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`ultralytics 8.0.70` minor fixes and improvements (#1892)

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Co-authored-by: feicccccccc <49809204+feicccccccc@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
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Glenn Jocher 2 years ago committed by GitHub
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17 changed files with 71 additions and 90 deletions
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26
docs/modes/predict.md
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@ -216,19 +216,19 @@ masks, classification logits, etc.) found in the results object
res_plotted = res[0].plot() res_plotted = res[0].plot()
cv2.imshow("result", res_plotted) cv2.imshow("result", res_plotted)
``` ```
| Argument | Description | | Argument | Description |
| ----------- | ------------- | |--------------------------------|----------------------------------------------------------------------------------------|
| `conf (bool)` | Whether to plot the detection confidence score. | | `conf (bool)` | Whether to plot the detection confidence score. |
| `line_width (float, optional)` | The line width of the bounding boxes. If None, it is scaled to the image size. | | `line_width (float, optional)` | The line width of the bounding boxes. If None, it is scaled to the image size. |
| `font_size (float, optional)` | The font size of the text. If None, it is scaled to the image size. | | `font_size (float, optional)` | The font size of the text. If None, it is scaled to the image size. |
| `font (str)` | The font to use for the text. | | `font (str)` | The font to use for the text. |
| `pil (bool)` | Whether to return the image as a PIL Image. | | `pil (bool)` | Whether to use PIL for image plotting. |
| `example (str)` | An example string to display. Useful for indicating the expected format of the output. | | `example (str)` | An example string to display. Useful for indicating the expected format of the output. |
| `img (numpy.ndarray)` | Plot to another image. if not, plot to original image. | | `img (numpy.ndarray)` | Plot to another image. if not, plot to original image. |
| `labels (bool)` | Whether to plot the label of bounding boxes. | | `labels (bool)` | Whether to plot the label of bounding boxes. |
| `boxes (bool)` | Whether to plot the bounding boxes. | | `boxes (bool)` | Whether to plot the bounding boxes. |
| `masks (bool)` | Whether to plot the masks. | | `masks (bool)` | Whether to plot the masks. |
| `probs (bool)` | Whether to plot classification probability. | | `probs (bool)` | Whether to plot classification probability. |
## Streaming Source `for`-loop ## Streaming Source `for`-loop

8
docs/tasks/pose.md
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@ -56,19 +56,19 @@ Train a YOLOv8-pose model on the COCO128-pose dataset.
model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt') # build from YAML and transfer weights model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt') # build from YAML and transfer weights
# Train the model # Train the model
model.train(data='coco128-pose.yaml', epochs=100, imgsz=640) model.train(data='coco8-pose.yaml', epochs=100, imgsz=640)
``` ```
=== "CLI" === "CLI"
```bash ```bash
# Build a new model from YAML and start training from scratch # Build a new model from YAML and start training from scratch
yolo pose train data=coco128-pose.yaml model=yolov8n-pose.yaml epochs=100 imgsz=640 yolo pose train data=coco8-pose.yaml model=yolov8n-pose.yaml epochs=100 imgsz=640
# Start training from a pretrained *.pt model # Start training from a pretrained *.pt model
yolo pose train data=coco128-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640 yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
# Build a new model from YAML, transfer pretrained weights to it and start training # Build a new model from YAML, transfer pretrained weights to it and start training
yolo pose train data=coco128-pose.yaml model=yolov8n-pose.yaml pretrained=yolov8n-pose.pt epochs=100 imgsz=640 yolo pose train data=coco8-pose.yaml model=yolov8n-pose.yaml pretrained=yolov8n-pose.pt epochs=100 imgsz=640
``` ```
## Val ## Val

2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, GPL-3.0 license # Ultralytics YOLO 🚀, GPL-3.0 license
__version__ = '8.0.69' __version__ = '8.0.70'
from ultralytics.hub import start from ultralytics.hub import start
from ultralytics.yolo.engine.model import YOLO from ultralytics.yolo.engine.model import YOLO

