Spaces:
Running
Running
File size: 34,944 Bytes
9acc552 99cd14f 9acc552 99cd14f 9acc552 99cd14f 7ee620d 9acc552 99cd14f 9acc552 3733e70 9acc552 8f8ef33 9acc552 8f8ef33 9acc552 8f8ef33 9acc552 8f8ef33 9acc552 8f8ef33 9acc552 3733e70 9acc552 4b41e60 9acc552 3733e70 9acc552 3733e70 9acc552 3733e70 8f8ef33 4b41e60 3733e70 4b41e60 8f8ef33 3733e70 8f8ef33 3733e70 8f8ef33 9acc552 99cd14f 9acc552 5f721d1 4b41e60 9acc552 5f721d1 9acc552 5f721d1 8f8ef33 9acc552 4b41e60 9acc552 8f8ef33 9acc552 8f8ef33 9acc552 8f8ef33 4b41e60 9acc552 4b41e60 9acc552 4b41e60 8f8ef33 4b41e60 3733e70 4b41e60 99cd14f 4b41e60 3733e70 4b41e60 5f721d1 9acc552 99cd14f 9acc552 99cd14f 9acc552 8f8ef33 4b41e60 5f721d1 8f8ef33 5f721d1 8f8ef33 3733e70 99cd14f 3733e70 99cd14f 7ee620d 99cd14f 7ee620d 99cd14f 7ee620d 2921e2e 7ee620d 2921e2e 7ee620d 99cd14f 7ee620d 99cd14f 7ee620d 99cd14f 7ee620d 99cd14f 7ee620d 99cd14f 7ee620d 99cd14f 7ee620d 99cd14f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 |
import base64
from io import BytesIO
import io
import os
import sys
import cv2
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import streamlit as st
import torch
import tempfile
from PIL import Image
from torchvision.transforms.functional import to_pil_image
from torchvision import transforms
from PIL import ImageOps
import altair as alt
import streamlit.components.v1 as components
from torchcam.methods import CAM
from torchcam import methods as torchcam_methods
from torchcam.utils import overlay_mask
import os.path as osp
root_path = osp.abspath(osp.join(__file__, osp.pardir))
sys.path.append(root_path)
from preprocessing.dataset_creation import EyeDentityDatasetCreation
from utils import get_model
CAM_METHODS = ["CAM"]
# colors = ["#2ca02c", "#d62728", "#1f77b4", "#ff7f0e"] # Green, Red, Blue, Orange
colors = ["#1f77b4", "#ff7f0e", "#636363"] # Blue, Orange, Gray
@torch.no_grad()
def load_model(model_configs, device="cpu"):
"""Loads the pre-trained model."""
model_path = os.path.join(root_path, model_configs["model_path"])
model_dict = torch.load(model_path, map_location=device)
model = get_model(model_configs=model_configs)
model.load_state_dict(model_dict)
model = model.to(device).eval()
return model
def extract_frames(video_path):
"""Extracts frames from a video file."""
vidcap = cv2.VideoCapture(video_path)
frames = []
success, image = vidcap.read()
while success:
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
frames.append(image_rgb)
success, image = vidcap.read()
vidcap.release()
return frames
def resize_frame(image, max_width=640, max_height=480):
if not isinstance(image, Image.Image):
image = Image.fromarray(image)
original_size = image.size
# Resize the frame similarly to the image resizing logic
if original_size[0] == original_size[1] and original_size[0] >= 256:
max_size = (256, 256)
else:
max_size = list(original_size)
if original_size[0] >= max_width:
max_size[0] = max_width
elif original_size[0] < 64:
max_size[0] = 64
if original_size[1] >= max_height:
max_size[1] = max_height
elif original_size[1] < 32:
max_size[1] = 32
image.thumbnail(max_size)
# image = image.resize(max_size)
return image
def is_image(file_extension):
"""Checks if the file is an image."""
return file_extension.lower() in ["png", "jpeg", "jpg"]
def is_video(file_extension):
"""Checks if the file is a video."""
return file_extension.lower() in ["mp4", "avi", "mov", "mkv", "webm"]
def get_codec_and_extension(file_format):
"""Return codec and file extension based on the format."""
if file_format == "mp4":
return "H264", ".mp4"
elif file_format == "avi":
return "MJPG", ".avi"
elif file_format == "webm":
return "VP80", ".webm"
else:
return "MJPG", ".avi"
def display_results(input_image, cam_frame, pupil_diameter, cols):
"""Displays the input image and overlayed CAM result."""
