hrnet量化rknn格式

我们下载一个转好的onnx模型
推理代码如下

import urllib
import traceback
import time
import sys
import warnings

import numpy as np
import cv2
from rknn.api import RKNN

RKNN_MODEL = "hrnet_w32_macaque_256x192-f7e9e04f_20210407.rknn"
IMG_PATH = "0.jpg"

QUANTIZE_ON = True

def bbox_xywh2cs(bbox, aspect_ratio, padding=1., pixel_std=200.):
    """Transform the bbox format from (x,y,w,h) into (center, scale)

    Args:
        bbox (ndarray): Single bbox in (x, y, w, h)
        aspect_ratio (float): The expected bbox aspect ratio (w over h)
        padding (float): Bbox padding factor that will be multilied to scale.
            Default: 1.0
        pixel_std (float): The scale normalization factor. Default: 200.0

    Returns:
        tuple: A tuple containing center and scale.
        - np.ndarray[float32](2,): Center of the bbox (x, y).
        - np.ndarray[float32](2,): Scale of the bbox w & h.
    """

    x, y, w, h = bbox[:4]
    center = np.array([x + w * 0.5, y + h * 0.5], dtype=np.float32)

    if w > aspect_ratio * h:
        h = w * 1.0 / aspect_ratio
    elif w < aspect_ratio * h:
        w = h * aspect_ratio

    scale = np.array([w, h], dtype=np.float32) / pixel_std
    scale = scale * padding

    return center, scale
def rotate_point(pt, angle_rad):
    """Rotate a point by an angle.

    Args:
        pt (list[float]): 2 dimensional point to be rotated
        angle_rad (float): rotation angle by radian

    Returns:
        list[float]: Rotated point.
    """
    assert len(pt) == 2
    sn, cs = np.sin(angle_rad), np.cos(angle_rad)
    new_x = pt[0] * cs - pt[1] * sn
    new_y = pt[0] * sn + pt[1] * cs
    rotated_pt = [new_x, new_y]

    return rotated_pt
def _get_3rd_point(a, b):
    """To calculate the affine matrix, three pairs of points are required. This
    function is used to get the 3rd point, given 2D points a & b.

    The 3rd point is defined by rotating vector `a - b` by 90 degrees
    anticlockwise, using b as the rotation center.

    Args:
        a (np.ndarray): point(x,y)
        b (np.ndarray): point(x,y)

    Returns:
        np.ndarray: The 3rd point.
    """
    assert len(a) == 2
    assert len(b) == 2
    direction = a - b
    third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)

    return third_pt
def get_affine_transform(center,
                         scale,
                         rot,
                         output_size,
                         shift=(0., 0.),
                         inv=False):
    """Get the affine transform matrix, given the center/scale/rot/output_size.

    Args:
        center (np.ndarray[2, ]): Center of the bounding box (x, y).
        scale (np.ndarray[2, ]): Scale of the bounding box
            wrt [width, height].
        rot (float): Rotation angle (degree).
        output_size (np.ndarray[2, ] | list(2,)): Size of the
            destination heatmaps.
        shift (0-100%): Shift translation ratio wrt the width/height.
            Default (0., 0.).
        inv (bool): Option to inverse the affine transform direction.
            (inv=False: src->dst or inv=True: dst->src)

    Returns:
        np.ndarray: The transform matrix.
    """
    assert len(center) == 2
    assert len(scale) == 2
    assert len(output_size) == 2
    assert len(shift) == 2

    # pixel_std is 200.
    scale_tmp = scale * 200.0

    shift = np.array(shift)
    src_w = scale_tmp[0]
    dst_w = output_size[0]
    dst_h = output_size[1]

    rot_rad = np.pi * rot / 180
    src_dir = rotate_point([0., src_w * -0.5], rot_rad)
    dst_dir = np.array([0., dst_w * -0.5])

    src = np.zeros((3, 2), dtype=np.float32)
    src[0, :] = center + scale_tmp * shift
    src[1, :] = center + src_dir + scale_tmp * shift
    src[2, :] = _get_3rd_point(src[0, :], src[1, :])

    dst = np.zeros((3, 2), dtype=np.float32)
    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
    dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])

    if inv:
        trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
    else:
        trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))

    return trans
def bbox_xyxy2xywh(bbox_xyxy):
    """Transform the bbox format from x1y1x2y2 to xywh.

