<!--Copyright 2024 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
*This model was published in HF papers on 2022-04-26 and contributed to Hugging Face Transformers on 2025-01-08.*


# ViTPose

[ViTPose](https://huggingface.co/papers/2204.12484) is a vision transformer-based model for keypoint (pose) estimation. It uses a simple, non-hierarchical [ViT](./vit) backbone and a lightweight decoder head. This architecture simplifies model design, takes advantage of transformer scalability, and can be adapted to different training strategies.

[ViTPose++](https://huggingface.co/papers/2212.04246) improves on ViTPose by incorporating a mixture-of-experts (MoE) module in the backbone and using more diverse pretraining data.

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/vitpose-architecture.png"
alt="drawing" width="600"/>

You can find all ViTPose and ViTPose++ checkpoints under the [ViTPose collection](https://huggingface.co/collections/usyd-community/vitpose-677fcfd0a0b2b5c8f79c4335).

The example below demonstrates pose estimation with the [`VitPoseForPoseEstimation`] class.

```python
import requests
import supervision as sv
import torch
from PIL import Image

from transformers import AutoProcessor, RTDetrForObjectDetection, VitPoseForPoseEstimation


url = "https://www.fcbarcelona.com/fcbarcelona/photo/2021/01/31/3c55a19f-dfc1-4451-885e-afd14e890a11/mini_2021-01-31-BARCELONA-ATHLETIC-BILBAOI-30.JPG"
image = Image.open(requests.get(url, stream=True).raw)

# Detect humans in the image
person_image_processor = AutoProcessor.from_pretrained("PekingU/rtdetr_r50vd_coco_o365")
person_model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd_coco_o365", device_map="auto")

inputs = person_image_processor(images=image, return_tensors="pt").to(person_model.device)

with torch.no_grad():
    outputs = person_model(**inputs)

results = person_image_processor.post_process_object_detection(
    outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.3
)
result = results[0]

# Human label refers 0 index in COCO dataset
person_boxes = result["boxes"][result["labels"] == 0]
person_boxes = person_boxes.cpu().numpy()

# Convert boxes from VOC (x1, y1, x2, y2) to COCO (x1, y1, w, h) format
person_boxes[:, 2] = person_boxes[:, 2] - person_boxes[:, 0]
person_boxes[:, 3] = person_boxes[:, 3] - person_boxes[:, 1]

# Detect keypoints for each person found
image_processor = AutoProcessor.from_pretrained("usyd-community/vitpose-base-simple")
model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-base-simple", device_map="auto")

inputs = image_processor(image, boxes=[person_boxes], return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs)

pose_results = image_processor.post_process_pose_estimation(outputs, boxes=[person_boxes])
image_pose_result = pose_results[0]

xy = torch.stack([pose_result['keypoints'] for pose_result in image_pose_result]).cpu().numpy()
scores = torch.stack([pose_result['scores'] for pose_result in image_pose_result]).cpu().numpy()

key_points = sv.KeyPoints(
    xy=xy, confidence=scores
)

edge_annotator = sv.EdgeAnnotator(
    color=sv.Color.GREEN,
    thickness=1
)
vertex_annotator = sv.VertexAnnotator(
    color=sv.Color.RED,
    radius=2
)
annotated_frame = edge_annotator.annotate(
    scene=image.copy(),
    key_points=key_points
)
annotated_frame = vertex_annotator.annotate(
    scene=annotated_frame,
    key_points=key_points
)
annotated_frame
```

<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/vitpose.png"/>
</div>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.

The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.

```python
# pip install torchao
import requests
import torch
from PIL import Image

from transformers import AutoProcessor, RTDetrForObjectDetection, TorchAoConfig, VitPoseForPoseEstimation


url = "https://www.fcbarcelona.com/fcbarcelona/photo/2021/01/31/3c55a19f-dfc1-4451-885e-afd14e890a11/mini_2021-01-31-BARCELONA-ATHLETIC-BILBAOI-30.JPG"
image = Image.open(requests.get(url, stream=True).raw)

person_image_processor = AutoProcessor.from_pretrained("PekingU/rtdetr_r50vd_coco_o365")
person_model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd_coco_o365", device_map="auto")

inputs = person_image_processor(images=image, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = person_model(**inputs)

results = person_image_processor.post_process_object_detection(
    outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.3
)
result = results[0]

person_boxes = result["boxes"][result["labels"] == 0]
person_boxes = person_boxes.cpu().numpy()

person_boxes[:, 2] = person_boxes[:, 2] - person_boxes[:, 0]
person_boxes[:, 3] = person_boxes[:, 3] - person_boxes[:, 1]

quantization_config = TorchAoConfig("int4_weight_only", group_size=128)

image_processor = AutoProcessor.from_pretrained("usyd-community/vitpose-plus-huge")
model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-plus-huge", device_map="auto", quantization_config=quantization_config)

inputs = image_processor(image, boxes=[person_boxes], return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs)

pose_results = image_processor.post_process_pose_estimation(outputs, boxes=[person_boxes])
image_pose_result = pose_results[0]
```

## Notes

- Use [`AutoProcessor`] to automatically prepare bounding box and image inputs.
- ViTPose is a top-down pose estimator. It uses a object detector to detect individuals first before keypoint prediction.
- ViTPose++ has 6 different MoE expert heads (COCO validation `0`, AiC `1`, MPII `2`, AP-10K `3`, APT-36K `4`, COCO-WholeBody `5`) which supports 6 different datasets. Pass a specific value corresponding to the dataset to the `dataset_index` to indicate which expert to use.