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
axs[0].imshow(input_image)
axs[0].axis("off")
axs[0].set_title("Input Image")
axs[1].imshow(cam_frame)
axs[1].axis("off")
axs[1].set_title("Overlayed CAM")
cols[-1].pyplot(fig)
cols[-1].text(f"Pupil Diameter: {pupil_diameter:.2f} mm")
def preprocess_image(input_img, max_size=(256, 256)):
"""Resizes and preprocesses an image."""
input_img.thumbnail(max_size)
preprocess_steps = [
transforms.ToTensor(),
transforms.Resize([32, 64], interpolation=transforms.InterpolationMode.BICUBIC, antialias=True),
]
return transforms.Compose(preprocess_steps)(input_img).unsqueeze(0)
def overlay_text_on_frame(frame, text, position=(16, 20)):
"""Write text on the image frame using OpenCV."""
return cv2.putText(frame, text, position, cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1, cv2.LINE_AA)
def get_configs(blink_detection=False):
upscale = "-"
upscale_method_or_model = "-"
if upscale == "-":
sr_configs = None
else:
sr_configs = {
"method": upscale_method_or_model,
"params": {"upscale": upscale},
}
config_file = {
"sr_configs": sr_configs,
"feature_extraction_configs": {
"blink_detection": blink_detection,
"upscale": upscale,
"extraction_library": "mediapipe",
},
}
return config_file
def setup(cols, pupil_selection, tv_model, output_path):
left_pupil_model = None
left_pupil_cam_extractor = None
right_pupil_model = None
right_pupil_cam_extractor = None
output_frames = {}
input_frames = {}
predicted_diameters = {}
pred_diameters_frames = {}
if pupil_selection == "both":
selected_eyes = ["left_eye", "right_eye"]
elif pupil_selection == "left_pupil":
selected_eyes = ["left_eye"]
elif pupil_selection == "right_pupil":
selected_eyes = ["right_eye"]
for i, eye_type in enumerate(selected_eyes):
model_configs = {
"model_path": root_path + f"/pre_trained_models/{tv_model}/{eye_type}.pt",
"registered_model_name": tv_model,
"num_classes": 1,
}
if eye_type == "left_eye":
left_pupil_model = load_model(model_configs)
left_pupil_cam_extractor = None
output_frames[eye_type] = []
input_frames[eye_type] = []
predicted_diameters[eye_type] = []
pred_diameters_frames[eye_type] = []
else:
right_pupil_model = load_model(model_configs)
right_pupil_cam_extractor = None
output_frames[eye_type] = []
input_frames[eye_type] = []
predicted_diameters[eye_type] = []
pred_diameters_frames[eye_type] = []
video_placeholders = {}
if output_path:
video_cols = cols[1].columns(len(input_frames.keys()))
for i, eye_type in enumerate(list(input_frames.keys())):
video_placeholders[eye_type] = video_cols[i].empty()
return (
selected_eyes,
input_frames,
output_frames,
predicted_diameters,
pred_diameters_frames,
video_placeholders,
left_pupil_model,
left_pupil_cam_extractor,
right_pupil_model,
right_pupil_cam_extractor,
)
def process_frames(
cols, input_imgs, tv_model, pupil_selection, cam_method, output_path=None, codec=None, blink_detection=False
):
config_file = get_configs(blink_detection)
face_frames = []
(
selected_eyes,
input_frames,
output_frames,
predicted_diameters,
pred_diameters_frames,
video_placeholders,
left_pupil_model,
left_pupil_cam_extractor,
right_pupil_model,
right_pupil_cam_extractor,
) = setup(cols, pupil_selection, tv_model, output_path)
ds_creation = EyeDentityDatasetCreation(
feature_extraction_configs=config_file["feature_extraction_configs"],
sr_configs=config_file["sr_configs"],
)
preprocess_steps = [
transforms.Resize(
[32, 64],
interpolation=transforms.InterpolationMode.BICUBIC,
antialias=True,
),
transforms.ToTensor(),
]
preprocess_function = transforms.Compose(preprocess_steps)
eyes_ratios = []
for idx, input_img in enumerate(input_imgs):
img = np.array(input_img)