4
ultralytics/hub/__init__.py
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@ -18,7 +18,7 @@ def login(api_key=''):
from ultralytics import hub from ultralytics import hub
hub.login('API_KEY') hub.login('API_KEY')
""" """
Auth(api_key) Auth(api_key, verbose=True)
def logout(): def logout():
@ -82,7 +82,7 @@ def export_model(model_id='', format='torchscript'):
def get_export(model_id='', format='torchscript'): def get_export(model_id='', format='torchscript'):
# Get an exported model dictionary with download URL # Get an exported model dictionary with download URL
assert format in export_fmts_hub, f"Unsupported export format '{format}', valid formats are {export_fmts_hub}" assert format in export_fmts_hub(), f"Unsupported export format '{format}', valid formats are {export_fmts_hub()}"
r = requests.post('https://api.ultralytics.com/get-export', r = requests.post('https://api.ultralytics.com/get-export',
json={ json={
'apiKey': Auth().api_key, 'apiKey': Auth().api_key,

2
ultralytics/hub/auth.py
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@ -11,7 +11,7 @@ API_KEY_URL = 'https://hub.ultralytics.com/settings?tab=api+keys'
class Auth: class Auth:
id_token = api_key = model_key = False id_token = api_key = model_key = False
def __init__(self, api_key='', verbose=True): def __init__(self, api_key='', verbose=False):
""" """
Initialize the Auth class with an optional API key. Initialize the Auth class with an optional API key.

2
ultralytics/hub/session.py
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@ -58,7 +58,7 @@ class HUBTrainingSession:
raise ValueError(f'Invalid HUBTrainingSession input: {url}') raise ValueError(f'Invalid HUBTrainingSession input: {url}')
# Authorize # Authorize
auth = Auth(key, verbose=False) auth = Auth(key)
self.agent_id = None # identifies which instance is communicating with server self.agent_id = None # identifies which instance is communicating with server
self.model_id = model_id self.model_id = model_id
self.model_url = f'https://hub.ultralytics.com/models/{model_id}' self.model_url = f'https://hub.ultralytics.com/models/{model_id}'

2
ultralytics/yolo/cfg/__init__.py
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@ -19,7 +19,7 @@ TASK2DATA = {
'detect': 'coco128.yaml', 'detect': 'coco128.yaml',
'segment': 'coco128-seg.yaml', 'segment': 'coco128-seg.yaml',
'classify': 'imagenet100', 'classify': 'imagenet100',
'pose': 'coco128-pose.yaml'} 'pose': 'coco8-pose.yaml'}
TASK2MODEL = { TASK2MODEL = {
'detect': 'yolov8n.pt', 'detect': 'yolov8n.pt',
'segment': 'yolov8n-seg.pt', 'segment': 'yolov8n-seg.pt',

13
ultralytics/yolo/engine/predictor.py
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@ -99,7 +99,7 @@ class BasePredictor:
self.device = None self.device = None
self.dataset = None self.dataset = None
self.vid_path, self.vid_writer = None, None self.vid_path, self.vid_writer = None, None
self.annotator = None self.plotted_img = None
self.data_path = None self.data_path = None
self.source_type = None self.source_type = None
self.batch = None self.batch = None
@ -109,9 +109,6 @@ class BasePredictor:
def preprocess(self, img): def preprocess(self, img):
pass pass
def get_annotator(self, img):
raise NotImplementedError('get_annotator function needs to be implemented')
def write_results(self, results, batch, print_string): def write_results(self, results, batch, print_string):
raise NotImplementedError('print_results function needs to be implemented') raise NotImplementedError('print_results function needs to be implemented')
@ -208,10 +205,10 @@ class BasePredictor:
if self.args.verbose or self.args.save or self.args.save_txt or self.args.show: if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:
s += self.write_results(i, self.results, (p, im, im0)) s += self.write_results(i, self.results, (p, im, im0))
if self.args.show: if self.args.show and self.plotted_img is not None:
self.show(p) self.show(p)
if self.args.save: if self.args.save and self.plotted_img is not None:
self.save_preds(vid_cap, i, str(self.save_dir / p.name)) self.save_preds(vid_cap, i, str(self.save_dir / p.name))
self.run_callbacks('on_predict_batch_end') self.run_callbacks('on_predict_batch_end')
yield from self.results yield from self.results
@ -251,7 +248,7 @@ class BasePredictor:
self.model.eval() self.model.eval()
def show(self, p): def show(self, p):
im0 = self.annotator.result() im0 = self.plotted_img
if platform.system() == 'Linux' and p not in self.windows: if platform.system() == 'Linux' and p not in self.windows:
self.windows.append(p) self.windows.append(p)
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
@ -260,7 +257,7 @@ class BasePredictor:
cv2.waitKey(500 if self.batch[4].startswith('image') else 1) # 1 millisecond cv2.waitKey(500 if self.batch[4].startswith('image') else 1) # 1 millisecond
def save_preds(self, vid_cap, idx, save_path): def save_preds(self, vid_cap, idx, save_path):
im0 = self.annotator.result() im0 = self.plotted_img
# save imgs # save imgs
if self.dataset.mode == 'image': if self.dataset.mode == 'image':
cv2.imwrite(save_path, im0) cv2.imwrite(save_path, im0)