    Args:
        bbox_xyxy (np.ndarray): Bounding boxes (with scores), shaped (n, 4) or
            (n, 5). (left, top, right, bottom, [score])

    Returns:
        np.ndarray: Bounding boxes (with scores),
          shaped (n, 4) or (n, 5). (left, top, width, height, [score])
    """
    bbox_xywh = bbox_xyxy.copy()
    bbox_xywh[:, 2] = bbox_xywh[:, 2] - bbox_xywh[:, 0]
    bbox_xywh[:, 3] = bbox_xywh[:, 3] - bbox_xywh[:, 1]

    return bbox_xywh
def _get_max_preds(heatmaps):
    """Get keypoint predictions from score maps.

    Note:
        batch_size: N
        num_keypoints: K
        heatmap height: H
        heatmap width: W

    Args:
        heatmaps (np.ndarray[N, K, H, W]): model predicted heatmaps.

    Returns:
        tuple: A tuple containing aggregated results.

        - preds (np.ndarray[N, K, 2]): Predicted keypoint location.
        - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
    """
    assert isinstance(heatmaps,
                      np.ndarray), ('heatmaps should be numpy.ndarray')
    assert heatmaps.ndim == 4, 'batch_images should be 4-ndim'

    N, K, _, W = heatmaps.shape
    heatmaps_reshaped = heatmaps.reshape((N, K, -1))
    idx = np.argmax(heatmaps_reshaped, 2).reshape((N, K, 1))
    maxvals = np.amax(heatmaps_reshaped, 2).reshape((N, K, 1))

    preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
    preds[:, :, 0] = preds[:, :, 0] % W
    preds[:, :, 1] = preds[:, :, 1] // W

    preds = np.where(np.tile(maxvals, (1, 1, 2)) > 0.0, preds, -1)
    return preds, maxvals
def transform_preds(coords, center, scale, output_size, use_udp=False):
    """Get final keypoint predictions from heatmaps and apply scaling and
    translation to map them back to the image.

    Note:
        num_keypoints: K

    Args:
        coords (np.ndarray[K, ndims]):

            * If ndims=2, corrds are predicted keypoint location.
            * If ndims=4, corrds are composed of (x, y, scores, tags)
            * If ndims=5, corrds are composed of (x, y, scores, tags,
              flipped_tags)

        center (np.ndarray[2, ]): Center of the bounding box (x, y).
        scale (np.ndarray[2, ]): Scale of the bounding box
            wrt [width, height].
        output_size (np.ndarray[2, ] | list(2,)): Size of the
            destination heatmaps.
        use_udp (bool): Use unbiased data processing

    Returns:
        np.ndarray: Predicted coordinates in the images.
    """
    assert coords.shape[1] in (2, 4, 5)
    assert len(center) == 2
    assert len(scale) == 2
    assert len(output_size) == 2

    # Recover the scale which is normalized by a factor of 200.
    scale = scale * 200.0

    if use_udp:
        scale_x = scale[0] / (output_size[0] - 1.0)
        scale_y = scale[1] / (output_size[1] - 1.0)
    else:
        scale_x = scale[0] / output_size[0]
        scale_y = scale[1] / output_size[1]

    target_coords = np.ones_like(coords)
    target_coords[:, 0] = coords[:, 0] * scale_x + center[0] - scale[0] * 0.5
    target_coords[:, 1] = coords[:, 1] * scale_y + center[1] - scale[1] * 0.5

    return target_coords
def keypoints_from_heatmaps(heatmaps,
                            center,
                            scale,
                            unbiased=False,
                            post_process='default',
                            kernel=11,
                            valid_radius_factor=0.0546875,
                            use_udp=False,
                            target_type='GaussianHeatmap'):
    
    # Avoid being affected
    heatmaps = heatmaps.copy()

    N, K, H, W = heatmaps.shape
    preds, maxvals = _get_max_preds(heatmaps)
    # add +/-0.25 shift to the predicted locations for higher acc.
    for n in range(N):
        for k in range(K):
            heatmap = heatmaps[n][k]
            px = int(preds[n][k][0])
            py = int(preds[n][k][1])
            if 1 < px < W - 1 and 1 < py < H - 1:
                diff = np.array([
                    heatmap[py][px + 1] - heatmap[py][px - 1],
                    heatmap[py + 1][px] - heatmap[py - 1][px]
                ])
                preds[n][k] += np.sign(diff) * .25
                if post_process == 'megvii':
                    preds[n][k] += 0.5