ds_results = ds_creation(img)
left_eye = None
right_eye = None
blinked = False
eyes_ratio = None
if ds_results is not None and "face" in ds_results:
face_img = to_pil_image(ds_results["face"])
has_face = True
else:
face_img = to_pil_image(np.zeros((256, 256, 3), dtype=np.uint8))
has_face = False
face_frames.append({"has_face": has_face, "img": face_img})
if ds_results is not None and "eyes" in ds_results.keys():
blinked = ds_results["eyes"]["blinked"]
eyes_ratio = ds_results["eyes"]["eyes_ratio"]
if eyes_ratio is not None:
eyes_ratios.append(eyes_ratio)
if "left_eye" in ds_results["eyes"].keys() and ds_results["eyes"]["left_eye"] is not None:
left_eye = ds_results["eyes"]["left_eye"]
left_eye = to_pil_image(left_eye).convert("RGB")
left_eye = preprocess_function(left_eye)
left_eye = left_eye.unsqueeze(0)
if "right_eye" in ds_results["eyes"].keys() and ds_results["eyes"]["right_eye"] is not None:
right_eye = ds_results["eyes"]["right_eye"]
right_eye = to_pil_image(right_eye).convert("RGB")
right_eye = preprocess_function(right_eye)
right_eye = right_eye.unsqueeze(0)
else:
input_img = preprocess_function(input_img)
input_img = input_img.unsqueeze(0)
if pupil_selection == "left_pupil":
left_eye = input_img
elif pupil_selection == "right_pupil":
right_eye = input_img
else:
left_eye = input_img
right_eye = input_img
for i, eye_type in enumerate(selected_eyes):
if blinked:
if left_eye is not None and eye_type == "left_eye":
_, height, width = left_eye.squeeze(0).shape
input_image_pil = to_pil_image(left_eye.squeeze(0))
elif right_eye is not None and eye_type == "right_eye":
_, height, width = right_eye.squeeze(0).shape
input_image_pil = to_pil_image(right_eye.squeeze(0))
input_img_np = np.array(input_image_pil)
zeros_img = to_pil_image(np.zeros((height, width, 3), dtype=np.uint8))
output_img_np = overlay_text_on_frame(np.array(zeros_img), "blink")
predicted_diameter = "blink"
else:
if left_eye is not None and eye_type == "left_eye":
if left_pupil_cam_extractor is None:
if tv_model == "ResNet18":
target_layer = left_pupil_model.resnet.layer4[-1].conv2
elif tv_model == "ResNet50":
target_layer = left_pupil_model.resnet.layer4[-1].conv3
else:
raise Exception(f"No target layer available for selected model: {tv_model}")
left_pupil_cam_extractor = torchcam_methods.__dict__[cam_method](
left_pupil_model,
target_layer=target_layer,
fc_layer=left_pupil_model.resnet.fc,
input_shape=left_eye.shape,
)
output = left_pupil_model(left_eye)
predicted_diameter = output[0].item()
act_maps = left_pupil_cam_extractor(0, output)
activation_map = act_maps[0] if len(act_maps) == 1 else left_pupil_cam_extractor.fuse_cams(act_maps)
input_image_pil = to_pil_image(left_eye.squeeze(0))
elif right_eye is not None and eye_type == "right_eye":
if right_pupil_cam_extractor is None:
if tv_model == "ResNet18":
target_layer = right_pupil_model.resnet.layer4[-1].conv2
elif tv_model == "ResNet50":
target_layer = right_pupil_model.resnet.layer4[-1].conv3
else:
raise Exception(f"No target layer available for selected model: {tv_model}")
right_pupil_cam_extractor = torchcam_methods.__dict__[cam_method](
right_pupil_model,
target_layer=target_layer,
fc_layer=right_pupil_model.resnet.fc,
input_shape=right_eye.shape,
)
output = right_pupil_model(right_eye)
predicted_diameter = output[0].item()
act_maps = right_pupil_cam_extractor(0, output)
activation_map = (
act_maps[0] if len(act_maps) == 1 else right_pupil_cam_extractor.fuse_cams(act_maps)
)
input_image_pil = to_pil_image(right_eye.squeeze(0))
# Create CAM overlay
activation_map_pil = to_pil_image(activation_map, mode="F")
result = overlay_mask(input_image_pil, activation_map_pil, alpha=0.5)
