Spaces:
Runtime error
Runtime error
File size: 23,011 Bytes
7a4b92f 592e4db 7a4b92f fbb3995 7a4b92f 592e4db 7a4b92f 592e4db 7a4b92f 81970c6 7a4b92f 81970c6 2a572c2 81970c6 7a4b92f 592e4db 7a4b92f 592e4db 7a4b92f 592e4db 7a4b92f 592e4db 7e4e0ac 592e4db 7a4b92f 592e4db a2bc65a 7edd774 a2bc65a 7a4b92f 592e4db 7a4b92f 592e4db 7a4b92f 592e4db a2bc65a 592e4db 7a4b92f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 |
import project_path
import numpy as np
import cv2
import os
from collections import namedtuple, defaultdict
import struct
from PIL import Image
from tqdm import tqdm
import datetime
from decimal import Decimal, ROUND_HALF_UP
import json
import pytz
from copy import deepcopy
from multiprocessing import Pool
import math
import lib.fish_eye.pyARIS as pyARIS
from lib.fish_eye.tracker import Tracker
BEAM_WIDTH_DIR = 'lib/fish_eye/beam_widths/'
ImageData = namedtuple('ImageData', [
'pixel_meter_size',
'xdim', 'ydim',
'x_meter_start', 'y_meter_start', 'x_meter_stop', 'y_meter_stop',
'sample_read_rows', 'sample_read_cols', 'image_write_rows', 'image_write_cols'
])
def FastARISRead(ARIS_data, start_frame, end_frame):
""" Just read in the ARIS frame, and not the other meta data.
"""
FrameSize = ARIS_data.SamplesPerChannel*ARIS_data.NumRawBeams
frames = np.empty([end_frame-start_frame, ARIS_data.SamplesPerChannel,
ARIS_data.NumRawBeams], dtype=np.uint8)
with open(ARIS_data.filename, 'rb') as data:
for i, j in enumerate(range(start_frame, end_frame)):
data.seek(j*(1024+(FrameSize))+2048, 0)
raw_data = struct.unpack("%dB" % FrameSize, data.read(FrameSize))
frames[i] = np.fliplr(np.reshape(
raw_data, [ARIS_data.SamplesPerChannel, ARIS_data.NumRawBeams]))
# Close the data file
data.close()
return frames
def get_info(aris_fp, beam_width_dir=BEAM_WIDTH_DIR):
"""
Return:
image_meter_width, image_meter_height, fps
"""
ARISdata, aris_frame = pyARIS.DataImport(aris_fp)
beam_width_data = pyARIS.load_beam_width_data(aris_frame, beam_width_dir=beam_width_dir)[0]
min_pixel_size = pyARIS.get_minimum_pixel_meter_size(aris_frame, beam_width_data)
sample_length = aris_frame.sampleperiod * 0.000001 * aris_frame.soundspeed / 2
pixel_meter_size = max(min_pixel_size, sample_length)
xdim, ydim, x_meter_start, y_meter_start, x_meter_stop, y_meter_stop = pyARIS.compute_image_bounds(
pixel_meter_size, aris_frame, beam_width_data,
additional_pixel_padding_x=0,
additional_pixel_padding_y=0
)
return pixel_meter_size * xdim, pixel_meter_size * ydim, aris_frame.framerate
def write_frames(aris_fp, out_dir, cb=None, max_mb=-1, beam_width_dir=BEAM_WIDTH_DIR, bg_out_dir=None, num_workers=0):
"""
Write all frames from an ARIS file to disk, using our 3-channel format:
(raw img, blurred & mean subtracted img, optical flow approximation)
Args:
aris_fp: path to aris file
out_dir: directory for frame extraction. frames will be named 0.jpg, 1.jpg, ... {n}.jpg
cb: a callback function for updating progress
max_mb: maximum amount of the file to be processed, in megabytes
beam_width_dir: location of ARIS camera information
bg_out_dir: where to write the background frame; None disables writing
Return:
(float) image_meter_width - the width of each image, in meters
(float) image_meter_height
(float) fps
"""
# Load in the ARIS file
ARISdata, aris_frame = pyARIS.DataImport(aris_fp)
if cb:
cb(2, msg="Decoding ARIS data...")
