other faces: {images_with_other_faces}/{len(images)}")
print(f"Other faces: {len(other_faces)}")
images = images + other_faces
else:
# use a full image as face bbox
for image_info in tqdm.tqdm(images, desc="Collect face bboxes: "):
cv_im = cv2.imread(image_info.image_path)
im_h, im_w = cv_im.shape[:2]
image_info.bbox = [0, 0, im_w, im_h] # xyxy
save_annotations(images, faces_dir, out_file=os.path.join(out_dir, "adience_annotations.csv"))
def get_parser():
parser = argparse.ArgumentParser(description="Adience")
parser.add_argument(
"--dataset_path",
default="data/adience",
type=str,
required=True,
help="path to dataset with faces/ and fold_{i}_data.txt files",
)
parser.add_argument(
"--detector_weights", default=None, type=str, required=False, help="path to face and person detector"
)
parser.add_argument("--device", default="cuda:0", type=str, required=False, help="device to inference detector")
return parser
if __name__ == "__main__":
parser = get_parser()
args = parser.parse_args()
data_dir = args.dataset_path
faces_dir = os.path.join(data_dir, "faces")
if data_dir[-1] == "/":
data_dir = data_dir[:-1]
annotations = [
os.path.join(data_dir, "fold_0_data.txt"),
os.path.join(data_dir, "fold_1_data.txt"),
os.path.join(data_dir, "fold_2_data.txt"),
os.path.join(data_dir, "fold_3_data.txt"),
os.path.join(data_dir, "fold_4_data.txt"),
]
detector_cfg: Optional[Dict[str, str]] = None
if args.detector_weights is not None:
detector_cfg = {"weights": args.detector_weights, "device": "cuda:0"}
main(faces_dir, annotations, data_dir, detector_cfg)