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bc971c7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 | import pandas as pd
import numpy as np
import torch
import json
from tqdm import tqdm
import os
import h5py
import csv
from os import listdir
from pathlib import Path
from modules.normalized_video_visualizer import normalize_megalist_frame
def fsl105_merge_metadata(root_dir, trim_indices):
final_dict = {}
with open(root_dir + "/labels.csv", mode="r") as infile:
with open(trim_indices, "r") as trimfile:
reader = csv.reader(infile)
trim_dict = json.load(trimfile)
next(reader)
for row in reader:
id_metadata = {"label": row[1], "category": row[2]}
final_dict.update({row[0]:id_metadata})
sign_video_list = listdir(root_dir + "/clips/" + row[0])
final_video_list = []
for x in sign_video_list:
video_instance = dict()
video_instance["filename"] = x
video_instance["filepath"] = root_dir + "/clips/" + str(row[0]) + "/" + x
remove = None
startsAt = None
endsAt = None
if row[0] in trim_dict:
if x in trim_dict[row[0]]:
remove = trim_dict[row[0]][x].get("remove")
startsAt = trim_dict[row[0]][x].get("start")
endsAt = trim_dict[row[0]][x].get("end")
video_instance["remove"] = remove
video_instance["startsAt"] = startsAt
video_instance["endsAt"] = endsAt
final_video_list.append(video_instance)
final_dict[row[0]].update({"instances":final_video_list})
with open("metadata/fsl105-labels.json", "w") as f:
f.write(json.dumps(final_dict, indent=2))
def get_valid_instances(filepath: str):
with open(filepath, "r") as file:
labels_dict = json.load(file)
for x in labels_dict.keys():
for y in labels_dict[x]["instances"]:
if y["remove"] != None:
labels_dict[x]["instances"].remove(y)
return labels_dict
def stream_npy_to_hdf5(source_dir, output_file):
source_path = Path(source_dir)
with h5py.File(output_file, "a") as hf:
class_dirs = [d for d in source_path.iterdir() if d.is_dir()]
for class_dir in class_dirs:
class_id = class_dir.name
grp = hf.require_group(class_id)
npy_files = list(class_dir.glob("*.npy"))
print(f"Processing class: {class_id} ({len(npy_files)} files)...")
for npy_path in npy_files:
file_name = npy_path.name
try:
features = np.load(npy_path)
arr = np.array(features, dtype="float32")
if file_name in grp:
del grp[file_name]
file_name = file_name.replace(".npy", "")
grp.create_dataset(file_name, data=arr, compression="gzip")
except Exception as e:
print(f"Error processing {npy_path}: {e}")
print(f"Final HDF5 file saved and closed at: {output_file}")
def process_and_save_normalized_hdf5(input_path, output_path):
if not os.path.exists(input_path):
print(f"Error: Source HDF5 not found at {input_path}")
return
with h5py.File(input_path, 'r') as source_hf, h5py.File(output_path, 'w') as target_hf:
for class_id in tqdm(source_hf.keys(), desc="Processing Classes"):
target_group = target_hf.create_group(class_id)
for file_key in source_hf[class_id].keys():
landmarks_raw = np.array(source_hf[class_id][file_key])[:, :, :2]
normalized_sequence = []
for frame_data in landmarks_raw:
b_norm, lh_norm, rh_norm = normalize_megalist_frame(frame_data)
combined_frame = torch.cat([b_norm, lh_norm, rh_norm], dim=0)
normalized_sequence.append(combined_frame.numpy())
final_data = np.array(normalized_sequence, dtype="float32")
target_group.create_dataset(
file_key,
data=final_data,
compression="gzip",
compression_opts=4
)
