Synthpose Vitpose Base Hf

🚀 SynthPose (Transformers 🤗 VitPose Base變體)

SynthPose是一種全新的方法，它藉助合成數據，能夠對預訓練的2D人體姿態模型進行微調，以預測任意更密集的關鍵點集，從而實現精確的運動學分析。

🚀 快速開始

模型信息

模型背景

SynthPose模型由Yoni Gozlan、Antoine Falisse、Scott Uhlrich、Anthony Gatti、Michael Black和Akshay Chaudhari在論文OpenCapBench: A Benchmark to Bridge Pose Estimation and Biomechanics中提出。此模型由Yoni Gozlan貢獻。

✨ 主要特性

• 使用特定骨幹網絡：該模型採用VitPose Base骨幹網絡。
• 支持關鍵點微調：SynthPose可利用合成數據對預訓練的2D人體姿態模型進行微調，以預測更密集的關鍵點集，用於精確的運動學分析。
• 特定關鍵點集微調：此特定變體在通常用於動作捕捉設置的一組關鍵點上進行了微調，同時也包含COCO關鍵點。

模型預測的關鍵點

模型會預測以下52個標記點：

{
    0: "Nose",
    1: "L_Eye",
    2: "R_Eye",
    3: "L_Ear",
    4: "R_Ear",
    5: "L_Shoulder",
    6: "R_Shoulder",
    7: "L_Elbow",
    8: "R_Elbow",
    9: "L_Wrist",
    10: "R_Wrist",
    11: "L_Hip",
    12: "R_Hip",
    13: "L_Knee",
    14: "R_Knee",
    15: "L_Ankle",
    16: "R_Ankle",
    17: "sternum",
    18: "rshoulder",
    19: "lshoulder",
    20: "r_lelbow",
    21: "l_lelbow",
    22: "r_melbow",
    23: "l_melbow",
    24: "r_lwrist",
    25: "l_lwrist",
    26: "r_mwrist",
    27: "l_mwrist",
    28: "r_ASIS",
    29: "l_ASIS",
    30: "r_PSIS",
    31: "l_PSIS",
    32: "r_knee",
    33: "l_knee",
    34: "r_mknee",
    35: "l_mknee",
    36: "r_ankle",
    37: "l_ankle",
    38: "r_mankle",
    39: "l_mankle",
    40: "r_5meta",
    41: "l_5meta",
    42: "r_toe",
    43: "l_toe",
    44: "r_big_toe",
    45: "l_big_toe",
    46: "l_calc",
    47: "r_calc",
    48: "C7",
    49: "L2",
    50: "T11",
    51: "T6",
}

其中前17個關鍵點是COCO關鍵點，接下來的35個是解剖學標記點。

💻 使用示例

基礎用法

以下是如何加載模型並對圖像進行推理的示例：

import torch
import requests
import numpy as np

from PIL import Image

from transformers import (
    AutoProcessor,
    RTDetrForObjectDetection,
    VitPoseForPoseEstimation,
)

device = "cuda" if torch.cuda.is_available() else "cpu"

url = "http://farm4.staticflickr.com/3300/3416216247_f9c6dfc939_z.jpg"
image = Image.open(requests.get(url, stream=True).raw)

# ------------------------------------------------------------------------
# Stage 1. Detect humans on the image
# ------------------------------------------------------------------------

# You can choose detector by your choice
person_image_processor = AutoProcessor.from_pretrained("PekingU/rtdetr_r50vd_coco_o365")
person_model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd_coco_o365", device_map=device)

inputs = person_image_processor(images=image, return_tensors="pt").to(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]  # take first image results

# 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]

# ------------------------------------------------------------------------
# Stage 2. Detect keypoints for each person found
# ------------------------------------------------------------------------

image_processor = AutoProcessor.from_pretrained("yonigozlan/synthpose-vitpose-base-hf")
model = VitPoseForPoseEstimation.from_pretrained("yonigozlan/synthpose-vitpose-base-hf", device_map=device)

inputs = image_processor(image, boxes=[person_boxes], return_tensors="pt").to(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]  # results for first image

高級用法

使用supervision庫進行可視化

import supervision as sv

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
)

vertex_annotator = sv.VertexAnnotator(
    color=sv.Color.PINK,
    radius=2
)

annotated_frame = vertex_annotator.annotate(
    scene=image.copy(),
    key_points=key_points
)
annotated_frame

手動高級可視化

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]+[4]*(52-17)]

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=2, 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

📄 許可證

本項目採用apache-2.0許可證。