beam_width_data = pyARIS.load_beam_width_data(aris_frame, beam_width_dir=beam_width_dir)[0]
# What is the meter resolution of the smallest sample?
min_pixel_size = pyARIS.get_minimum_pixel_meter_size(aris_frame, beam_width_data)
# What is the meter resolution of the sample length?
sample_length = aris_frame.sampleperiod * 0.000001 * aris_frame.soundspeed / 2
# Choose the size of a pixel (or hard code it to some specific value)
pixel_meter_size = max(min_pixel_size, sample_length)
# Determine the image dimensions
xdim, ydim, x_meter_start, y_meter_start, x_meter_stop, y_meter_stop = pyARIS.compute_image_bounds(
pixel_meter_size, aris_frame, beam_width_data,
additional_pixel_padding_x=0,
additional_pixel_padding_y=0
)
# Compute the mapping from the samples to the image
sample_read_rows, sample_read_cols, image_write_rows, image_write_cols = pyARIS.compute_mapping_from_sample_to_image(
pixel_meter_size,
xdim, ydim, x_meter_start, y_meter_start,
aris_frame, beam_width_data
)
image_data = ImageData(
pixel_meter_size,
xdim, ydim, x_meter_start, y_meter_start, x_meter_stop, y_meter_stop,
sample_read_rows, sample_read_cols, image_write_rows, image_write_cols
)
start_frame = 0
end_frame = ARISdata.FrameCount
bytes_per_frame = 1024+ARISdata.SamplesPerChannel*ARISdata.NumRawBeams
print("ARIS bytes per frame", bytes_per_frame)
img_bytes_per_frame = image_data.ydim * image_data.xdim * 4 # for fp32 frames
print("Image bytes per frame", img_bytes_per_frame)
max_bytes = max(bytes_per_frame, img_bytes_per_frame)
if max_mb > 0:
max_frames = int(max_mb*1000000 / (max_bytes))
if end_frame > max_frames:
end_frame = max_frames
# use a max of 4gb per batch to avoid memory errors (16gb RAM on a g4dn.xlarge)
batch_size = 1000 # int(4000*1000000 / (max_bytes))
clips = [[pos, pos+batch_size+1] for pos in range(0, end_frame, batch_size)]
clips[-1][1] = ARISdata.FrameCount
print("Batch size:", batch_size)
with tqdm(total=(end_frame-start_frame-1), desc="Extracting frames", ncols=0) as pbar:
# compute info for bg subtraction using first batch
# TODO: make this a sliding window
mean_blurred_frame, mean_normalization_value = write_frame_range(ARISdata, image_data, out_dir, clips[0][0], clips[0][1], None, None, cb, pbar)
# do rest of batches in parallel
if num_workers > 0:
args = [ (ARISdata, image_data, out_dir, start, end, mean_blurred_frame, mean_normalization_value, cb) for (start, end) in clips[1:] ] # TODO: can't pass pbar to thread
with Pool(num_workers) as pool:
results = [ pool.apply_async(write_frame_range, arg) for arg in args ]
results = [ r.get() for r in results ] # need this call to block on thread execution
pbar.update(sum([ arg[4] - arg[3] for arg in args ]))
else:
for j, (start, end) in enumerate(clips[1:]):
write_frame_range(ARISdata, image_data, out_dir, start, end, mean_blurred_frame, mean_normalization_value, cb, pbar)
if bg_out_dir is not None:
bg_img = (mean_blurred_frame * 255).astype(np.uint8)
out_fp = os.path.join(bg_out_dir, 'bg_start.jpg')
Image.fromarray(bg_img).save(out_fp, quality=95)
return pixel_meter_size * xdim, pixel_meter_size * ydim, aris_frame.framerate
def write_frame_range(ARISdata, image_data, out_dir, start, end, mean_blurred_frame=None, mean_normalization_value=None, cb=None, pbar=None):
try:
frames = np.zeros([end-start, image_data.ydim, image_data.xdim], dtype=np.uint8)
frames[:, image_data.image_write_rows, image_data.image_write_cols] = FastARISRead(ARISdata, start, end)[:, image_data.sample_read_rows, image_data.sample_read_cols]