    # Transform back to the image
    for i in range(N):
        preds[i] = transform_preds(
            preds[i], center[i], scale[i], [W, H], use_udp=use_udp)

    if post_process == 'megvii':
        maxvals = maxvals / 255.0 + 0.5

    return preds, maxvals

def decode(output,center,scale,score_,batch_size = 1):


    c = np.zeros((batch_size, 2), dtype=np.float32)
    s = np.zeros((batch_size, 2), dtype=np.float32)
    score = np.ones(batch_size)
    for i in range(batch_size):
        c[i, :] = center
        s[i, :] = scale

        score[i] = np.array(score_).reshape(-1)
	  

    preds, maxvals = keypoints_from_heatmaps(
	    output,
	    c,
	    s,
       False,
        'default',
        11,
        0.0546875,
        False,
        'GaussianHeatmap'
        )

    all_preds = np.zeros((batch_size, preds.shape[1], 3), dtype=np.float32)
    all_boxes = np.zeros((batch_size, 6), dtype=np.float32)
    all_preds[:, :, 0:2] = preds[:, :, 0:2]
    all_preds[:, :, 2:3] = maxvals
    all_boxes[:, 0:2] = c[:, 0:2]
    all_boxes[:, 2:4] = s[:, 0:2]
    all_boxes[:, 4] = np.prod(s * 200.0, axis=1)
    all_boxes[:, 5] = score
    result = {}

    result['preds'] = all_preds
    result['boxes'] = all_boxes

    print(result)
    return result
def draw(bgr,predict_dict,skeleton):
    bboxes = predict_dict["boxes"]
    for box in bboxes:
        cv2.rectangle(bgr, (int(box[0]), int(box[1])), (int(box[0]) + int(box[2]), int(box[1]) + int(box[3])),(255, 0, 0))

    all_preds = predict_dict["preds"]
    for all_pred in all_preds:
        for x,y,s in all_pred:
            cv2.circle(bgr,(int(x), int(y)), 3,(0, 255, 120), -1)
        for sk in skeleton:
            x0=  int(all_pred[sk[0]][0])
            y0 = int(all_pred[sk[0]][1])
            x1 = int(all_pred[sk[1]][0])
            y1 = int(all_pred[sk[1]][1])
            cv2.line(bgr, (x0, y0), (x1, y1),(0, 255, 0), 1)
    cv2.imwrite("result.jpg",bgr)

if __name__ == "__main__":

    # Create RKNN object
    rknn = RKNN()

    if not os.path.exists(RKNN_MODEL):
        print("model not exist")
        exit(-1)

    # Load ONNX model
    print("--> Loading model")
    ret = rknn.load_rknn(RKNN_MODEL)
    if ret != 0:
        print("Load rknn model failed!")
        exit(ret)
    print("done")

    # init runtime environment
    print("--> Init runtime environment")
    ret = rknn.init_runtime()
    if ret != 0:
        print("Init runtime environment failed")
        exit(ret)
    print("done")
    bbox=[2.213932e+02, 1.935179e+02, 9.873443e+02-2.213932e+02, 1.035825e+03-1.935179e+02,9.995332e-01]
    image_size=[192,256]
    src_img = cv2.imread(IMG_PATH)
    img = cv2.cvtColor(src_img, cv2.COLOR_BGR2RGB)  # hwc rgb
    aspect_ratio = image_size[0] / image_size[1]
    img_height = img.shape[0]
    img_width = img.shape[1]
    padding=1.25
    pixel_std=200
    center, scale = bbox_xywh2cs(
        bbox,
        aspect_ratio,
        padding,
        pixel_std)
    trans = get_affine_transform(center, scale, 0, image_size)
    img = cv2.warpAffine(
        img,
        trans, (int(image_size[0]), int(image_size[1])),
        flags=cv2.INTER_LINEAR)
    print(trans)
    #img = np.transpose(img, (2, 0, 1)).astype(np.float32)  # chw rgb
    #outputs = rknn.inference(inputs=[img], data_type=None, data_format="nchw")[0]
    #img[0, ...] = ((img[0, ...] / 255.0) - 0.485) / 0.229
    #img[1, ...] = ((img[1, ...] / 255.0) - 0.456) / 0.224
    #img[2, ...] = ((img[2, ...] / 255.0) - 0.406) / 0.225
    # Inference
    print("--> Running model")
    start = time.clock()
    outputs= rknn.inference(inputs=[img])[0]
    # 获取结束时间
    end = time.clock()
    # 计算运行时间
    runTime = end - start
    runTime_ms = runTime * 1000
    # 输出运行时间
    print("运行时间:", runTime_ms, "毫秒")

    print(outputs)
    predict_dict=decode(outputs,center,scale,bbox[-1])
    skeleton = [[15, 13],[13, 11], [16, 14],[14, 12],[11, 12], [5, 11], [6, 12], [5, 6],[5, 7], [6, 8], [7, 9], [8, 10],[1, 2], [0, 1], [0, 2], [1, 3],[2, 4], [3, 5], [4, 6]]
    draw(src_img,predict_dict,skeleton)
    rknn.release()

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