25
ultralytics/yolo/engine/results.py
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@ -10,11 +10,10 @@ from functools import lru_cache
import numpy as np import numpy as np
import torch import torch
import torchvision.transforms.functional as F
from ultralytics.yolo.data.augment import LetterBox
from ultralytics.yolo.utils import LOGGER, SimpleClass, deprecation_warn, ops from ultralytics.yolo.utils import LOGGER, SimpleClass, deprecation_warn, ops
from ultralytics.yolo.utils.plotting import Annotator, colors from ultralytics.yolo.utils.plotting import Annotator, colors
from ultralytics.yolo.utils.torch_utils import TORCHVISION_0_10
class BaseTensor(SimpleClass): class BaseTensor(SimpleClass):
@ -160,6 +159,7 @@ class Results(SimpleClass):
pil=False, pil=False,
example='abc', example='abc',
img=None, img=None,
img_gpu=None,
kpt_line=True, kpt_line=True,
labels=True, labels=True,
boxes=True, boxes=True,
@ -178,6 +178,7 @@ class Results(SimpleClass):
pil (bool): Whether to return the image as a PIL Image. pil (bool): Whether to return the image as a PIL Image.
example (str): An example string to display. Useful for indicating the expected format of the output. example (str): An example string to display. Useful for indicating the expected format of the output.
img (numpy.ndarray): Plot to another image. if not, plot to original image. img (numpy.ndarray): Plot to another image. if not, plot to original image.
img_gpu (torch.Tensor): Normalized image in gpu with shape (1, 3, 640, 640), for faster mask plotting.
kpt_line (bool): Whether to draw lines connecting keypoints. kpt_line (bool): Whether to draw lines connecting keypoints.
labels (bool): Whether to plot the label of bounding boxes. labels (bool): Whether to plot the label of bounding boxes.
boxes (bool): Whether to plot the bounding boxes. boxes (bool): Whether to plot the bounding boxes.
@ -185,7 +186,7 @@ class Results(SimpleClass):
probs (bool): Whether to plot classification probability probs (bool): Whether to plot classification probability
Returns: Returns:
(None) or (PIL.Image): If `pil` is True, a PIL Image is returned. Otherwise, nothing is returned. (numpy.ndarray): A numpy array of the annotated image.
""" """
# Deprecation warn TODO: remove in 8.2 # Deprecation warn TODO: remove in 8.2
if 'show_conf' in kwargs: if 'show_conf' in kwargs:
@ -200,6 +201,13 @@ class Results(SimpleClass):
pred_probs, show_probs = self.probs, probs pred_probs, show_probs = self.probs, probs
names = self.names names = self.names
keypoints = self.keypoints keypoints = self.keypoints
if pred_masks and show_masks:
if img_gpu is None:
img = LetterBox(pred_masks.shape[1:])(image=annotator.im)
img_gpu = torch.as_tensor(img, dtype=torch.float16, device=pred_masks.masks.device).permute(
2, 0, 1).flip(0).contiguous() / 255
annotator.masks(pred_masks.data, colors=[colors(x, True) for x in pred_boxes.cls], im_gpu=img_gpu)
if pred_boxes and show_boxes: if pred_boxes and show_boxes:
for d in reversed(pred_boxes): for d in reversed(pred_boxes):
c, conf, id = int(d.cls), float(d.conf) if conf else None, None if d.id is None else int(d.id.item()) c, conf, id = int(d.cls), float(d.conf) if conf else None, None if d.id is None else int(d.id.item())
@ -207,15 +215,6 @@ class Results(SimpleClass):
label = (f'{name} {conf:.2f}' if conf else name) if labels else None label = (f'{name} {conf:.2f}' if conf else name) if labels else None
annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True)) annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
if pred_masks and show_masks:
im = torch.as_tensor(annotator.im, dtype=torch.float16, device=pred_masks.data.device).permute(2, 0,
1).flip(0)
if TORCHVISION_0_10:
im = F.resize(im.contiguous(), pred_masks.data.shape[1:], antialias=True) / 255
else:
im = F.resize(im.contiguous(), pred_masks.data.shape[1:]) / 255
annotator.masks(pred_masks.data, colors=[colors(x, True) for x in pred_boxes.cls], im_gpu=im)
if pred_probs is not None and show_probs: if pred_probs is not None and show_probs:
n5 = min(len(names), 5) n5 = min(len(names), 5)
top5i = pred_probs.argsort(0, descending=True)[:n5].tolist() # top 5 indices top5i = pred_probs.argsort(0, descending=True)[:n5].tolist() # top 5 indices
@ -226,7 +225,7 @@ class Results(SimpleClass):
for k in reversed(keypoints): for k in reversed(keypoints):
annotator.kpts(k, self.orig_shape, kpt_line=kpt_line) annotator.kpts(k, self.orig_shape, kpt_line=kpt_line)
return np.asarray(annotator.im) if annotator.pil else annotator.im return annotator.result()
class Boxes(BaseTensor): class Boxes(BaseTensor):