input_img_np = np.array(input_image_pil)
output_img_np = np.array(result)
# Add frame and predicted diameter to lists
input_frames[eye_type].append(input_img_np)
output_frames[eye_type].append(output_img_np)
predicted_diameters[eye_type].append(predicted_diameter)
if output_path:
height, width, _ = output_img_np.shape
frame = np.zeros((height, width, 3), dtype=np.uint8)
if not isinstance(predicted_diameter, str):
text = f"{predicted_diameter:.2f}"
else:
text = predicted_diameter
frame = overlay_text_on_frame(frame, text)
pred_diameters_frames[eye_type].append(frame)
combined_frame = np.vstack((input_img_np, output_img_np, frame))
img_base64 = pil_image_to_base64(Image.fromarray(combined_frame))
image_html = f'<div style="width: {str(50*len(selected_eyes))}%;"><img src="data:image/png;base64,{img_base64}" style="width: 100%;"></div>'
video_placeholders[eye_type].markdown(image_html, unsafe_allow_html=True)
# video_placeholders[eye_type].image(combined_frame, use_column_width=True)
st.session_state.current_frame = idx + 1
txt = f"<p style='font-size:20px;'> Number of Frames Processed: <strong>{st.session_state.current_frame} / {st.session_state.total_frames}</strong> </p>"
st.session_state.frame_placeholder.markdown(txt, unsafe_allow_html=True)
if output_path:
combine_and_show_frames(
input_frames, output_frames, pred_diameters_frames, output_path, codec, video_placeholders
)
return input_frames, output_frames, predicted_diameters, face_frames, eyes_ratios
# Function to display video with autoplay and loop
def display_video_with_autoplay(video_col, video_path, width):
video_html = f"""
<video width="{str(width)}%" height="auto" autoplay loop muted>
<source src="data:video/mp4;base64,{video_path}" type="video/mp4">
</video>
"""
video_col.markdown(video_html, unsafe_allow_html=True)
def process_video(cols, video_frames, tv_model, pupil_selection, output_path, cam_method, blink_detection=False):
resized_frames = []
for i, frame in enumerate(video_frames):
input_img = resize_frame(frame, max_width=640, max_height=480)
resized_frames.append(input_img)
file_format = output_path.split(".")[-1]
codec, extension = get_codec_and_extension(file_format)
input_frames, output_frames, predicted_diameters, face_frames, eyes_ratios = process_frames(
cols, resized_frames, tv_model, pupil_selection, cam_method, output_path, codec, blink_detection
)
return input_frames, output_frames, predicted_diameters, face_frames, eyes_ratios
# Function to convert string values to float or None
def convert_diameter(value):
try:
return float(value)
except (ValueError, TypeError):
return None # Return None if conversion fails
def combine_and_show_frames(input_frames, cam_frames, pred_diameters_frames, output_path, codec, video_cols):
# Assuming all frames have the same keys (eye types)
eye_types = input_frames.keys()
for i, eye_type in enumerate(eye_types):
in_frames = input_frames[eye_type]
cam_out_frames = cam_frames[eye_type]
pred_diameters_text_frames = pred_diameters_frames[eye_type]
# Get frame properties (assuming all frames have the same dimensions)
height, width, _ = in_frames[0].shape
fourcc = cv2.VideoWriter_fourcc(*codec)
fps = 10.0
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height * 3)) # Width is tripled for concatenation
# Loop through each set of frames and concatenate them
for j in range(len(in_frames)):
input_frame = in_frames[j]
cam_frame = cam_out_frames[j]
pred_frame = pred_diameters_text_frames[j]
# Convert frames to BGR if necessary
input_frame_bgr = cv2.cvtColor(input_frame, cv2.COLOR_RGB2BGR)
cam_frame_bgr = cv2.cvtColor(cam_frame, cv2.COLOR_RGB2BGR)
