mirror of
https://github.com/invoke-ai/InvokeAI
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362 lines
11 KiB
Python
362 lines
11 KiB
Python
# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
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from typing import List, Tuple
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import cv2
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import numpy as np
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import onnxruntime as ort
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def preprocess(
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img: np.ndarray, out_bbox, input_size: Tuple[int, int] = (192, 256)
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""Do preprocessing for RTMPose model inference.
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Args:
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img (np.ndarray): Input image in shape.
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input_size (tuple): Input image size in shape (w, h).
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Returns:
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tuple:
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- resized_img (np.ndarray): Preprocessed image.
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- center (np.ndarray): Center of image.
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- scale (np.ndarray): Scale of image.
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"""
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# get shape of image
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img_shape = img.shape[:2]
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out_img, out_center, out_scale = [], [], []
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if len(out_bbox) == 0:
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out_bbox = [[0, 0, img_shape[1], img_shape[0]]]
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for i in range(len(out_bbox)):
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x0 = out_bbox[i][0]
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y0 = out_bbox[i][1]
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x1 = out_bbox[i][2]
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y1 = out_bbox[i][3]
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bbox = np.array([x0, y0, x1, y1])
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# get center and scale
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center, scale = bbox_xyxy2cs(bbox, padding=1.25)
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# do affine transformation
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resized_img, scale = top_down_affine(input_size, scale, center, img)
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# normalize image
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mean = np.array([123.675, 116.28, 103.53])
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std = np.array([58.395, 57.12, 57.375])
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resized_img = (resized_img - mean) / std
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out_img.append(resized_img)
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out_center.append(center)
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out_scale.append(scale)
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return out_img, out_center, out_scale
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def inference(sess: ort.InferenceSession, img: np.ndarray) -> np.ndarray:
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"""Inference RTMPose model.
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Args:
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sess (ort.InferenceSession): ONNXRuntime session.
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img (np.ndarray): Input image in shape.
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Returns:
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outputs (np.ndarray): Output of RTMPose model.
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"""
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all_out = []
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# build input
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for i in range(len(img)):
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input = [img[i].transpose(2, 0, 1)]
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# build output
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sess_input = {sess.get_inputs()[0].name: input}
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sess_output = []
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for out in sess.get_outputs():
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sess_output.append(out.name)
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# run model
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outputs = sess.run(sess_output, sess_input)
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all_out.append(outputs)
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return all_out
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def postprocess(
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outputs: List[np.ndarray],
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model_input_size: Tuple[int, int],
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center: Tuple[int, int],
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scale: Tuple[int, int],
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simcc_split_ratio: float = 2.0,
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) -> Tuple[np.ndarray, np.ndarray]:
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"""Postprocess for RTMPose model output.
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Args:
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outputs (np.ndarray): Output of RTMPose model.
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model_input_size (tuple): RTMPose model Input image size.
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center (tuple): Center of bbox in shape (x, y).
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scale (tuple): Scale of bbox in shape (w, h).
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simcc_split_ratio (float): Split ratio of simcc.
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Returns:
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tuple:
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- keypoints (np.ndarray): Rescaled keypoints.
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- scores (np.ndarray): Model predict scores.
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"""
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all_key = []
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all_score = []
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for i in range(len(outputs)):
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# use simcc to decode
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simcc_x, simcc_y = outputs[i]
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keypoints, scores = decode(simcc_x, simcc_y, simcc_split_ratio)
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# rescale keypoints
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keypoints = keypoints / model_input_size * scale[i] + center[i] - scale[i] / 2
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all_key.append(keypoints[0])
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all_score.append(scores[0])
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return np.array(all_key), np.array(all_score)
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def bbox_xyxy2cs(bbox: np.ndarray, padding: float = 1.0) -> Tuple[np.ndarray, np.ndarray]:
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"""Transform the bbox format from (x,y,w,h) into (center, scale)
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Args:
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bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted
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as (left, top, right, bottom)
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padding (float): BBox padding factor that will be multilied to scale.
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Default: 1.0
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Returns:
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tuple: A tuple containing center and scale.
