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# Openpose
# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
# 3rd Edited by ControlNet
# 4th Edited by ControlNet (added face and correct hands)
# 5th Edited by ControlNet (Improved JSON serialization/deserialization, and lots of bug fixs)
# This preprocessor is licensed by CMU for non-commercial use only.
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
import json
import warnings
from typing import Callable, List, NamedTuple, Tuple, Union
import cv2
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, HF_MODEL_NAME
from . import util
from .body import Body, BodyResult, Keypoint
from .face import Face
from .hand import Hand
HandResult = List[Keypoint]
FaceResult = List[Keypoint]
class PoseResult(NamedTuple):
body: BodyResult
left_hand: Union[HandResult, None]
right_hand: Union[HandResult, None]
face: Union[FaceResult, None]
def draw_poses(poses: List[PoseResult], H, W, draw_body=True, draw_hand=True, draw_face=True):
"""
Draw the detected poses on an empty canvas.
Args:
poses (List[PoseResult]): A list of PoseResult objects containing the detected poses.
H (int): The height of the canvas.
W (int): The width of the canvas.
draw_body (bool, optional): Whether to draw body keypoints. Defaults to True.
draw_hand (bool, optional): Whether to draw hand keypoints. Defaults to True.
draw_face (bool, optional): Whether to draw face keypoints. Defaults to True.
Returns:
numpy.ndarray: A 3D numpy array representing the canvas with the drawn poses.
"""
canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
for pose in poses:
if draw_body:
canvas = util.draw_bodypose(canvas, pose.body.keypoints)
if draw_hand:
canvas = util.draw_handpose(canvas, pose.left_hand)
canvas = util.draw_handpose(canvas, pose.right_hand)
if draw_face:
canvas = util.draw_facepose(canvas, pose.face)
return canvas
def encode_poses_as_dict(poses: List[PoseResult], canvas_height: int, canvas_width: int) -> str:
""" Encode the pose as a dict following openpose JSON output format:
https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/02_output.md
"""
def compress_keypoints(keypoints: Union[List[Keypoint], None]) -> Union[List[float], None]:
if not keypoints:
return None
return [
value
for keypoint in keypoints
for value in (
[float(keypoint.x), float(keypoint.y), 1.0]
if keypoint is not None
else [0.0, 0.0, 0.0]
)
]
return {
'people': [
{
'pose_keypoints_2d': compress_keypoints(pose.body.keypoints),
"face_keypoints_2d": compress_keypoints(pose.face),
"hand_left_keypoints_2d": compress_keypoints(pose.left_hand),
"hand_right_keypoints_2d":compress_keypoints(pose.right_hand),
}
for pose in poses
],
'canvas_height': canvas_height,
'canvas_width': canvas_width,
}
class OpenposeDetector:
"""
A class for detecting human poses in images using the Openpose model.
Attributes:
model_dir (str): Path to the directory where the pose models are stored.
"""
def __init__(self, body_estimation, hand_estimation=None, face_estimation=None):
self.body_estimation = body_estimation
self.hand_estimation = hand_estimation
self.face_estimation = face_estimation
@classmethod
def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="body_pose_model.pth", hand_filename="hand_pose_model.pth", face_filename="facenet.pth"):
if pretrained_model_or_path == "lllyasviel/ControlNet":
subfolder = "annotator/ckpts"
face_pretrained_model_or_path = "lllyasviel/Annotators"
else:
subfolder = ''
face_pretrained_model_or_path = pretrained_model_or_path
body_model_path = custom_hf_download(pretrained_model_or_path, filename, subfolder=subfolder)
hand_model_path = custom_hf_download(pretrained_model_or_path, hand_filename, subfolder=subfolder)
face_model_path = custom_hf_download(face_pretrained_model_or_path, face_filename, subfolder=subfolder)
body_estimation = Body(body_model_path)
hand_estimation = Hand(hand_model_path)
face_estimation = Face(face_model_path)
return cls(body_estimation, hand_estimation, face_estimation)
def to(self, device):
self.body_estimation.to(device)
self.hand_estimation.to(device)
self.face_estimation.to(device)
return self
def detect_hands(self, body: BodyResult, oriImg) -> Tuple[Union[HandResult, None], Union[HandResult, None]]:
left_hand = None
right_hand = None
H, W, _ = oriImg.shape
for x, y, w, is_left in util.handDetect(body, oriImg):
peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :]).astype(np.float32)
if peaks.ndim == 2 and peaks.shape[1] == 2:
peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W)
peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H)
hand_result = [
Keypoint(x=peak[0], y=peak[1])
for peak in peaks
]
if is_left:
left_hand = hand_result
else:
right_hand = hand_result
return left_hand, right_hand
def detect_face(self, body: BodyResult, oriImg) -> Union[FaceResult, None]:
face = util.faceDetect(body, oriImg)
if face is None:
return None
x, y, w = face
H, W, _ = oriImg.shape
heatmaps = self.face_estimation(oriImg[y:y+w, x:x+w, :])
peaks = self.face_estimation.compute_peaks_from_heatmaps(heatmaps).astype(np.float32)
if peaks.ndim == 2 and peaks.shape[1] == 2:
peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W)
peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H)
return [
Keypoint(x=peak[0], y=peak[1])
for peak in peaks
]
return None
def detect_poses(self, oriImg, include_hand=False, include_face=False) -> List[PoseResult]:
"""
Detect poses in the given image.
Args:
oriImg (numpy.ndarray): The input image for pose detection.
include_hand (bool, optional): Whether to include hand detection. Defaults to False.
include_face (bool, optional): Whether to include face detection. Defaults to False.
Returns:
List[PoseResult]: A list of PoseResult objects containing the detected poses.
"""
oriImg = oriImg[:, :, ::-1].copy()
H, W, C = oriImg.shape
with torch.no_grad():
candidate, subset = self.body_estimation(oriImg)
bodies = self.body_estimation.format_body_result(candidate, subset)
results = []
for body in bodies:
left_hand, right_hand, face = (None,) * 3
if include_hand:
left_hand, right_hand = self.detect_hands(body, oriImg)
if include_face:
face = self.detect_face(body, oriImg)
results.append(PoseResult(BodyResult(
keypoints=[
Keypoint(
x=keypoint.x / float(W),
y=keypoint.y / float(H)
) if keypoint is not None else None
for keypoint in body.keypoints
],
total_score=body.total_score,
total_parts=body.total_parts
), left_hand, right_hand, face))
return results
def __call__(self, input_image, detect_resolution=512, include_body=True, include_hand=False, include_face=False, hand_and_face=None, output_type="pil", image_and_json=False, upscale_method="INTER_CUBIC", **kwargs):
if hand_and_face is not None:
warnings.warn("hand_and_face is deprecated. Use include_hand and include_face instead.", DeprecationWarning)
include_hand = hand_and_face
include_face = hand_and_face
input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method)
poses = self.detect_poses(input_image, include_hand=include_hand, include_face=include_face)
canvas = draw_poses(poses, input_image.shape[0], input_image.shape[1], draw_body=include_body, draw_hand=include_hand, draw_face=include_face)
detected_map = HWC3(remove_pad(canvas))
if output_type == "pil":
detected_map = Image.fromarray(detected_map)
if image_and_json:
return (detected_map, encode_poses_as_dict(poses, detected_map.shape[0], detected_map.shape[1]))
return detected_map
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