pred_frame_bgr = cv2.cvtColor(pred_frame, cv2.COLOR_RGB2BGR)
# Concatenate frames horizontally (input, cam, pred)
combined_frame = np.vstack((input_frame_bgr, cam_frame_bgr, pred_frame_bgr))
# Write the combined frame to the video
out.write(combined_frame)
# Release the video writer
out.release()
# Read the video and encode it in base64 for displaying
with open(output_path, "rb") as video_file:
video_bytes = video_file.read()
video_base64 = base64.b64encode(video_bytes).decode("utf-8")
# Display the combined video
display_video_with_autoplay(video_cols[eye_type], video_base64, width=len(video_cols) * 50)
# Clean up
os.remove(output_path)
def set_input_image_on_ui(uploaded_file, cols):
input_img = Image.open(BytesIO(uploaded_file.read())).convert("RGB")
# NOTE: images taken with phone camera has an EXIF data field which often rotates images taken with the phone in a tilted position. PIL has a utility function that removes this data and ‘uprights’ the image.
input_img = ImageOps.exif_transpose(input_img)
input_img = resize_frame(input_img, max_width=640, max_height=480)
input_img = resize_frame(input_img, max_width=640, max_height=480)
cols[0].image(input_img, use_column_width=True)
st.session_state.total_frames = 1
return input_img
def set_input_video_on_ui(uploaded_file, cols):
tfile = tempfile.NamedTemporaryFile(delete=False)
try:
tfile.write(uploaded_file.read())
except Exception:
tfile.write(uploaded_file)
video_path = tfile.name
video_frames = extract_frames(video_path)
cols[0].video(video_path)
st.session_state.total_frames = len(video_frames)
return video_frames, video_path
def set_frames_processed_count_placeholder(cols):
st.session_state.current_frame = 0
st.session_state.frame_placeholder = cols[0].empty()
txt = f"<p style='font-size:20px;'> Number of Frames Processed: <strong>{st.session_state.current_frame} / {st.session_state.total_frames}</strong> </p>"
st.session_state.frame_placeholder.markdown(txt, unsafe_allow_html=True)
def video_to_bytes(video_path):
# Open the video file in binary mode and return the bytes
with open(video_path, "rb") as video_file:
return video_file.read()
def display_video_library(video_folder="./sample_videos"):
# Get all video files from the folder
video_files = [f for f in os.listdir(video_folder) if f.endswith(".webm")]
# Store the selected video path
selected_video_path = None
# Calculate number of columns (adjust based on your layout preferences)
num_columns = 3 # For a grid of 3 videos per row
# Display videos in a grid layout with 'Select' button for each video
for i in range(0, len(video_files), num_columns):
cols = st.columns(num_columns)
for idx, video_file in enumerate(video_files[i : i + num_columns]):
with cols[idx]:
st.subheader(video_file.split(".")[0]) # Use the file name as the title
video_path = os.path.join(video_folder, video_file)
st.video(video_path) # Show the video
if st.button(f"Select {video_file.split('.')[0]}", key=video_file, type="primary"):
st.session_state.clear()
st.toast("Scroll Down to see the input and predictions", icon="⏬")
selected_video_path = video_path # Store the path of the selected video
return selected_video_path
def set_page_info_and_sidebar_info():
st.set_page_config(page_title="Pupil Diameter Estimator", layout="wide")
st.title("👁️ PupilSense 👁️🕵️♂️")
# st.markdown("Upload your own images or video **OR** select from our sample library below")
st.markdown(
"<p style='font-size: 30px;'>"
"Upload your own image 🖼️ or video 🎞️ <strong>OR</strong> select from our sample videos 📚"
"</p>",
unsafe_allow_html=True,
)
# video_path = display_video_library()
show_demo_videos = st.sidebar.checkbox("Show Sample Videos", value=False)