def flatten_samples_preserve_hierarchy(source_h5_path, target_h5_path, extraction_order):
mapping_logic = {}
for ds_name in ['pose', 'left_hand', 'right_hand', 'face']:
req = extraction_order[ds_name]
sorted_idx = sorted(list(set(req)))
idx_map = {idx: i for i, idx in enumerate(sorted_idx)}
reorder = np.array([idx_map[idx] for idx in req])
mapping_logic[ds_name] = {
'sorted': sorted_idx,
'reorder': reorder
}
with h5py.File(source_h5_path, 'r') as src, h5py.File(target_h5_path, 'w') as dst:
all_classes = list(src.keys())
for class_id in tqdm(all_classes, desc="Flattening Classes"):
class_group_dst = dst.create_group(class_id)
class_group_src = src[class_id]
for sample_name in class_group_src.keys():
sample_block = class_group_src[sample_name]
try:
parts = []
for ds_name in ['pose', 'left_hand', 'right_hand', 'face']:
logic = mapping_logic[ds_name]
data = sample_block[ds_name][:, logic['sorted'], :3]
data = data[:, logic['reorder'], :]
parts.append(data)
combined_tensor = np.concatenate(parts, axis=1).astype(np.float32)
class_group_dst.create_dataset(
sample_name,
data=combined_tensor,
compression="gzip",
chunks=True
)
except Exception as e:
print(f"Error processing {class_id}/{sample_name}: {e}")
print(f"\nProcessing complete. New hierarchy saved to: {target_h5_path}")
def apply_signbart_normalization(data):
pose_len = len(extraction_order['pose'])
lh_len = len(extraction_order['left_hand'])
rh_len = len(extraction_order['right_hand'])
part_indices = {
"body": (0, pose_len),
"lh": (pose_len, pose_len + lh_len),
"rh": (pose_len + lh_len, pose_len + lh_len + rh_len)
}
normalized = data.copy()
for name, (start, end) in part_indices.items():
part_data = normalized[:, start:end, :2]
mask = (part_data != 0).any(axis=-1)
if not np.any(mask):
continue
points = part_data[mask]
p_min = points.min(axis=0)
p_max = points.max(axis=0)
margin = (p_max - p_min) * 0.05
p_min -= margin
p_max += margin
range_val = p_max - p_min
range_val[range_val == 0] = 1.0
normalized[:, start:end, :2] = np.where(
normalized[:, start:end, :2] != 0,
(normalized[:, start:end, :2] - p_min) / range_val,
0
)
return normalized
def extract_video_data(pose_seq, face_seq, lh_seq, rh_seq, extraction_order):
def get_ordered_indices(indices, sequence):
requested_indices = extraction_order[indices]
sorted_indices = sorted(list(set(requested_indices)))
data_subset = sequence[:, sorted_indices, :]
index_map = {idx: i for i, idx in enumerate(sorted_indices)}
reorder_map = [index_map[idx] for idx in requested_indices]
return data_subset[:, reorder_map, :]
pose = get_ordered_indices("pose", pose_seq)[:, :, :2]
lh = get_ordered_indices("left_hand", lh_seq)[:, :, :2]
rh = get_ordered_indices("right_hand", rh_seq)[:, :, :2]
face = get_ordered_indices("face", face_seq)[:, :, :2]
combined = np.concatenate([pose, lh, rh, face], axis=1)
return combined
def flatten_samples_preserve_hierarchy(source_h5_path, target_h5_path, extraction_order):
mapping_logic = {}
for ds_name in ['pose', 'left_hand', 'right_hand', 'face']:
req = extraction_order[ds_name]
sorted_idx = sorted(list(set(req)))
idx_map = {idx: i for i, idx in enumerate(sorted_idx)}
reorder = np.array([idx_map[idx] for idx in req])
mapping_logic[ds_name] = {
'sorted': sorted_idx,
'reorder': reorder
}
with h5py.File(source_h5_path, 'r') as src, h5py.File(target_h5_path, 'w') as dst:
all_classes = list(src.keys())
for class_id in tqdm(all_classes, desc="Flattening Classes"):
