You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
374 lines
15 KiB
374 lines
15 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
|
|
|
|
import glob
|
|
import math
|
|
import os
|
|
import time
|
|
from dataclasses import dataclass
|
|
from pathlib import Path
|
|
from threading import Thread
|
|
from urllib.parse import urlparse
|
|
|
|
import cv2
|
|
import numpy as np
|
|
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
|
|
|
|
|
|
@dataclass
|
|
class SourceTypes:
|
|
webcam: bool = False
|
|
screenshot: bool = False
|
|
from_img: bool = False
|
|
tensor: bool = False
|
|
|
|
|
|
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):
|
|
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)
|
|
self.sources = [ops.clean_str(x) for x in sources] # clean source names for later
|
|
self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n
|
|
for i, s in enumerate(sources): # index, source
|
|
# Start thread to read frames from video stream
|
|
st = f'{i + 1}/{n}: {s}... '
|
|
if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'): # if source is YouTube video
|
|
# YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc'
|
|
check_requirements(('pafy', 'youtube_dl==2020.12.2'))
|
|
import pafy # noqa
|
|
s = pafy.new(s).getbest(preftype='mp4').url # YouTube URL
|
|
s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam
|
|
if s == 0 and (is_colab() or is_kaggle()):
|
|
raise NotImplementedError("'source=0' webcam not supported in Colab and Kaggle notebooks. "
|
|
"Try running 'source=0' in a local environment.")
|
|
cap = cv2.VideoCapture(s)
|
|
if not cap.isOpened():
|
|
raise ConnectionError(f'{st}Failed to open {s}')
|
|
w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
|
h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
|
fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan
|
|
self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback
|
|
self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback
|
|
|
|
success, self.imgs[i] = cap.read() # guarantee first frame
|
|
if not success or self.imgs[i] is None:
|
|
raise ConnectionError(f'{st}Failed to read images from {s}')
|
|
self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
|
|
LOGGER.info(f'{st}Success ✅ ({self.frames[i]} frames of shape {w}x{h} at {self.fps[i]:.2f} FPS)')
|
|
self.threads[i].start()
|
|
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:
|
|
LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.')
|
|
|
|
def update(self, i, cap, stream):
|
|
# Read stream `i` frames in daemon thread
|
|
n, f = 0, self.frames[i] # frame number, frame array
|
|
while cap.isOpened() and n < f:
|
|
n += 1
|
|
cap.grab() # .read() = .grab() followed by .retrieve()
|
|
if n % self.vid_stride == 0:
|
|
success, im = cap.retrieve()
|
|
if success:
|
|
self.imgs[i] = im
|
|
else:
|
|
LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.')
|
|
self.imgs[i] = np.zeros_like(self.imgs[i])
|
|
cap.open(stream) # re-open stream if signal was lost
|
|
time.sleep(0.0) # wait time
|
|
|
|
def __iter__(self):
|
|
self.count = -1
|
|
return self
|
|
|
|
def __next__(self):
|
|
self.count += 1
|
|
if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'): # q to quit
|
|
cv2.destroyAllWindows()
|
|
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, ''
|
|
|
|
def __len__(self):
|
|
return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years
|
|
|
|
|
|
class LoadScreenshots:
|
|
# YOLOv8 screenshot dataloader, i.e. `yolo predict source=screen`
|
|
def __init__(self, source, imgsz=640, stride=32, auto=True, transforms=None):
|
|
# source = [screen_number left top width height] (pixels)
|
|
check_requirements('mss')
|
|
import mss # noqa
|
|
|
|
source, *params = source.split()
|
|
self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0
|
|
if len(params) == 1:
|
|
self.screen = int(params[0])
|
|
elif len(params) == 4:
|
|
left, top, width, height = (int(x) for x in params)
|
|
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()
|
|
self.bs = 1
|
|
|
|
# Parse monitor shape
|
|
monitor = self.sct.monitors[self.screen]
|
|
self.top = monitor['top'] if top is None else (monitor['top'] + top)
|
|
self.left = monitor['left'] if left is None else (monitor['left'] + left)
|
|
self.width = width or monitor['width']
|
|
self.height = height or monitor['height']
|
|
self.monitor = {'left': self.left, 'top': self.top, 'width': self.width, 'height': self.height}
|
|
|
|
def __iter__(self):
|
|
return self
|
|
|
|
def __next__(self):
|
|
# mss screen capture: get raw pixels from the screen as np array
|
|
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
|
|
|
|
|
|
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):
|
|
if isinstance(path, str) and Path(path).suffix == '.txt': # *.txt file with img/vid/dir on each line
|
|
path = Path(path).read_text().rsplit()