if show_demo_videos:
video_path = display_video_library()
else:
video_path = None
st.markdown("<hr id='target_element' style='border: 1px solid #6d6d6d; margin: 20px 0;'>", unsafe_allow_html=True)
cols = st.columns((1, 1))
cols[0].header("Input")
cols[-1].header("Prediction")
st.markdown("<hr style='border: 1px solid #6d6d6d; margin: 20px 0;'>", unsafe_allow_html=True)
LABEL_MAP = ["left_pupil", "right_pupil"]
TV_MODELS = ["ResNet18", "ResNet50"]
if "uploader_key" not in st.session_state:
st.session_state["uploader_key"] = 1
st.sidebar.title("Upload Face 👨🦱 or Eye 👁️")
uploaded_file = st.sidebar.file_uploader(
"Upload Image or Video",
type=["png", "jpeg", "jpg", "mp4", "avi", "mov", "mkv", "webm"],
key=st.session_state["uploader_key"],
)
if uploaded_file is not None:
st.session_state["uploaded_file"] = uploaded_file
st.sidebar.title("Setup")
pupil_selection = st.sidebar.selectbox(
"Pupil Selection", ["both"] + LABEL_MAP, help="Select left or right pupil OR both for diameter estimation"
)
tv_model = st.sidebar.selectbox("Classification model", TV_MODELS, help="Supported Models")
blink_detection = st.sidebar.checkbox("Detect Blinks", value=True)
st.markdown("<style>#vg-tooltip-element{z-index: 1000051}</style>", unsafe_allow_html=True)
if "uploaded_file" not in st.session_state:
st.session_state["uploaded_file"] = None
if "og_video_path" not in st.session_state:
st.session_state["og_video_path"] = None
if uploaded_file is None and video_path is not None:
video_bytes = video_to_bytes(video_path)
uploaded_file = video_bytes
st.session_state["uploaded_file"] = uploaded_file
st.session_state["og_video_path"] = video_path
st.session_state["uploader_key"] = 0
return (
cols,
st.session_state["og_video_path"],
st.session_state["uploaded_file"],
pupil_selection,
tv_model,
blink_detection,
)
def pil_image_to_base64(img):
"""Convert a PIL Image to a base64 encoded string."""
buffered = io.BytesIO()
img.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
return img_str
def process_image_and_vizualize_data(cols, input_img, tv_model, pupil_selection, blink_detection):
input_frames, output_frames, predicted_diameters, face_frames, eyes_ratios = process_frames(
cols,
[input_img],
tv_model,
pupil_selection,
cam_method=CAM_METHODS[-1],
blink_detection=blink_detection,
)
# for ff in face_frames:
# if ff["has_face"]:
# cols[1].image(face_frames[0]["img"], use_column_width=True)
input_frames_keys = input_frames.keys()
video_cols = cols[1].columns(len(input_frames_keys))
for i, eye_type in enumerate(input_frames_keys):
# Check the pupil_selection and set the width accordingly
if pupil_selection == "both":
video_cols[i].image(input_frames[eye_type][-1], use_column_width=True)
else:
img_base64 = pil_image_to_base64(Image.fromarray(input_frames[eye_type][-1]))
image_html = f'<div style="width: 50%; margin-bottom: 1.2%;"><img src="data:image/png;base64,{img_base64}" style="width: 100%;"></div>'
video_cols[i].markdown(image_html, unsafe_allow_html=True)
output_frames_keys = output_frames.keys()
fig, axs = plt.subplots(1, len(output_frames_keys), figsize=(10, 5))
for i, eye_type in enumerate(output_frames_keys):
height, width, c = output_frames[eye_type][0].shape
frame = np.zeros((height, width, c), dtype=np.uint8)
text = f"{predicted_diameters[eye_type][0]:.2f}"
frame = overlay_text_on_frame(frame, text)
if pupil_selection == "both":
video_cols[i].image(output_frames[eye_type][-1], use_column_width=True)
video_cols[i].image(frame, use_column_width=True)
else:
img_base64 = pil_image_to_base64(Image.fromarray(output_frames[eye_type][-1]))