except:
print("Error extracting frames from", ARISdata.filename, "during batch", i)
return
blurred_frames = frames.astype(np.float32)
for i in range(frames.shape[0]):
blurred_frames[i] = cv2.GaussianBlur(
blurred_frames[i],
(5,5),
0
)
if mean_blurred_frame is None:
mean_blurred_frame = blurred_frames.mean(axis=0)
blurred_frames -= mean_blurred_frame
if mean_normalization_value is None:
mean_normalization_value = np.max(np.abs(blurred_frames))
blurred_frames /= mean_normalization_value
blurred_frames += 1
blurred_frames /= 2
# Because of the optical flow computation, we only go to end_frame - 1
for i, frame_offset in enumerate(range(start, end - 1)):
frame_image = np.dstack([
frames[i] / 255,
blurred_frames[i],
np.abs(blurred_frames[i+1] - blurred_frames[i])
]).astype(np.float32)
frame_image = (frame_image * 255).astype(np.uint8)
out_fp = os.path.join(out_dir, f'{start+i}.jpg') # = frame_offset.jpg?
Image.fromarray(frame_image).save(out_fp, quality=95)
if pbar:
pbar.update(1)
if cb:
pct = 2 + int( (start+i) / (end_frame - start_frame - 1) * 98)
cb(pct, msg=pbar.__str__())
return mean_blurred_frame, mean_normalization_value
def prep_for_mm(json_data):
"""Prepare json results for writing to a manual marking file."""
json_data = deepcopy(json_data)
# map fish id -> [ (bbox, frame_num), (bbox, frame_num), ... ]
tracks = defaultdict(list)
for frame in json_data['frames']:
for bbox in frame['fish']:
tracks[bbox['fish_id']].append((bbox['bbox'], frame['frame_num']))
# find frame number for manual marking
# look for first time a track crosses the center
# if it never crosses the center, use the closest box to the center
mm_frame_nums = {}
for f_id, track in tracks.items():
# keep track of frame closest to the center
closest_frame = 0
closest_dist = 1.0
for i, (box, frame) in enumerate(track):
x = (box[0] + box[2]) / 2.0
if i > 0:
last_x = (track[i-1][0][0] + track[i-1][0][2]) / 2.0
if (x < 0.5 and last_x >= 0.5) or (last_x < 0.5 and x >= 0.5):
closest_frame = frame
break
dist = abs(x - 0.5)
if dist < closest_dist:
closest_frame = frame
closest_dist = dist
mm_frame_nums[f_id] = closest_frame
# sort tracks by their frame numbers and re-key
# IDs are 1-indexed
id_frame = [ (k, v) for k,v in mm_frame_nums.items() ]
id_frame = sorted(id_frame, key=lambda x: x[1])
id_map = {}
for i, (f_id, frame) in enumerate(id_frame, start=1):
id_map[f_id] = i
# map IDs and keep frame['fish'] sorted by ID
for i, frame in enumerate(json_data['frames']):
new_frame_entries = []
for frame_entry in frame['fish']:
frame_entry['fish_id'] = id_map[frame_entry['fish_id']]
new_frame_entries.append(frame_entry)
frame['fish'] = sorted(new_frame_entries, key=lambda k: k['fish_id'])
# store manual marking frame and re-map 'fish' field
for fish in json_data['fish']:
fish['marking_frame'] = mm_frame_nums[fish['id']] # mm_frame_nums refers to old IDs
fish['id'] = id_map[fish['id']]
json_data['fish'] = sorted(json_data['fish'], key=lambda x: x['id'])
return json_data
def add_metadata_to_result(aris_fp, json_data, beam_width_dir=BEAM_WIDTH_DIR):
"""
Return:
dictionary, for manual marking
"""
metadata = {}
metadata["FILE_NAME"] = aris_fp
ARISdata, frame = pyARIS.DataImport(aris_fp)
metadata["FRAME_RATE"] = frame.framerate
# Load in the beam width information
beam_width_data, camera_type = pyARIS.load_beam_width_data(frame, beam_width_dir=beam_width_dir)