11
ultralytics/yolo/utils/ops.py
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@ -300,13 +300,12 @@ def clip_coords(coords, shape):
coords[..., 1] = coords[..., 1].clip(0, shape[0]) # y coords[..., 1] = coords[..., 1].clip(0, shape[0]) # y
def scale_image(im1_shape, masks, im0_shape, ratio_pad=None): def scale_image(masks, im0_shape, ratio_pad=None):
""" """
Takes a mask, and resizes it to the original image size Takes a mask, and resizes it to the original image size
Args: Args:
im1_shape (tuple): model input shape, [h, w] masks (torch.Tensor): resized and padded masks/images, [h, w, num]/[h, w, 3].
masks (torch.Tensor): [h, w, num]
im0_shape (tuple): the original image shape im0_shape (tuple): the original image shape
ratio_pad (tuple): the ratio of the padding to the original image. ratio_pad (tuple): the ratio of the padding to the original image.
@ -314,10 +313,14 @@ def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
masks (torch.Tensor): The masks that are being returned. masks (torch.Tensor): The masks that are being returned.
""" """
# Rescale coordinates (xyxy) from im1_shape to im0_shape # Rescale coordinates (xyxy) from im1_shape to im0_shape
im1_shape = masks.shape
if im1_shape[:2] == im0_shape[:2]:
return masks
if ratio_pad is None: # calculate from im0_shape if ratio_pad is None: # calculate from im0_shape
gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new
pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding
else: else:
gain = ratio_pad[0][0]
pad = ratio_pad[1] pad = ratio_pad[1]
top, left = int(pad[1]), int(pad[0]) # y, x top, left = int(pad[1]), int(pad[0]) # y, x
bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])
@ -329,9 +332,9 @@ def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
# masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0] # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]
# masks = masks.permute(1, 2, 0).contiguous() # masks = masks.permute(1, 2, 0).contiguous()
masks = cv2.resize(masks, (im0_shape[1], im0_shape[0])) masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))
if len(masks.shape) == 2: if len(masks.shape) == 2:
masks = masks[:, :, None] masks = masks[:, :, None]
return masks return masks

