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ultralytics 8.0.89 SAM predict and auto-annotate (#2298)

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This commit is contained in:
Glenn Jocher
2023-04-28 00:36:50 +02:00
committed by GitHub
parent 3e118f6170
commit 243fc4b1fe
44 changed files with 2915 additions and 440 deletions
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66
ultralytics/yolo/data/dataloaders/stream_loaders.py
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@ -15,7 +15,6 @@ import requests
import torch
from PIL import Image
from ultralytics.yolo.data.augment import LetterBox
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
from ultralytics.yolo.utils import LOGGER, ROOT, is_colab, is_kaggle, ops
from ultralytics.yolo.utils.checks import check_requirements
@ -31,12 +30,11 @@ class SourceTypes:
class LoadStreams:
# YOLOv8 streamloader, i.e. `yolo predict source='rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams`
def __init__(self, sources='file.streams', imgsz=640, stride=32, auto=True, transforms=None, vid_stride=1):
def __init__(self, sources='file.streams', imgsz=640, vid_stride=1):
"""Initialize instance variables and check for consistent input stream shapes."""
torch.backends.cudnn.benchmark = True # faster for fixed-size inference
self.mode = 'stream'
self.imgsz = imgsz
self.stride = stride
self.vid_stride = vid_stride # video frame-rate stride
sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]
n = len(sources)
@ -72,10 +70,6 @@ class LoadStreams:
LOGGER.info('') # newline
# Check for common shapes
s = np.stack([LetterBox(imgsz, auto, stride=stride)(image=x).shape for x in self.imgs])
self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal
self.auto = auto and self.rect
self.transforms = transforms # optional
self.bs = self.__len__()
if not self.rect:
@ -110,14 +104,7 @@ class LoadStreams:
raise StopIteration
im0 = self.imgs.copy()
if self.transforms:
im = np.stack([self.transforms(x) for x in im0]) # transforms
else:
im = np.stack([LetterBox(self.imgsz, self.auto, stride=self.stride)(image=x) for x in im0])
im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW
im = np.ascontiguousarray(im) # contiguous
return self.sources, im, im0, None, ''
return self.sources, im0, None, ''
def __len__(self):
"""Return the length of the sources object."""
@ -126,7 +113,7 @@ class LoadStreams:
class LoadScreenshots:
# YOLOv8 screenshot dataloader, i.e. `yolo predict source=screen`
def __init__(self, source, imgsz=640, stride=32, auto=True, transforms=None):
def __init__(self, source, imgsz=640):
"""source = [screen_number left top width height] (pixels)."""
check_requirements('mss')
import mss # noqa
@ -140,9 +127,6 @@ class LoadScreenshots:
elif len(params) == 5:
self.screen, left, top, width, height = (int(x) for x in params)
self.imgsz = imgsz
self.stride = stride
self.transforms = transforms
self.auto = auto
self.mode = 'stream'
self.frame = 0
self.sct = mss.mss()
@ -165,19 +149,13 @@ class LoadScreenshots:
im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR
s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: '
if self.transforms:
im = self.transforms(im0) # transforms
else:
im = LetterBox(self.imgsz, self.auto, stride=self.stride)(image=im0)
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
im = np.ascontiguousarray(im) # contiguous
self.frame += 1
return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s
return str(self.screen), im0, None, s # screen, img, original img, im0s, s
class LoadImages:
# YOLOv8 image/video dataloader, i.e. `yolo predict source=image.jpg/vid.mp4`
def __init__(self, path, imgsz=640, stride=32, auto=True, transforms=None, vid_stride=1):
def __init__(self, path, imgsz=640, vid_stride=1):
"""Initialize the Dataloader and raise FileNotFoundError if file not found."""
if isinstance(path, str) and Path(path).suffix == '.txt': # *.txt file with img/vid/dir on each line
path = Path(path).read_text().rsplit()
@ -198,13 +176,10 @@ class LoadImages:
ni, nv = len(images), len(videos)
self.imgsz = imgsz
self.stride = stride
self.files = images + videos
self.nf = ni + nv # number of files
self.video_flag = [False] * ni + [True] * nv
self.mode = 'image'
self.auto = auto
self.transforms = transforms # optional
self.vid_stride = vid_stride # video frame-rate stride
self.bs = 1
if any(videos):
@ -254,14 +229,7 @@ class LoadImages:
raise FileNotFoundError(f'Image Not Found {path}')
s = f'image {self.count}/{self.nf} {path}: '
if self.transforms:
im = self.transforms(im0) # transforms
else:
im = LetterBox(self.imgsz, self.auto, stride=self.stride)(image=im0)
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
im = np.ascontiguousarray(im) # contiguous
return path, im, im0, self.cap, s
return [path], [im0], self.cap, s
def _new_video(self, path):
"""Create a new video capture object."""
@ -290,16 +258,13 @@ class LoadImages:
class LoadPilAndNumpy:
def __init__(self, im0, imgsz=640, stride=32, auto=True, transforms=None):
def __init__(self, im0, imgsz=640):
"""Initialize PIL and Numpy Dataloader."""
if not isinstance(im0, list):
im0 = [im0]
self.paths = [getattr(im, 'filename', f'image{i}.jpg') for i, im in enumerate(im0)]
self.im0 = [self._single_check(im) for im in im0]
self.imgsz = imgsz
self.stride = stride
self.auto = auto
self.transforms = transforms
self.mode = 'image'
# Generate fake paths
self.bs = len(self.im0)
@ -315,16 +280,6 @@ class LoadPilAndNumpy:
im = np.ascontiguousarray(im) # contiguous
return im
def _single_preprocess(self, im, auto):
"""Preprocesses a single image for inference."""
if self.transforms:
im = self.transforms(im) # transforms
else:
im = LetterBox(self.imgsz, auto=auto, stride=self.stride)(image=im)
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
im = np.ascontiguousarray(im) # contiguous
return im
def __len__(self):
"""Returns the length of the 'im0' attribute."""
return len(self.im0)
@ -333,11 +288,8 @@ class LoadPilAndNumpy:
"""Returns batch paths, images, processed images, None, ''."""
if self.count == 1: # loop only once as it's batch inference
raise StopIteration
auto = all(x.shape == self.im0[0].shape for x in self.im0) and self.auto
im = [self._single_preprocess(im, auto) for im in self.im0]
im = np.stack(im, 0) if len(im) > 1 else im[0][None]
self.count += 1
return self.paths, im, self.im0, None, ''
return self.paths, self.im0, None, ''
def __iter__(self):
"""Enables iteration for class LoadPilAndNumpy."""
@ -362,7 +314,7 @@ class LoadTensor:
if self.count == 1:
raise StopIteration
self.count += 1
return None, self.im0, self.im0, None, '' # self.paths, im, self.im0, None, ''
return None, self.im0, None, '' # self.paths, im, self.im0, None, ''
def __len__(self):
"""Returns the batch size."""