image_html = f'<div style="width: 50%; margin-top: 1.2%; margin-bottom: 1.2%"><img src="data:image/png;base64,{img_base64}" style="width: 100%;"></div>'
video_cols[i].markdown(image_html, unsafe_allow_html=True)
img_base64 = pil_image_to_base64(Image.fromarray(frame))
image_html = f'<div style="width: 50%; margin-top: 1.2%"><img src="data:image/png;base64,{img_base64}" style="width: 100%;"></div>'
video_cols[i].markdown(image_html, unsafe_allow_html=True)
return None
def plot_ears(eyes_ratios, eyes_df):
eyes_df["EAR"] = eyes_ratios
df = pd.DataFrame(eyes_ratios, columns=["EAR"])
df["Frame"] = range(1, len(eyes_ratios) + 1) # Create a frame column starting from 1
# Create an Altair chart for eyes_ratios
line_chart = (
alt.Chart(df)
.mark_line(color=colors[-1]) # Set color of the line
.encode(
x=alt.X("Frame:Q", title="Frame Number"),
y=alt.Y("EAR:Q", title="Eyes Aspect Ratio"),
tooltip=["Frame", "EAR"],
)
# .properties(title="Eyes Aspect Ratios (EARs)")
# .configure_axis(grid=True)
)
points_chart = line_chart.mark_point(color=colors[-1], filled=True)
# Create a horizontal rule at y=0.22
line1 = alt.Chart(pd.DataFrame({"y": [0.22]})).mark_rule(color="red").encode(y="y:Q")
line2 = alt.Chart(pd.DataFrame({"y": [0.25]})).mark_rule(color="green").encode(y="y:Q")
# Add text annotations for the lines
text1 = (
alt.Chart(pd.DataFrame({"y": [0.22], "label": ["Definite Blinks (<=0.22)"]}))
.mark_text(align="left", dx=100, dy=9, color="red", size=16)
.encode(y="y:Q", text="label:N")
)
text2 = (
alt.Chart(pd.DataFrame({"y": [0.25], "label": ["No Blinks (>=0.25)"]}))
.mark_text(align="left", dx=-150, dy=-9, color="green", size=16)
.encode(y="y:Q", text="label:N")
)
# Add gray area text for the region between red and green lines
gray_area_text = (
alt.Chart(pd.DataFrame({"y": [0.235], "label": ["Gray Area"]}))
.mark_text(align="left", dx=0, dy=0, color="gray", size=16)
.encode(y="y:Q", text="label:N")
)
# Combine all elements: line chart, points, rules, and text annotations
final_chart = (
line_chart.properties(title="Eyes Aspect Ratios (EARs)")
+ points_chart
+ line1
+ line2
+ text1
+ text2
+ gray_area_text
).interactive()
# Configure axis properties at the chart level
final_chart = final_chart.configure_axis(grid=True)
# Display the Altair chart
# st.subheader("Eyes Aspect Ratios (EARs)")
st.altair_chart(final_chart, use_container_width=True)
return eyes_df
def plot_individual_charts(predicted_diameters, cols):
# Iterate through categories and assign charts to columns
for i, (category, values) in enumerate(predicted_diameters.items()):
with cols[i]: # Directly use the column index
# st.subheader(category) # Add a subheader for the category
if "left" in category:
selected_color = colors[0]
elif "right" in category:
selected_color = colors[1]
else:
selected_color = colors[i]
# Convert values to numeric, replacing non-numeric values with None
values = [convert_diameter(value) for value in values]
if "left" in category:
category_name = "Left Pupil Diameter"
else:
category_name = "Right Pupil Diameter"
# Create a DataFrame from the values for Altair
df = pd.DataFrame(
{
"Frame": range(1, len(values) + 1),
category_name: values,
}
)
# Get the min and max values for y-axis limits, ignoring None
min_value = min(filter(lambda x: x is not None, values), default=None)
max_value = max(filter(lambda x: x is not None, values), default=None)
# Create an Altair chart with y-axis limits
line_chart = (
alt.Chart(df)
.mark_line(color=selected_color)
.encode(
x=alt.X("Frame:Q", title="Frame Number"),
y=alt.Y(
f"{category_name}:Q",
title="Diameter",
scale=alt.Scale(domain=[min_value, max_value]),
),
tooltip=[
"Frame",
alt.Tooltip(f"{category_name}:Q", title="Diameter"),
],
