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import copy
import torch
import numpy as np
import gradio as gr
from spoter_mod.skeleton_extractor import obtain_pose_data
from spoter_mod.normalization.body_normalization import normalize_single_dict as normalize_single_body_dict, BODY_IDENTIFIERS
from spoter_mod.normalization.hand_normalization import normalize_single_dict as normalize_single_hand_dict, HAND_IDENTIFIERS
model = torch.load("spoter-checkpoint.pth", map_location=torch.device('cpu'))
model.train(False)
HAND_IDENTIFIERS = [id + "_Left" for id in HAND_IDENTIFIERS] + [id + "_Right" for id in HAND_IDENTIFIERS]
GLOSS = ['book', 'drink', 'computer', 'before', 'chair', 'go', 'clothes', 'who', 'candy', 'cousin', 'deaf', 'fine',
'help', 'no', 'thin', 'walk', 'year', 'yes', 'all', 'black', 'cool', 'finish', 'hot', 'like', 'many', 'mother',
'now', 'orange', 'table', 'thanksgiving', 'what', 'woman', 'bed', 'blue', 'bowling', 'can', 'dog', 'family',
'fish', 'graduate', 'hat', 'hearing', 'kiss', 'language', 'later', 'man', 'shirt', 'study', 'tall', 'white',
'wrong', 'accident', 'apple', 'bird', 'change', 'color', 'corn', 'cow', 'dance', 'dark', 'doctor', 'eat',
'enjoy', 'forget', 'give', 'last', 'meet', 'pink', 'pizza', 'play', 'school', 'secretary', 'short', 'time',
'want', 'work', 'africa', 'basketball', 'birthday', 'brown', 'but', 'cheat', 'city', 'cook', 'decide', 'full',
'how', 'jacket', 'letter', 'medicine', 'need', 'paint', 'paper', 'pull', 'purple', 'right', 'same', 'son',
'tell', 'thursday']
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda")
def tensor_to_dictionary(landmarks_tensor: torch.Tensor) -> dict:
data_array = landmarks_tensor.numpy()
output = {}
for landmark_index, identifier in enumerate(BODY_IDENTIFIERS + HAND_IDENTIFIERS):
output[identifier] = data_array[:, landmark_index]
return output
def dictionary_to_tensor(landmarks_dict: dict) -> torch.Tensor:
output = np.empty(shape=(len(landmarks_dict["leftEar"]), len(BODY_IDENTIFIERS + HAND_IDENTIFIERS), 2))
for landmark_index, identifier in enumerate(BODY_IDENTIFIERS + HAND_IDENTIFIERS):
output[:, landmark_index, 0] = [frame[0] for frame in landmarks_dict[identifier]]
output[:, landmark_index, 1] = [frame[1] for frame in landmarks_dict[identifier]]
return torch.from_numpy(output)
def greet(label, video0, video1):
if label == "Webcam":
video = video0
elif label == "Video":
video = video1
elif label == "X":
return {"A": 0.8, "B": 0.1, "C": 0.1}
else:
return {}
data = obtain_pose_data(video)
depth_map = np.empty(shape=(len(data.data_hub["nose_X"]), len(BODY_IDENTIFIERS + HAND_IDENTIFIERS), 2))
for index, identifier in enumerate(BODY_IDENTIFIERS + HAND_IDENTIFIERS):
depth_map[:, index, 0] = data.data_hub[identifier + "_X"]
depth_map[:, index, 1] = data.data_hub[identifier + "_Y"]
depth_map = torch.from_numpy(np.copy(depth_map))
depth_map = tensor_to_dictionary(depth_map)
keys = copy.copy(list(depth_map.keys()))
for key in keys:
data = depth_map[key]
del depth_map[key]
depth_map[key.replace("_Left", "_0").replace("_Right", "_1")] = data
depth_map = normalize_single_body_dict(depth_map)
depth_map = normalize_single_hand_dict(depth_map)
keys = copy.copy(list(depth_map.keys()))
for key in keys:
data = depth_map[key]
del depth_map[key]
depth_map[key.replace("_0", "_Left").replace("_1", "_Right")] = data
depth_map = dictionary_to_tensor(depth_map)
depth_map = depth_map - 0.5
inputs = depth_map.squeeze(0).to(device)
outputs = model(inputs).expand(1, -1, -1)
results = torch.nn.functional.softmax(outputs, dim=2).detach().numpy()[0, 0]
results = {GLOSS[i]: float(results[i]) for i in range(100)}
return results
label = gr.outputs.Label(num_top_classes=5, label="Top class probabilities")
demo = gr.Interface(fn=greet, inputs=[gr.Dropdown(["Webcam", "Video"], label="Please select the input type:", type="value"), gr.Video(source="webcam", label="Webcam recording", type="mp4"), gr.Video(source="upload", label="Video upload", type="mp4")], outputs=label,
title="🤟 SPOTER Sign language recognition",
description="""Current user interfaces are not accessible for D/deaf and hard-of-hearing users, whose natural communication medium is sign language. We work on AI systems for sign language to come closer to sign-driven technology and empower accessible apps, websites, and video conferencing platforms.