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- np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or
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(n, 2)
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- np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or
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(n, 2)
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"""
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# convert single bbox from (4, ) to (1, 4)
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dim = bbox.ndim
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if dim == 1:
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bbox = bbox[None, :]
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# get bbox center and scale
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x1, y1, x2, y2 = np.hsplit(bbox, [1, 2, 3])
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center = np.hstack([x1 + x2, y1 + y2]) * 0.5
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scale = np.hstack([x2 - x1, y2 - y1]) * padding
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if dim == 1:
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center = center[0]
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scale = scale[0]
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return center, scale
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def _fix_aspect_ratio(bbox_scale: np.ndarray, aspect_ratio: float) -> np.ndarray:
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"""Extend the scale to match the given aspect ratio.
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Args:
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scale (np.ndarray): The image scale (w, h) in shape (2, )
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aspect_ratio (float): The ratio of ``w/h``
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Returns:
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np.ndarray: The reshaped image scale in (2, )
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"""
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w, h = np.hsplit(bbox_scale, [1])
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bbox_scale = np.where(w > h * aspect_ratio, np.hstack([w, w / aspect_ratio]), np.hstack([h * aspect_ratio, h]))
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return bbox_scale
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def _rotate_point(pt: np.ndarray, angle_rad: float) -> np.ndarray:
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"""Rotate a point by an angle.
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Args:
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pt (np.ndarray): 2D point coordinates (x, y) in shape (2, )
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angle_rad (float): rotation angle in radian
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Returns:
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np.ndarray: Rotated point in shape (2, )
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"""
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sn, cs = np.sin(angle_rad), np.cos(angle_rad)
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rot_mat = np.array([[cs, -sn], [sn, cs]])
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return rot_mat @ pt
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def _get_3rd_point(a: np.ndarray, b: np.ndarray) -> np.ndarray:
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"""To calculate the affine matrix, three pairs of points are required. This
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function is used to get the 3rd point, given 2D points a & b.
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The 3rd point is defined by rotating vector `a - b` by 90 degrees
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anticlockwise, using b as the rotation center.
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Args:
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a (np.ndarray): The 1st point (x,y) in shape (2, )
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b (np.ndarray): The 2nd point (x,y) in shape (2, )
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Returns:
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np.ndarray: The 3rd point.
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"""
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direction = a - b
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c = b + np.r_[-direction[1], direction[0]]
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return c
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def get_warp_matrix(
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center: np.ndarray,
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scale: np.ndarray,
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rot: float,
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output_size: Tuple[int, int],
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shift: Tuple[float, float] = (0.0, 0.0),
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inv: bool = False,
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) -> np.ndarray:
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"""Calculate the affine transformation matrix that can warp the bbox area
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in the input image to the output size.
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Args:
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center (np.ndarray[2, ]): Center of the bounding box (x, y).
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scale (np.ndarray[2, ]): Scale of the bounding box
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wrt [width, height].
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rot (float): Rotation angle (degree).
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output_size (np.ndarray[2, ] | list(2,)): Size of the
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destination heatmaps.
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shift (0-100%): Shift translation ratio wrt the width/height.
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Default (0., 0.).
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inv (bool): Option to inverse the affine transform direction.
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(inv=False: src->dst or inv=True: dst->src)
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Returns:
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np.ndarray: A 2x3 transformation matrix
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"""
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shift = np.array(shift)
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src_w = scale[0]
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dst_w = output_size[0]
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dst_h = output_size[1]
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# compute transformation matrix
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rot_rad = np.deg2rad(rot)
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src_dir = _rotate_point(np.array([0.0, src_w * -0.5]), rot_rad)
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dst_dir = np.array([0.0, dst_w * -0.5])
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# get four corners of the src rectangle in the original image
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src = np.zeros((3, 2), dtype=np.float32)
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src[0, :] = center + scale * shift
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src[1, :] = center + src_dir + scale * shift
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src[2, :] = _get_3rd_point(src[0, :], src[1, :])
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# get four corners of the dst rectangle in the input image
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dst = np.zeros((3, 2), dtype=np.float32)
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dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
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dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
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dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])
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if inv:
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warp_mat = cv2.getAffineTransform(np.float32(dst), np.float32(src))
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else:
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warp_mat = cv2.getAffineTransform(np.float32(src), np.float32(dst))
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return warp_mat
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def top_down_affine(
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input_size: dict, bbox_scale: dict, bbox_center: dict, img: np.ndarray
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) -> Tuple[np.ndarray, np.ndarray]:
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"""Get the bbox image as the model input by affine transform.