# What is the meter resolution of the smallest sample?
min_pixel_size = pyARIS.get_minimum_pixel_meter_size(frame, beam_width_data)
# What is the meter resolution of the sample length?
sample_length = frame.sampleperiod * 0.000001 * frame.soundspeed / 2
# Choose the size of a pixel
pixel_meter_size = max(min_pixel_size, sample_length)
# Determine the image dimensions
xdim, ydim, x_meter_start, y_meter_start, x_meter_stop, y_meter_stop = pyARIS.compute_image_bounds(
pixel_meter_size, frame, beam_width_data,
additional_pixel_padding_x=0,
additional_pixel_padding_y=0
)
# Compute the mapping from the samples to the image
sample_read_rows, sample_read_cols, image_write_rows, image_write_cols = pyARIS.compute_mapping_from_sample_to_image(
pixel_meter_size,
xdim, ydim, x_meter_start, y_meter_start,
frame, beam_width_data
)
marking_mapping = dict(zip(zip(image_write_rows, image_write_cols),
zip(sample_read_rows, sample_read_cols)))
# Manual marking format rounds 0.5 to 1 instead of 0 in IEEE 754
def round(number, ndigits=0):
return float(Decimal(number).quantize(ndigits, ROUND_HALF_UP))
right, left, none = Tracker.count_dirs(json_data)
metadata["UPSTREAM_FISH"] = left # TODO
metadata["DOWNSTREAM_FISH"] = right # TODO
metadata["NONDIRECTIONAL_FISH"] = none # TODO
metadata["TOTAL_FISH"] = metadata["UPSTREAM_FISH"] + metadata["DOWNSTREAM_FISH"] + metadata["NONDIRECTIONAL_FISH"]
metadata["TOTAL_FRAMES"] = ARISdata.FrameCount
metadata["EXPECTED_FRAMES"] = -1 # What is this?
metadata["TOTAL_TIME"] = str(datetime.timedelta(seconds=round(metadata["TOTAL_FRAMES"]/metadata["FRAME_RATE"])))
metadata["EXPECTED_TIME"] = str(datetime.timedelta(seconds=round(metadata["EXPECTED_FRAMES"]/metadata["FRAME_RATE"])))
metadata["UPSTREAM_MOTION"] = 'Right To Left' or 'Left To Right' #TODO
metadata["COUNT_FILE_NAME"] = 'N/A'
metadata["EDITOR_ID"] = 'N/A'
metadata["INTENSITY"] = f'{round(frame.intensity, 1):.1f} dB' # Missing
metadata["THRESHOLD"] = f'{round(frame.threshold, 1):.1f} dB' # Missing
metadata["WINDOW_START"] = round(frame.windowstart, 2)
metadata["WINDOW_END"] = round(frame.windowstart + frame.windowlength, 2)
metadata["WATER_TEMP"] = f'{int(round(frame.watertemp))} degC'
s = f''''''
upstream_motion_map = {}
if (metadata["UPSTREAM_MOTION"] == 'Left To Right'):
upstream_motion_map = {
'right': ' Up',
'left': 'Down',
'none': ' N/A',
}
elif (metadata["UPSTREAM_MOTION"] == 'Right To Left'):
upstream_motion_map = {
'left': ' Up',
'right': 'Down',
'none': ' N/A',
}
def get_entry(fish):
if 'marking_frame' in fish:
frame_num = fish['marking_frame']
entry = None
for json_frame in json_data['frames']:
if json_frame['frame_num'] == frame_num:
for json_frame_entry in json_frame['fish']:
if json_frame_entry['fish_id'] == fish['id']:
json_frame_entry = json_frame_entry.copy()