)
# .properties(title=f"{category} - Predicted Diameters")
# .configure_axis(grid=True)
)
points_chart = line_chart.mark_point(color=selected_color, filled=True)
final_chart = (
line_chart.properties(
title=f"{'Left Pupil' if 'left' in category else 'Right Pupil'} - Predicted Diameters"
)
+ points_chart
).interactive()
final_chart = final_chart.configure_axis(grid=True)
# Display the Altair chart
st.altair_chart(final_chart, use_container_width=True)
return df
def plot_combined_charts(predicted_diameters):
all_min_values = []
all_max_values = []
# Create an empty DataFrame to store combined data for plotting
combined_df = pd.DataFrame()
# Iterate through categories and collect data
for category, values in predicted_diameters.items():
# Convert values to numeric, replacing non-numeric values with None
values = [convert_diameter(value) for value in values]
# Get the min and max values for y-axis limits, ignoring None
min_value = min(filter(lambda x: x is not None, values), default=None)
max_value = max(filter(lambda x: x is not None, values), default=None)
all_min_values.append(min_value)
all_max_values.append(max_value)
category = "left_pupil" if "left" in category else "right_pupil"
# Create a DataFrame from the values
df = pd.DataFrame(
{
"Diameter": values,
"Frame": range(1, len(values) + 1), # Create a frame column starting from 1
"Category": category, # Add a column to specify the category
}
)
# Append to combined DataFrame
combined_df = pd.concat([combined_df, df], ignore_index=True)
combined_chart = (
alt.Chart(combined_df)
.mark_line()
.encode(
x=alt.X("Frame:Q", title="Frame Number"),
y=alt.Y(
"Diameter:Q",
title="Diameter",
scale=alt.Scale(domain=[min(all_min_values), max(all_max_values)]),
),
color=alt.Color("Category:N", scale=alt.Scale(range=colors), title="Pupil Type"),
tooltip=["Frame", "Diameter:Q", "Category:N"],
)
)
points_chart = combined_chart.mark_point(filled=True)
final_chart = (combined_chart.properties(title="Predicted Diameters") + points_chart).interactive()
final_chart = final_chart.configure_axis(grid=True)
# Display the combined chart
st.altair_chart(final_chart, use_container_width=True)
# --------------------------------------------
# Convert to a DataFrame
left_pupil_values = [convert_diameter(value) for value in predicted_diameters["left_eye"]]
right_pupil_values = [convert_diameter(value) for value in predicted_diameters["right_eye"]]
df = pd.DataFrame(
{
"Frame": range(1, len(left_pupil_values) + 1),
"Left Pupil Diameter": left_pupil_values,
"Right Pupil Diameter": right_pupil_values,
}
)
# Calculate the difference between left and right pupil diameters
df["Difference Value"] = df["Left Pupil Diameter"] - df["Right Pupil Diameter"]
# Determine the status of the difference
df["Difference Status"] = df.apply(
lambda row: "L>R" if row["Left Pupil Diameter"] > row["Right Pupil Diameter"] else "L<R",
axis=1,
)
return df
def process_video_and_visualize_data(cols, video_frames, tv_model, pupil_selection, blink_detection, video_path):
output_video_path = f"{root_path}/tmp.webm"
input_frames, output_frames, predicted_diameters, face_frames, eyes_ratios = process_video(
cols,
video_frames,
tv_model,
pupil_selection,
output_video_path,
cam_method=CAM_METHODS[-1],
blink_detection=blink_detection,
)
os.remove(video_path)
num_columns = len(predicted_diameters)
cols = st.columns(num_columns)
if num_columns == 2:
df = plot_combined_charts(predicted_diameters)
else:
df = plot_individual_charts(predicted_diameters, cols)
if eyes_ratios is not None and len(eyes_ratios) > 0:
df = plot_ears(eyes_ratios, df)
st.dataframe(df, hide_index=True, use_container_width=True)
|