|
|
files = []
|
|
for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
|
|
p = str(Path(p).resolve())
|
|
if '*' in p:
|
|
files.extend(sorted(glob.glob(p, recursive=True))) # glob
|
|
elif os.path.isdir(p):
|
|
files.extend(sorted(glob.glob(os.path.join(p, '*.*')))) # dir
|
|
elif os.path.isfile(p):
|
|
files.append(p) # files
|
|
else:
|
|
raise FileNotFoundError(f'{p} does not exist')
|
|
|
|
images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
|
|
videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
|
|
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):
|
|
self.orientation = None # rotation degrees
|
|
self._new_video(videos[0]) # new video
|
|
else:
|
|
self.cap = None
|
|
if self.nf == 0:
|
|
raise FileNotFoundError(f'No images or videos found in {p}. '
|
|
f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}')
|
|
|
|
def __iter__(self):
|
|
self.count = 0
|
|
return self
|
|
|
|
def __next__(self):
|
|
if self.count == self.nf:
|
|
raise StopIteration
|
|
path = self.files[self.count]
|
|
|
|
if self.video_flag[self.count]:
|
|
# Read video
|
|
self.mode = 'video'
|
|
for _ in range(self.vid_stride):
|
|
self.cap.grab()
|
|
success, im0 = self.cap.retrieve()
|
|
while not success:
|
|
self.count += 1
|
|
self.cap.release()
|
|
if self.count == self.nf: # last video
|
|
raise StopIteration
|
|
path = self.files[self.count]
|
|
self._new_video(path)
|
|
success, im0 = self.cap.read()
|
|
|
|
self.frame += 1
|
|
# im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False
|
|
s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: '
|
|
|
|
else:
|
|
# Read image
|
|
self.count += 1
|
|
im0 = cv2.imread(path) # BGR
|
|
if im0 is None:
|
|
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
|
|
|
|
def _new_video(self, path):
|
|
# Create a new video capture object
|
|
self.frame = 0
|
|
self.cap = cv2.VideoCapture(path)
|
|
self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
|
|
if hasattr(cv2, 'CAP_PROP_ORIENTATION_META'): # cv2<4.6.0 compatibility
|
|
self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees
|
|
# Disable auto-orientation due to known issues in https://github.com/ultralytics/yolov5/issues/8493
|
|
# self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0)
|
|
|
|
def _cv2_rotate(self, im):
|
|
# Rotate a cv2 video manually
|
|
if self.orientation == 0:
|
|
return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)
|
|
elif self.orientation == 180:
|
|
return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE)
|
|
elif self.orientation == 90:
|
|
return cv2.rotate(im, cv2.ROTATE_180)
|
|
return im
|
|
|
|
def __len__(self):
|
|
return self.nf # number of files
|
|
|
|
|
|
class LoadPilAndNumpy:
|
|
|
|
def __init__(self, im0, imgsz=640, stride=32, auto=True, transforms=None):
|
|
if not isinstance(im0, list):
|
|
im0 = [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.paths = [getattr(im, 'filename', f'image{i}.jpg') for i, im in enumerate(self.im0)]
|
|
self.bs = len(self.im0)
|
|
|
|
@staticmethod
|
|
def _single_check(im):
|
|
assert isinstance(im, (Image.Image, np.ndarray)), f'Expected PIL/np.ndarray image type, but got {type(im)}'
|
|
if isinstance(im, Image.Image):
|
|
if im.mode != 'RGB':
|
|
im = im.convert('RGB')
|
|
im = np.asarray(im)[:, :, ::-1]
|
|
im = np.ascontiguousarray(im) # contiguous
|
|
return im
|
|
|
|
def _single_preprocess(self, im, auto):
|
|
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):
|
|
return len(self.im0)
|
|
|
|
def __next__(self):
|
|
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, ''
|
|
|
|
def __iter__(self):
|
|
self.count = 0
|
|
return self
|
|
|
|
|
|
class LoadTensor:
|
|
|
|
def __init__(self, imgs) -> None:
|
|
self.im0 = imgs
|
|
self.bs = imgs.shape[0]
|
|
|
|
def __iter__(self):
|
|
self.count = 0
|
|
return self
|
|
|
|
def __next__(self):
|
|
if self.count == 1:
|
|
raise StopIteration
|
|
self.count += 1
|
|
return None, self.im0, self.im0, None, '' # self.paths, im, self.im0, None, ''
|
|
|
|
|
|
def autocast_list(source):
|
|
"""
|
|
Merges a list of source of different types into a list of numpy arrays or PIL images
|
|
"""
|
|
files = []
|
|
for im in source:
|
|
if isinstance(im, (str, Path)): # filename or uri
|
|
files.append(Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im))
|
|
elif isinstance(im, (Image.Image, np.ndarray)): # PIL or np Image
|
|
files.append(im)
|
|
else:
|
|
raise TypeError(f'type {type(im).__name__} is not a supported Ultralytics prediction source type. \n'
|
|
f'See https://docs.ultralytics.com/predict for supported source types.')
|
|
|
|
return files
|
|
|
|
|
|
LOADERS = [LoadStreams, LoadPilAndNumpy, LoadImages, LoadScreenshots]
|
|
|
|
if __name__ == '__main__':
|
|
img = cv2.imread(str(ROOT / 'assets/bus.jpg'))
|
|
dataset = LoadPilAndNumpy(im0=img)
|
|
for d in dataset:
|
|
print(d[0])
|