8
ultralytics/yolo/utils/plotting.py
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@ -138,7 +138,7 @@ class Annotator:
im_gpu = im_gpu * inv_alph_masks[-1] + mcs im_gpu = im_gpu * inv_alph_masks[-1] + mcs
im_mask = (im_gpu * 255) im_mask = (im_gpu * 255)
im_mask_np = im_mask.byte().cpu().numpy() im_mask_np = im_mask.byte().cpu().numpy()
self.im[:] = im_mask_np if retina_masks else scale_image(im_gpu.shape, im_mask_np, self.im.shape) self.im[:] = im_mask_np if retina_masks else scale_image(im_mask_np, self.im.shape)
if self.pil: if self.pil:
# convert im back to PIL and update draw # convert im back to PIL and update draw
self.fromarray(self.im) self.fromarray(self.im)
@ -165,11 +165,11 @@ class Annotator:
conf = k[2] conf = k[2]
if conf < 0.5: if conf < 0.5:
continue continue
cv2.circle(self.im, (int(x_coord), int(y_coord)), radius, color_k, -1) cv2.circle(self.im, (int(x_coord), int(y_coord)), radius, color_k, -1, lineType=cv2.LINE_AA)
if kpt_line: if kpt_line:
ndim = kpts.shape[-1] ndim = kpts.shape[-1]
for sk_id, sk in enumerate(self.skeleton): for i, sk in enumerate(self.skeleton):
pos1 = (int(kpts[(sk[0] - 1), 0]), int(kpts[(sk[0] - 1), 1])) pos1 = (int(kpts[(sk[0] - 1), 0]), int(kpts[(sk[0] - 1), 1]))
pos2 = (int(kpts[(sk[1] - 1), 0]), int(kpts[(sk[1] - 1), 1])) pos2 = (int(kpts[(sk[1] - 1), 0]), int(kpts[(sk[1] - 1), 1]))
if ndim == 3: if ndim == 3:
@ -181,7 +181,7 @@ class Annotator:
continue continue
if pos2[0] % shape[1] == 0 or pos2[1] % shape[0] == 0 or pos2[0] < 0 or pos2[1] < 0: if pos2[0] % shape[1] == 0 or pos2[1] % shape[0] == 0 or pos2[0] < 0 or pos2[1] < 0:
continue continue
cv2.line(self.im, pos1, pos2, [int(x) for x in self.limb_color[sk_id]], thickness=2) cv2.line(self.im, pos1, pos2, [int(x) for x in self.limb_color[i]], thickness=2, lineType=cv2.LINE_AA)
if self.pil: if self.pil:
# convert im back to PIL and update draw # convert im back to PIL and update draw
self.fromarray(self.im) self.fromarray(self.im)

5
ultralytics/yolo/v8/classify/predict.py
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@ -44,7 +44,6 @@ class ClassificationPredictor(BasePredictor):
# save_path = str(self.save_dir / p.name) # im.jpg # save_path = str(self.save_dir / p.name) # im.jpg
self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
log_string += '%gx%g ' % im.shape[2:] # print string log_string += '%gx%g ' % im.shape[2:] # print string
self.annotator = self.get_annotator(im0)
result = results[idx] result = results[idx]
if len(result) == 0: if len(result) == 0:
@ -56,10 +55,10 @@ class ClassificationPredictor(BasePredictor):
log_string += f"{', '.join(f'{self.model.names[j]} {prob[j]:.2f}' for j in top5i)}, " log_string += f"{', '.join(f'{self.model.names[j]} {prob[j]:.2f}' for j in top5i)}, "
# write # write
text = '\n'.join(f'{prob[j]:.2f} {self.model.names[j]}' for j in top5i)
if self.args.save or self.args.show: # Add bbox to image if self.args.save or self.args.show: # Add bbox to image
self.annotator.text((32, 32), text, txt_color=(255, 255, 255)) self.plotted_img = result.plot()
if self.args.save_txt: # Write to file if self.args.save_txt: # Write to file
text = '\n'.join(f'{prob[j]:.2f} {self.model.names[j]}' for j in top5i)
with open(f'{self.txt_path}.txt', 'a') as f: with open(f'{self.txt_path}.txt', 'a') as f:
f.write(text + '\n') f.write(text + '\n')