json_frame_entry['frame_num'] = frame_num
return json_frame_entry
else:
print("Warning: JSON not correctly formatted for manual marking creation. Use aris.prep_for_mm()")
entries = []
for json_frame in json_data['frames']:
for json_frame_entry in json_frame['fish']:
if json_frame_entry['fish_id'] == fish['id']:
entries.append({'frame_num': json_frame['frame_num'], **json_frame_entry})
entry = entries[len(entries)//2]
return entry
print("Error, could not find entry for", fish)
return None # TODO better error handling
entries = []
for fish in json_data['fish']:
entry = get_entry(fish)
entry['length'] = fish['length']*100
entry['direction'] = fish['direction']
entry['travel_dist'] = fish['travel_dist']
entry['start_frame_index'] = fish['start_frame_index']
entry['end_frame_index'] = fish['end_frame_index']
entries.append(entry)
metadata["FISH"] = []
for entry in sorted(entries, key=lambda x: x['fish_id']):
frame_num = entry['frame_num']
frame = pyARIS.FrameRead(ARISdata, frame_num)
y = (entry['bbox'][1]+entry['bbox'][3])/2
x = (entry['bbox'][0]+entry['bbox'][2])/2
h = np.max(image_write_rows)
w = np.max(image_write_cols)
# TODO actually fix this
try:
bin_num, beam_num = marking_mapping[(round(y*h), round(x*w))]
except:
bin_num = 0
beam_num = 0
fish_entry = {}
fish_entry['FILE'] = 1
fish_entry['TOTAL'] = entry['fish_id']
fish_entry['FRAME_NUM'] = entry['frame_num']
fish_entry['START_FRAME'] = entry['start_frame_index']
fish_entry['END_FRAME'] = entry['end_frame_index']
fish_entry['NBR_FRAMES'] = entry['end_frame_index'] + 1 - entry['start_frame_index']
fish_entry['TRAVEL'] = entry['travel_dist']
fish_entry['DIR'] = upstream_motion_map[entry['direction']]
fish_entry['R'] = bin_num * pixel_meter_size + frame.windowstart
fish_entry['THETA'] = beam_width_data['beam_center'][beam_num]
fish_entry['L'] = entry['length']
fish_entry['DR'] = -1.0 # What is this?
fish_entry['LDR'] = -1.0 # What is this?
fish_entry['ASPECT'] = -1.0 # What is this?
TIME, DATE = datetime.datetime.fromtimestamp(frame.sonartimestamp/1000000, pytz.timezone('UTC')).strftime('%H:%M:%S %Y-%m-%d').split()
fish_entry['TIME'] = TIME
fish_entry['DATE'] = DATE
fish_entry['LATITUDE'] = frame.latitude or 'N 00 d 0.00000 m'
fish_entry['LONGITUDE'] = frame.longitude or 'E 000 d 0.00000 m'
fish_entry['PAN'] = frame.sonarpan
if math.isnan(fish_entry['PAN']): fish_entry['PAN'] = "nan"
fish_entry['TILT'] = frame.sonartilt
if math.isnan(fish_entry['TILT']): fish_entry['TILT'] = "nan"
fish_entry['ROLL'] = frame.roll # May be wrong number but sonarroll was NaN
fish_entry['SPECIES'] = 'Unknown'
fish_entry['MOTION'] = 'Running <-->'
fish_entry['Q'] = -1 #5 # I don't know what this is or where it comes from
fish_entry['N'] = -1 #1 # I don't know what this is or where it comes from
fish_entry['COMMENT'] = ''
metadata["FISH"].append(fish_entry)