    ```py
    from transformers import AutoProcessor, VitPoseForPoseEstimation

    image_processor = AutoProcessor.from_pretrained("usyd-community/vitpose-plus-base")
    model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-plus-base", device_map="auto")

    inputs = image_processor(image, boxes=[person_boxes], return_tensors="pt").to(model.device)
    dataset_index = torch.tensor([0], device=device) # must be a tensor of shape (batch_size,)

    with torch.no_grad():
        outputs = model(**inputs, dataset_index=dataset_index)
    ```

- [OpenCV](https://opencv.org/) is an alternative option for visualizing the estimated pose.

    ```py
    # pip install opencv-python
    import math
    import cv2

    def draw_points(image, keypoints, scores, pose_keypoint_color, keypoint_score_threshold, radius, show_keypoint_weight):
        if pose_keypoint_color is not None:
            assert len(pose_keypoint_color) == len(keypoints)
        for kid, (kpt, kpt_score) in enumerate(zip(keypoints, scores)):
            x_coord, y_coord = int(kpt[0]), int(kpt[1])
            if kpt_score > keypoint_score_threshold:
                color = tuple(int(c) for c in pose_keypoint_color[kid])
                if show_keypoint_weight:
                    cv2.circle(image, (int(x_coord), int(y_coord)), radius, color, -1)
                    transparency = max(0, min(1, kpt_score))
                    cv2.addWeighted(image, transparency, image, 1 - transparency, 0, dst=image)
                else:
                    cv2.circle(image, (int(x_coord), int(y_coord)), radius, color, -1)

    def draw_links(image, keypoints, scores, keypoint_edges, link_colors, keypoint_score_threshold, thickness, show_keypoint_weight, stick_width = 2):
        height, width, _ = image.shape
        if keypoint_edges is not None and link_colors is not None:
            assert len(link_colors) == len(keypoint_edges)
            for sk_id, sk in enumerate(keypoint_edges):
                x1, y1, score1 = (int(keypoints[sk[0], 0]), int(keypoints[sk[0], 1]), scores[sk[0]])
                x2, y2, score2 = (int(keypoints[sk[1], 0]), int(keypoints[sk[1], 1]), scores[sk[1]])
                if (
                    x1 > 0
                    and x1 < width
                    and y1 > 0
                    and y1 < height
                    and x2 > 0
                    and x2 < width
                    and y2 > 0
                    and y2 < height
                    and score1 > keypoint_score_threshold
                    and score2 > keypoint_score_threshold
                ):
                    color = tuple(int(c) for c in link_colors[sk_id])
                    if show_keypoint_weight:
                        X = (x1, x2)
                        Y = (y1, y2)
                        mean_x = np.mean(X)
                        mean_y = np.mean(Y)
                        length = ((Y[0] - Y[1]) ** 2 + (X[0] - X[1]) ** 2) ** 0.5
                        angle = math.degrees(math.atan2(Y[0] - Y[1], X[0] - X[1]))
                        polygon = cv2.ellipse2Poly(
                            (int(mean_x), int(mean_y)), (int(length / 2), int(stick_width)), int(angle), 0, 360, 1
                        )
                        cv2.fillConvexPoly(image, polygon, color)
                        transparency = max(0, min(1, 0.5 * (keypoints[sk[0], 2] + keypoints[sk[1], 2])))
                        cv2.addWeighted(image, transparency, image, 1 - transparency, 0, dst=image)
                    else:
                        cv2.line(image, (x1, y1), (x2, y2), color, thickness=thickness)

    # Note: keypoint_edges and color palette are dataset-specific
    keypoint_edges = model.config.edges

    palette = np.array(
        [
            [255, 128, 0],
            [255, 153, 51],
            [255, 178, 102],
            [230, 230, 0],
            [255, 153, 255],
            [153, 204, 255],
            [255, 102, 255],
            [255, 51, 255],
            [102, 178, 255],
            [51, 153, 255],
            [255, 153, 153],
            [255, 102, 102],
            [255, 51, 51],
            [153, 255, 153],
            [102, 255, 102],
            [51, 255, 51],
            [0, 255, 0],
            [0, 0, 255],
            [255, 0, 0],
            [255, 255, 255],
        ]
    )

    link_colors = palette[[0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16]]
    keypoint_colors = palette[[16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0]]

    numpy_image = np.array(image)

    for pose_result in image_pose_result:
        scores = np.array(pose_result["scores"])
        keypoints = np.array(pose_result["keypoints"])

        # draw each point on image
        draw_points(numpy_image, keypoints, scores, keypoint_colors, keypoint_score_threshold=0.3, radius=4, show_keypoint_weight=False)

        # draw links
        draw_links(numpy_image, keypoints, scores, keypoint_edges, link_colors, keypoint_score_threshold=0.3, thickness=1, show_keypoint_weight=False)

    pose_image = Image.fromarray(numpy_image)
    pose_image
    ```

## Resources

Refer to resources below to learn more about using ViTPose.

- This [notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/ViTPose/Inference_with_ViTPose_for_body_pose_estimation.ipynb) demonstrates inference and visualization.
- This [Space](https://huggingface.co/spaces/hysts/ViTPose-transformers) demonstrates ViTPose on images and video.

## VitPoseImageProcessor

[[autodoc]] VitPoseImageProcessor
    - preprocess
    - post_process_pose_estimation

## VitPoseImageProcessorPil

[[autodoc]] VitPoseImageProcessorPil
    - preprocess
    - post_process_pose_estimation

## VitPoseConfig

[[autodoc]] VitPoseConfig

## VitPoseForPoseEstimation

[[autodoc]] VitPoseForPoseEstimation
    - forward