Try out our recent model for sign language recognition right in your browser! The model below takes a video of a single sign in the American Sign Language at the input and provides you with probabilities of the lemmas (equivalent to words in natural language).
### Our work at CVPR
Our efforts on lightweight and efficient models for sign language recognition were first introduced at WACV with our SPOTER paper. We now presented a work-in-progress follow-up here at CVPR's AVA workshop. Be sure to check our work and code below:
- **WACV2022** - Original SPOTER paper - [Paper](https://openaccess.thecvf.com/content/WACV2022W/HADCV/papers/Bohacek_Sign_Pose-Based_Transformer_for_Word-Level_Sign_Language_Recognition_WACVW_2022_paper.pdf), [Code](https://github.com/matyasbohacek/spoter)
- **CVPR2022 (AVA Worshop)** - Follow-up WIP – [Extended Abstract](https://drive.google.com/file/d/1Szbhi7ZwZ6VAWAcGcDDU6qV9Uj9xnDsS/view?usp=sharing), [Poster](https://drive.google.com/file/d/1_xvmTNbLjTrx6psKdsLkufAtfmI5wfbF/view?usp=sharing)
### How to sign?
The model wrapped in this demo was trained on [WLASL100](https://dxli94.github.io/WLASL/), so it only knows selected ASL vocabulary. Take a look at these tutorial video examples (this is how you sign [computer](https://www.handspeak.com/word/search/index.php?id=449), [work](https://www.handspeak.com/word/search/index.php?id=2423), or [time](https://www.handspeak.com/word/search/index.php?id=2223)), try to replicate them yourself, and have them recognized using the webcam capture below. Have fun!
> The demo can analyze webcam recordings or your uploaded videos. Before you hit Submit, **don't forget to select the input source in the dropdown first**.""",
article="This is joint work of [Matyas Bohacek](https://scholar.google.cz/citations?user=wDy1xBwAAAAJ) and [Zhuo Cao](https://www.linkedin.com/in/zhuo-cao-b0787a1aa/?originalSubdomain=hk). For more info, visit [our website](https://www.signlanguagerecognition.com). To contact us, drop an e-mail [here](mailto:matyas.bohacek@matsworld.io).",
css="""
@font-face {
font-family: Graphik;
font-weight: regular;
src: url("https://www.signlanguagerecognition.com/supplementary/GraphikRegular.otf") format("opentype");
}
@font-face {
font-family: Graphik;
font-weight: bold;
src: url("https://www.signlanguagerecognition.com/supplementary/GraphikBold.otf") format("opentype");
}
@font-face {
font-family: MonumentExpanded;
font-weight: regular;
src: url("https://www.signlanguagerecognition.com/supplementary/MonumentExtended-Regular.otf") format("opentype");
}
@font-face {
font-family: MonumentExpanded;
font-weight: bold;
src: url("https://www.signlanguagerecognition.com/supplementary/MonumentExtended-Bold.otf") format("opentype");
}
html {
font-family: "Graphik";
}
h1 {
font-family: "MonumentExpanded";
}
#12 {
- background-image: linear-gradient(to left, #61D836, #6CB346) !important;
background-color: #61D836 !important;
}
#12:hover {
- background-image: linear-gradient(to left, #61D836, #6CB346) !important;
background-color: #6CB346 !important;
border: 0 !important;
border-color: 0 !important;
}
.dark .gr-button-primary {
--tw-gradient-from: #61D836;
--tw-gradient-to: #6CB346;
border: 0 !important;
border-color: 0 !important;
}
.dark .gr-button-primary:hover {
--tw-gradient-from: #64A642;
--tw-gradient-to: #58933B;
border: 0 !important;
border-color: 0 !important;
}
""",
cache_examples=True
)
demo.launch(debug=True)
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