# What are these?
# Maybe the date and time range for the recording?
first_frame = pyARIS.FrameRead(ARISdata, 0)
last_frame = pyARIS.FrameRead(ARISdata, metadata["TOTAL_FRAMES"]-1)
start_time, start_date = datetime.datetime.fromtimestamp(first_frame.sonartimestamp/1000000, pytz.timezone('UTC')).strftime('%H:%M:%S %Y-%m-%d').split()
end_time, end_date = datetime.datetime.fromtimestamp(last_frame.sonartimestamp/1000000, pytz.timezone('UTC')).strftime('%H:%M:%S %Y-%m-%d').split()
metadata["DATE"] = start_date
metadata["START"] = start_time
metadata["END"] = end_time
json_data['metadata'] = metadata
return json_data
def create_metadata_table(result, table_headers, info_headers):
if 'metadata' in result:
metadata = result['metadata']
else:
metadata = { 'FISH': [] }
# Calculate detection dropout
for fish in metadata['FISH']:
count = 0
for frame in result['frames'][fish['START_FRAME']:fish['END_FRAME']+1]:
for ann in frame['fish']:
if ann['fish_id'] == fish['TOTAL']:
count += 1
fish['DETECTION_DROPOUT'] = 1 - count / (fish['END_FRAME'] + 1 - fish['START_FRAME'])
# Create fish table
table = []
for fish in metadata["FISH"]:
row = []
for header in table_headers:
row.append(fish[header])
table.append(row)
if len(metadata["FISH"]) == 0:
row = []
for header in table_headers:
row.append("-")
table.append(row)
# Create info table
info = []
for field in info_headers:
field_name = "**" + field + "**"
if field in metadata:
info.append([field_name, str(metadata[field])])
else:
info.append([field_name, ""])
if 'hyperparameters' in metadata:
for param_name in metadata['hyperparameters']:
info.append(['**' + param_name + '**', str(metadata['hyperparameters'][param_name])])
return table, info
def create_manual_marking(results, out_path=None):
"""
Return:
string, full contents of manual marking
"""
metadata = results['metadata']
s = f'''
Total Fish = {metadata["TOTAL_FISH"]}
Upstream = {metadata["UPSTREAM_FISH"]}
Downstream = {metadata["DOWNSTREAM_FISH"]}
?? = {metadata["NONDIRECTIONAL_FISH"]}
Total Frames = {metadata["TOTAL_FRAMES"]}
Expected Frames = {metadata["EXPECTED_FRAMES"]}
Total Time = {metadata["TOTAL_TIME"]}
Expected Time = {metadata["EXPECTED_TIME"]}
Upstream Motion = {metadata["UPSTREAM_MOTION"]}
Count File Name: {metadata["COUNT_FILE_NAME"]}
Editor ID = {metadata["EDITOR_ID"]}
Intensity = {metadata["INTENSITY"]}
Threshold = {metadata["THRESHOLD"]}
Window Start = {metadata["WINDOW_START"]:.2f}
Window End = {metadata["WINDOW_END"]:.2f}
Water Temperature = {metadata["WATER_TEMP"]}
*** Manual Marking (Manual Sizing: Q = Quality, N = Repeat Count) ***
File Total Frame# Dir R (m) Theta L(cm) dR(cm) L/dR Aspect Time Date Latitude Longitude Pan Tilt Roll Species Motion Q N Comment
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
'''
for fish in metadata["FISH"]:
entry = {}
for field in fish.keys():
if fish[field] == "nan":
entry[field] = math.nan
else:
entry[field] = fish[field]
s += f'{entry["FILE"]:>4} {entry["TOTAL"]:>5} {entry["FRAME_NUM"]:>6} {entry["DIR"]:>3} {entry["R"]:>6.2f} {entry["THETA"]:>6.1f} {entry["L"]:>6.1f} {entry["DR"]:>6.1f} {entry["LDR"]:>6.2f} {entry["ASPECT"]:>6.1f} {entry["TIME"]:>8} {entry["DATE"]:>10} {entry["LATITUDE"]:>17} {entry["LONGITUDE"]:>18} {entry["PAN"]:>7.2f} {entry["TILT"]:>7.2f} {entry["ROLL"]:>7.2f} {entry["SPECIES"]:>8} {entry["MOTION"]:>37} {entry["Q"]:>5} {entry["N"]:>2} {entry["COMMENT"]}\n'
s += f'''
*** Source File Key ***
1. Source File Name: {metadata["FILE_NAME"]}
Source File Date: {metadata["DATE"]}
Source File Start: {metadata["START"]}
Source File End: {metadata["END"]}
Settings
Upstream: {metadata["UPSTREAM_MOTION"]}
Default Mark Direction: Upstream
Editor ID: {metadata["EDITOR_ID"]}
Show Marks: ??
Show marks for ?? seconds
Loop for ?? seconds
'''
if out_path:
with open(out_path, 'w') as f:
f.write(s)
return s |