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Args:
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input_size (dict): The input size of the model.
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bbox_scale (dict): The bbox scale of the img.
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bbox_center (dict): The bbox center of the img.
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img (np.ndarray): The original image.
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Returns:
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tuple: A tuple containing center and scale.
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- np.ndarray[float32]: img after affine transform.
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- np.ndarray[float32]: bbox scale after affine transform.
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"""
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w, h = input_size
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warp_size = (int(w), int(h))
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# reshape bbox to fixed aspect ratio
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bbox_scale = _fix_aspect_ratio(bbox_scale, aspect_ratio=w / h)
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# get the affine matrix
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center = bbox_center
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scale = bbox_scale
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rot = 0
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warp_mat = get_warp_matrix(center, scale, rot, output_size=(w, h))
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# do affine transform
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img = cv2.warpAffine(img, warp_mat, warp_size, flags=cv2.INTER_LINEAR)
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return img, bbox_scale
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def get_simcc_maximum(simcc_x: np.ndarray, simcc_y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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"""Get maximum response location and value from simcc representations.
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Note:
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instance number: N
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num_keypoints: K
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heatmap height: H
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heatmap width: W
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Args:
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simcc_x (np.ndarray): x-axis SimCC in shape (K, Wx) or (N, K, Wx)
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simcc_y (np.ndarray): y-axis SimCC in shape (K, Wy) or (N, K, Wy)
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Returns:
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tuple:
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- locs (np.ndarray): locations of maximum heatmap responses in shape
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(K, 2) or (N, K, 2)
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- vals (np.ndarray): values of maximum heatmap responses in shape
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(K,) or (N, K)
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"""
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N, K, Wx = simcc_x.shape
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simcc_x = simcc_x.reshape(N * K, -1)
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simcc_y = simcc_y.reshape(N * K, -1)
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# get maximum value locations
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x_locs = np.argmax(simcc_x, axis=1)
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y_locs = np.argmax(simcc_y, axis=1)
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locs = np.stack((x_locs, y_locs), axis=-1).astype(np.float32)
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max_val_x = np.amax(simcc_x, axis=1)
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max_val_y = np.amax(simcc_y, axis=1)
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# get maximum value across x and y axis
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mask = max_val_x > max_val_y
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max_val_x[mask] = max_val_y[mask]
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vals = max_val_x
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locs[vals <= 0.0] = -1
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# reshape
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locs = locs.reshape(N, K, 2)
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vals = vals.reshape(N, K)
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return locs, vals
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def decode(simcc_x: np.ndarray, simcc_y: np.ndarray, simcc_split_ratio) -> Tuple[np.ndarray, np.ndarray]:
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"""Modulate simcc distribution with Gaussian.
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Args:
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simcc_x (np.ndarray[K, Wx]): model predicted simcc in x.
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simcc_y (np.ndarray[K, Wy]): model predicted simcc in y.
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simcc_split_ratio (int): The split ratio of simcc.
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Returns:
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tuple: A tuple containing center and scale.
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- np.ndarray[float32]: keypoints in shape (K, 2) or (n, K, 2)
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- np.ndarray[float32]: scores in shape (K,) or (n, K)
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"""
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keypoints, scores = get_simcc_maximum(simcc_x, simcc_y)
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keypoints /= simcc_split_ratio
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return keypoints, scores
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def inference_pose(session, out_bbox, oriImg):
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h, w = session.get_inputs()[0].shape[2:]
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model_input_size = (w, h)
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resized_img, center, scale = preprocess(oriImg, out_bbox, model_input_size)
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outputs = inference(session, resized_img)
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keypoints, scores = postprocess(outputs, model_input_size, center, scale)
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return keypoints, scores
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