ultralytics 8.0.65 YOLOv8 Pose models (#1347)
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This commit is contained in:
Ayush Chaurasia
GitHub
parent
9af3e69b1a
commit
1cb92d7f42
57 changed files with 1578 additions and 489 deletions
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@ -16,7 +16,7 @@ from ultralytics.yolo.utils import LOGGER, TryExcept, threaded
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from .checks import check_font, check_version, is_ascii
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from .files import increment_path
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from .ops import clip_coords, scale_image, xywh2xyxy, xyxy2xywh
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from .ops import clip_boxes, scale_image, xywh2xyxy, xyxy2xywh
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matplotlib.rc('font', **{'size': 11})
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matplotlib.use('Agg') # for writing to files only
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@ -30,6 +30,11 @@ class Colors:
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'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
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self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
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self.n = len(self.palette)
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self.pose_palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102], [230, 230, 0], [255, 153, 255],
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[153, 204, 255], [255, 102, 255], [255, 51, 255], [102, 178, 255], [51, 153, 255],
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[255, 153, 153], [255, 102, 102], [255, 51, 51], [153, 255, 153], [102, 255, 102],
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[51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0], [255, 255, 255]],
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dtype=np.uint8)
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def __call__(self, i, bgr=False):
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c = self.palette[int(i) % self.n]
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@ -62,6 +67,12 @@ class Annotator:
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else: # use cv2
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self.im = im
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self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
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# pose
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self.skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12], [7, 13], [6, 7], [6, 8], [7, 9],
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[8, 10], [9, 11], [2, 3], [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
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self.limb_color = colors.pose_palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]
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self.kpt_color = colors.pose_palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]
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def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
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# Add one xyxy box to image with label
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@ -132,6 +143,49 @@ class Annotator:
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# convert im back to PIL and update draw
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self.fromarray(self.im)
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def kpts(self, kpts, shape=(640, 640), radius=5, kpt_line=True):
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"""Plot keypoints.
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Args:
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kpts (tensor): predicted kpts, shape: [17, 3]
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shape (tuple): image shape, (h, w)
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steps (int): keypoints step
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radius (int): size of drawing points
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"""
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if self.pil:
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# convert to numpy first
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self.im = np.asarray(self.im).copy()
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nkpt, ndim = kpts.shape
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is_pose = nkpt == 17 and ndim == 3
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kpt_line &= is_pose # `kpt_line=True` for now only supports human pose plotting
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for i, k in enumerate(kpts):
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color_k = [int(x) for x in self.kpt_color[i]] if is_pose else colors(i)
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x_coord, y_coord = k[0], k[1]
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if x_coord % shape[1] != 0 and y_coord % shape[0] != 0:
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if len(k) == 3:
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conf = k[2]
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if conf < 0.5:
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continue
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cv2.circle(self.im, (int(x_coord), int(y_coord)), radius, color_k, -1)
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if kpt_line:
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ndim = kpts.shape[-1]
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for sk_id, sk in enumerate(self.skeleton):
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pos1 = (int(kpts[(sk[0] - 1), 0]), int(kpts[(sk[0] - 1), 1]))
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pos2 = (int(kpts[(sk[1] - 1), 0]), int(kpts[(sk[1] - 1), 1]))
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if ndim == 3:
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conf1 = kpts[(sk[0] - 1), 2]
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conf2 = kpts[(sk[1] - 1), 2]
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if conf1 < 0.5 or conf2 < 0.5:
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continue
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if pos1[0] % shape[1] == 0 or pos1[1] % shape[0] == 0 or pos1[0] < 0 or pos1[1] < 0:
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continue
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if pos2[0] % shape[1] == 0 or pos2[1] % shape[0] == 0 or pos2[0] < 0 or pos2[1] < 0:
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continue
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cv2.line(self.im, pos1, pos2, [int(x) for x in self.limb_color[sk_id]], thickness=2)
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if self.pil:
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# convert im back to PIL and update draw
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self.fromarray(self.im)
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def rectangle(self, xy, fill=None, outline=None, width=1):
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# Add rectangle to image (PIL-only)
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self.draw.rectangle(xy, fill, outline, width)
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@ -213,7 +267,7 @@ def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False,
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b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
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b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
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xyxy = xywh2xyxy(b).long()
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clip_coords(xyxy, im.shape)
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clip_boxes(xyxy, im.shape)
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crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
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if save:
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file.parent.mkdir(parents=True, exist_ok=True) # make directory
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@ -229,6 +283,7 @@ def plot_images(images,
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cls,
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bboxes,
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masks=np.zeros(0, dtype=np.uint8),
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kpts=np.zeros((0, 51), dtype=np.float32),
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paths=None,
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fname='images.jpg',
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names=None):
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@ -241,6 +296,8 @@ def plot_images(images,
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bboxes = bboxes.cpu().numpy()
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if isinstance(masks, torch.Tensor):
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masks = masks.cpu().numpy().astype(int)
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if isinstance(kpts, torch.Tensor):
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kpts = kpts.cpu().numpy()
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if isinstance(batch_idx, torch.Tensor):
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batch_idx = batch_idx.cpu().numpy()
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@ -300,6 +357,21 @@ def plot_images(images,
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label = f'{c}' if labels else f'{c} {conf[j]:.1f}'
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annotator.box_label(box, label, color=color)
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# Plot keypoints
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if len(kpts):
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kpts_ = kpts[idx].copy()
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if len(kpts_):
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if kpts_[..., 0].max() <= 1.01 or kpts_[..., 1].max() <= 1.01: # if normalized with tolerance .01
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kpts_[..., 0] *= w # scale to pixels
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kpts_[..., 1] *= h
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elif scale < 1: # absolute coords need scale if image scales
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kpts_ *= scale
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kpts_[..., 0] += x
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kpts_[..., 1] += y
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for j in range(len(kpts_)):
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if labels or conf[j] > 0.25: # 0.25 conf thresh
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annotator.kpts(kpts_[j])
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# Plot masks
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if len(masks):
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if idx.shape[0] == masks.shape[0]: # overlap_masks=False
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@ -307,7 +379,7 @@ def plot_images(images,
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else: # overlap_masks=True
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image_masks = masks[[i]] # (1, 640, 640)
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nl = idx.sum()
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index = np.arange(nl).reshape(nl, 1, 1) + 1
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index = np.arange(nl).reshape((nl, 1, 1)) + 1
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image_masks = np.repeat(image_masks, nl, axis=0)
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image_masks = np.where(image_masks == index, 1.0, 0.0)
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@ -328,13 +400,16 @@ def plot_images(images,
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annotator.im.save(fname) # save
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def plot_results(file='path/to/results.csv', dir='', segment=False):
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def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False):
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# Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
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import pandas as pd
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save_dir = Path(file).parent if file else Path(dir)
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if segment:
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fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)
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index = [1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]
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elif pose:
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fig, ax = plt.subplots(2, 9, figsize=(21, 6), tight_layout=True)
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index = [1, 2, 3, 4, 5, 6, 7, 10, 11, 14, 15, 16, 17, 18, 8, 9, 12, 13]
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else:
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fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
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index = [1, 2, 3, 4, 5, 8, 9, 10, 6, 7]
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