18
ultralytics/yolo/v8/detect/predict.py
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@ -5,14 +5,11 @@ import torch
from ultralytics.yolo.engine.predictor import BasePredictor from ultralytics.yolo.engine.predictor import BasePredictor
from ultralytics.yolo.engine.results import Results from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box from ultralytics.yolo.utils.plotting import save_one_box
class DetectionPredictor(BasePredictor): class DetectionPredictor(BasePredictor):
def get_annotator(self, img):
return Annotator(img, line_width=self.args.line_thickness, example=str(self.model.names))
def preprocess(self, img): def preprocess(self, img):
img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device) img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32 img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
@ -52,15 +49,18 @@ class DetectionPredictor(BasePredictor):
self.data_path = p self.data_path = p
self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
log_string += '%gx%g ' % im.shape[2:] # print string log_string += '%gx%g ' % im.shape[2:] # print string
self.annotator = self.get_annotator(im0)
det = results[idx].boxes # TODO: make boxes inherit from tensors result = results[idx] # TODO: make boxes inherit from tensors
if len(det) == 0: if len(result) == 0:
return f'{log_string}(no detections), ' return f'{log_string}(no detections), '
det = result.boxes
for c in det.cls.unique(): for c in det.cls.unique():
n = (det.cls == c).sum() # detections per class n = (det.cls == c).sum() # detections per class
log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, " log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "
if self.args.save or self.args.show: # Add bbox to image
self.plotted_img = result.plot(line_width=self.args.line_thickness)
# write # write
for d in reversed(det): for d in reversed(det):
c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item()) c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
@ -68,10 +68,6 @@ class DetectionPredictor(BasePredictor):
line = (c, *d.xywhn.view(-1)) + (conf, ) * self.args.save_conf + (() if id is None else (id, )) line = (c, *d.xywhn.view(-1)) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
with open(f'{self.txt_path}.txt', 'a') as f: with open(f'{self.txt_path}.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n') f.write(('%g ' * len(line)).rstrip() % line + '\n')
if self.args.save or self.args.show: # Add bbox to image
name = ('' if id is None else f'id:{id} ') + self.model.names[c]
label = (f'{name} {conf:.2f}' if self.args.show_conf else name) if self.args.show_labels else None
self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
if self.args.save_crop: if self.args.save_crop:
save_one_box(d.xyxy, save_one_box(d.xyxy,
imc, imc,

20
ultralytics/yolo/v8/pose/predict.py
Unescape View File

@ -2,7 +2,7 @@
from ultralytics.yolo.engine.results import Results from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
from ultralytics.yolo.utils.plotting import colors, save_one_box from ultralytics.yolo.utils.plotting import save_one_box
from ultralytics.yolo.v8.detect.predict import DetectionPredictor from ultralytics.yolo.v8.detect.predict import DetectionPredictor
@ -49,33 +49,27 @@ class PosePredictor(DetectionPredictor):
self.data_path = p self.data_path = p
self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
log_string += '%gx%g ' % im.shape[2:] # print string log_string += '%gx%g ' % im.shape[2:] # print string
self.annotator = self.get_annotator(im0)
det = results[idx].boxes # TODO: make boxes inherit from tensors result = results[idx] # TODO: make boxes inherit from tensors
if len(det) == 0: if len(result) == 0:
return f'{log_string}(no detections), ' return f'{log_string}(no detections), '
det = result.boxes
for c in det.cls.unique(): for c in det.cls.unique():
n = (det.cls == c).sum() # detections per class n = (det.cls == c).sum() # detections per class
log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, " log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "
kpts = reversed(results[idx].keypoints) if self.args.save or self.args.show: # Add bbox to image
for k in kpts: self.plotted_img = result.plot(line_width=self.args.line_thickness, boxes=self.args.boxes)
self.annotator.kpts(k, shape=results[idx].orig_shape)
# write # write
for j, d in enumerate(reversed(det)): for j, d in enumerate(reversed(det)):
c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item()) c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
if self.args.save_txt: # Write to file if self.args.save_txt: # Write to file
kpt = (kpts[j][:, :2] / d.orig_shape[[1, 0]]).reshape(-1).tolist() kpt = (result[j].keypoints[:, :2] / d.orig_shape[[1, 0]]).reshape(-1).tolist()
box = d.xywhn.view(-1).tolist() box = d.xywhn.view(-1).tolist()
line = (c, *box, *kpt) + (conf, ) * self.args.save_conf + (() if id is None else (id, )) line = (c, *box, *kpt) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
with open(f'{self.txt_path}.txt', 'a') as f: with open(f'{self.txt_path}.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n') f.write(('%g ' * len(line)).rstrip() % line + '\n')
if self.args.save or self.args.show: # Add bbox to image
name = ('' if id is None else f'id:{id} ') + self.model.names[c]
label = (f'{name} {conf:.2f}' if self.args.show_conf else name) if self.args.show_labels else None
if self.args.boxes:
self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
if self.args.save_crop: if self.args.save_crop:
save_one_box(d.xyxy, save_one_box(d.xyxy,
imc, imc,