class_group_dst = dst.create_group(class_id)
class_group_src = src[class_id]
for sample_name in class_group_src.keys():
sample_block = class_group_src[sample_name]
try:
parts = []
for ds_name in ['pose', 'left_hand', 'right_hand', 'face']:
logic = mapping_logic[ds_name]
data = sample_block[ds_name][:, logic['sorted'], :3]
data = data[:, logic['reorder'], :]
parts.append(data)
combined_tensor = np.concatenate(parts, axis=1).astype(np.float32)
class_group_dst.create_dataset(
sample_name,
data=combined_tensor,
compression="gzip",
chunks=True
)
except Exception as e:
print(f"Error processing {class_id}/{sample_name}: {e}")
print(f"\nProcessing complete. New hierarchy saved to: {target_h5_path}")
def apply_hybrid_normalization(data, extraction_order):
pose_len = len(extraction_order['pose'])
lh_len = len(extraction_order['left_hand'])
rh_len = len(extraction_order['right_hand'])
pose_start = 0
lh_start = pose_len
rh_start = lh_start + lh_len
face_start = rh_start + rh_len
FACE_REGIONS = {
"left_eye": [46, 52, 53, 65, 7, 159, 155, 145, 70, 107, 105, 22, 23, 24, 110, 157, 158],
"right_eye": [295, 283, 282, 276, 382, 386, 249, 374, 336, 300, 285, 252, 253, 254, 339, 384, 385],
"mouth": [324, 13, 78, 14, 61, 291, 37, 0, 267, 84, 17, 314, 308, 318, 402, 312, 178, 88, 95]
}
region_map = {}
for name, ids in FACE_REGIONS.items():
indices = [face_start + extraction_order['face'].index(lm_id)
for lm_id in ids if lm_id in extraction_order['face']]
region_map[name] = indices
normalized = data.copy()
for f in range(normalized.shape[0]):
frame = normalized[f]
try:
NOSE_I = pose_start + extraction_order['pose'].index(0)
L_SHOULDER_I = pose_start + extraction_order['pose'].index(11)
R_SHOULDER_I = pose_start + extraction_order['pose'].index(12)
L_EYE_I = face_start + extraction_order['face'].index(386)
shoulder_dist = np.linalg.norm(frame[L_SHOULDER_I, :2] - frame[R_SHOULDER_I, :2])
head_unit = shoulder_dist / 2.0
if head_unit > 1e-8:
nose_x = frame[NOSE_I, 0]
l_eye_y = frame[L_EYE_I, 1]
box_left = nose_x - (3 * head_unit)
box_right = nose_x + (3 * head_unit)
box_top = l_eye_y + (0.5 * head_unit)
box_bottom = l_eye_y - (6 * head_unit)
width = max(box_right - box_left, 1e-8)
height = max(box_top - box_bottom, 1e-8)
pose_indices = range(pose_start, lh_start)
for idx in pose_indices:
if not np.all(frame[idx, :2] == 0):
frame[idx, 0] = (frame[idx, 0] - box_left) / width - 0.5
frame[idx, 1] = (frame[idx, 1] - box_bottom) / height - 0.5
except ValueError:
pass
for start, end in [(lh_start, rh_start), (rh_start, face_start)]:
pts = frame[start:end, :2]
mask = (pts != 0).any(axis=-1)
if np.sum(mask) >= 2:
h_min, h_max = pts[mask].min(axis=0), pts[mask].max(axis=0)
side = np.max(h_max - h_min) * 1.2
if side > 1e-8:
center = (h_min + h_max) / 2.0
box_min = center - (side / 2.0)
frame[start:end, :2] = np.where(
frame[start:end, :2] != 0,
((frame[start:end, :2] - box_min) / side) - 0.5,
0
)
for region_name, indices in region_map.items():
pts = frame[indices, :2]
mask = (pts != 0).any(axis=-1)
if np.sum(mask) >= 2:
p_min, p_max = pts[mask].min(axis=0), pts[mask].max(axis=0)
side = np.max(p_max - p_min) * 1.2
if side > 1e-8:
center = (p_min + p_max) / 2.0
box_min = center - (side / 2.0)
frame[indices, :2] = np.where(
frame[indices, :2] != 0,
((frame[indices, :2] - box_min) / side) - 0.5,
0
)
normalized[f] = frame
return normalized
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