2
ultralytics/yolo/v8/pose/val.py
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@ -198,7 +198,7 @@ class PoseValidator(DetectionValidator):
def val(cfg=DEFAULT_CFG, use_python=False): def val(cfg=DEFAULT_CFG, use_python=False):
model = cfg.model or 'yolov8n-pose.pt' model = cfg.model or 'yolov8n-pose.pt'
data = cfg.data or 'coco128-pose.yaml' data = cfg.data or 'coco8-pose.yaml'
args = dict(model=model, data=data) args = dict(model=model, data=data)
if use_python: if use_python:

10
ultralytics/yolo/v8/segment/predict.py
Unescape View File

@ -4,7 +4,7 @@ import torch
from ultralytics.yolo.engine.results import Results from ultralytics.yolo.engine.results import Results
from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
from ultralytics.yolo.utils.plotting import colors, save_one_box from ultralytics.yolo.utils.plotting import save_one_box
from ultralytics.yolo.v8.detect.predict import DetectionPredictor from ultralytics.yolo.v8.detect.predict import DetectionPredictor
@ -56,7 +56,6 @@ class SegmentationPredictor(DetectionPredictor):
self.data_path = p self.data_path = p
self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}') self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
log_string += '%gx%g ' % im.shape[2:] # print string log_string += '%gx%g ' % im.shape[2:] # print string
self.annotator = self.get_annotator(im0)
result = results[idx] result = results[idx]
if len(result) == 0: if len(result) == 0:
@ -72,7 +71,7 @@ class SegmentationPredictor(DetectionPredictor):
if self.args.save or self.args.show: if self.args.save or self.args.show:
im_gpu = torch.as_tensor(im0, dtype=torch.float16, device=mask.masks.device).permute( im_gpu = torch.as_tensor(im0, dtype=torch.float16, device=mask.masks.device).permute(
2, 0, 1).flip(0).contiguous() / 255 if self.args.retina_masks else im[idx] 2, 0, 1).flip(0).contiguous() / 255 if self.args.retina_masks else im[idx]
self.annotator.masks(masks=mask.masks, colors=[colors(x, True) for x in det.cls], im_gpu=im_gpu) self.plotted_img = result.plot(line_width=self.args.line_thickness, im_gpu=im_gpu, boxes=self.args.boxes)
# Write results # Write results
for j, d in enumerate(reversed(det)): for j, d in enumerate(reversed(det)):
@ -82,11 +81,6 @@ class SegmentationPredictor(DetectionPredictor):
line = (c, *seg) + (conf, ) * self.args.save_conf + (() if id is None else (id, )) line = (c, *seg) + (conf, ) * self.args.save_conf + (() if id is None else (id, ))
with open(f'{self.txt_path}.txt', 'a') as f: with open(f'{self.txt_path}.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n') f.write(('%g ' * len(line)).rstrip() % line + '\n')
if self.args.save or self.args.show: # Add bbox to image
name = ('' if id is None else f'id:{id} ') + self.model.names[c]
label = (f'{name} {conf:.2f}' if self.args.show_conf else name) if self.args.show_labels else None
if self.args.boxes:
self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
if self.args.save_crop: if self.args.save_crop:
save_one_box(d.xyxy, save_one_box(d.xyxy,
imc, imc,

3
ultralytics/yolo/v8/segment/val.py
Unescape View File

@ -111,8 +111,7 @@ class SegmentationValidator(DetectionValidator):
# Save # Save
if self.args.save_json: if self.args.save_json:
pred_masks = ops.scale_image(batch['img'][si].shape[1:], pred_masks = ops.scale_image(pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(),
pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(),
shape, shape,
ratio_pad=batch['ratio_pad'][si]) ratio_pad=batch['ratio_pad'][si])
self.pred_to_json(predn, batch['im_file'][si], pred_masks) self.pred_to_json(predn, batch['im_file'][si], pred_masks)

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