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255 lines
11 KiB
255 lines
11 KiB
import glob
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import math
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import os
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import time
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from pathlib import Path
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from threading import Thread
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from urllib.parse import urlparse
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import cv2
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import numpy as np
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import torch
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from ultralytics.yolo.data.augment import LetterBox
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from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
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from ultralytics.yolo.utils import LOGGER, is_colab, is_kaggle, ops
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from ultralytics.yolo.utils.checks import check_requirements
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class LoadStreams:
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# YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams`
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def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
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torch.backends.cudnn.benchmark = True # faster for fixed-size inference
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self.mode = 'stream'
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self.img_size = img_size
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self.stride = stride
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self.vid_stride = vid_stride # video frame-rate stride
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sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]
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n = len(sources)
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self.sources = [ops.clean_str(x) for x in sources] # clean source names for later
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self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n
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for i, s in enumerate(sources): # index, source
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# Start thread to read frames from video stream
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st = f'{i + 1}/{n}: {s}... '
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if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'): # if source is YouTube video
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# YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc'
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check_requirements(('pafy', 'youtube_dl==2020.12.2'))
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import pafy
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s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL
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s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam
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if s == 0:
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assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.'
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assert not is_kaggle(), '--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.'
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cap = cv2.VideoCapture(s)
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assert cap.isOpened(), f'{st}Failed to open {s}'
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w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan
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self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback
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self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback
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_, self.imgs[i] = cap.read() # guarantee first frame
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self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
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LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
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self.threads[i].start()
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LOGGER.info('') # newline
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# check for common shapes
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s = np.stack([LetterBox(img_size, auto, stride=stride)(image=x).shape for x in self.imgs])
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self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal
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self.auto = auto and self.rect
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self.transforms = transforms # optional
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if not self.rect:
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LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.')
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def update(self, i, cap, stream):
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# Read stream `i` frames in daemon thread
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n, f = 0, self.frames[i] # frame number, frame array
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while cap.isOpened() and n < f:
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n += 1
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cap.grab() # .read() = .grab() followed by .retrieve()
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if n % self.vid_stride == 0:
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success, im = cap.retrieve()
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if success:
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self.imgs[i] = im
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else:
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LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.')
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self.imgs[i] = np.zeros_like(self.imgs[i])
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cap.open(stream) # re-open stream if signal was lost
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time.sleep(0.0) # wait time
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def __iter__(self):
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self.count = -1
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return self
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def __next__(self):
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self.count += 1
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if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'): # q to quit
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cv2.destroyAllWindows()
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raise StopIteration
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im0 = self.imgs.copy()
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if self.transforms:
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im = np.stack([self.transforms(x) for x in im0]) # transforms
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else:
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im = np.stack([LetterBox(self.img_size, self.auto, stride=self.stride)(image=x) for x in im0])
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im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW
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im = np.ascontiguousarray(im) # contiguous
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return self.sources, im, im0, None, ''
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def __len__(self):
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return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years
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class LoadScreenshots:
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# YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"`
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def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
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# source = [screen_number left top width height] (pixels)
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check_requirements('mss')
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import mss
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source, *params = source.split()
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self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0
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if len(params) == 1:
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self.screen = int(params[0])
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elif len(params) == 4:
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left, top, width, height = (int(x) for x in params)
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elif len(params) == 5:
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self.screen, left, top, width, height = (int(x) for x in params)
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self.img_size = img_size
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self.stride = stride
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self.transforms = transforms
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self.auto = auto
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self.mode = 'stream'
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self.frame = 0
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self.sct = mss.mss()
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# Parse monitor shape
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monitor = self.sct.monitors[self.screen]
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self.top = monitor["top"] if top is None else (monitor["top"] + top)
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self.left = monitor["left"] if left is None else (monitor["left"] + left)
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self.width = width or monitor["width"]
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self.height = height or monitor["height"]
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self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height}
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def __iter__(self):
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return self
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def __next__(self):
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# mss screen capture: get raw pixels from the screen as np array
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im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR
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s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: "
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if self.transforms:
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im = self.transforms(im0) # transforms
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else:
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im = LetterBox(self.img_size, self.auto, stride=self.stride)(image=im0)
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im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
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im = np.ascontiguousarray(im) # contiguous
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self.frame += 1
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return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s
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class LoadImages:
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# YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
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def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
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if isinstance(path, str) and Path(path).suffix == ".txt": # *.txt file with img/vid/dir on each line
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path = Path(path).read_text().rsplit()
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files = []
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for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
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p = str(Path(p).resolve())
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if '*' in p:
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files.extend(sorted(glob.glob(p, recursive=True))) # glob
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elif os.path.isdir(p):
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files.extend(sorted(glob.glob(os.path.join(p, '*.*')))) # dir
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elif os.path.isfile(p):
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files.append(p) # files
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else:
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raise FileNotFoundError(f'{p} does not exist')
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images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
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videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
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ni, nv = len(images), len(videos)
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self.img_size = img_size
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self.stride = stride
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self.files = images + videos
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self.nf = ni + nv # number of files
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self.video_flag = [False] * ni + [True] * nv
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self.mode = 'image'
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self.auto = auto
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self.transforms = transforms # optional
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self.vid_stride = vid_stride # video frame-rate stride
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if any(videos):
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self._new_video(videos[0]) # new video
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else:
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self.cap = None
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assert self.nf > 0, f'No images or videos found in {p}. ' \
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f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}'
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def __iter__(self):
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self.count = 0
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return self
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def __next__(self):
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if self.count == self.nf:
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raise StopIteration
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path = self.files[self.count]
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if self.video_flag[self.count]:
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# Read video
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self.mode = 'video'
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for _ in range(self.vid_stride):
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self.cap.grab()
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ret_val, im0 = self.cap.retrieve()
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while not ret_val:
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self.count += 1
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self.cap.release()
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if self.count == self.nf: # last video
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raise StopIteration
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path = self.files[self.count]
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self._new_video(path)
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ret_val, im0 = self.cap.read()
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self.frame += 1
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# im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False
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s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: '
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else:
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# Read image
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self.count += 1
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im0 = cv2.imread(path) # BGR
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assert im0 is not None, f'Image Not Found {path}'
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s = f'image {self.count}/{self.nf} {path}: '
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if self.transforms:
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im = self.transforms(im0) # transforms
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else:
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im = LetterBox(self.img_size, self.auto, stride=self.stride)(image=im0)
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im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
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im = np.ascontiguousarray(im) # contiguous
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return path, im, im0, self.cap, s
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def _new_video(self, path):
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# Create a new video capture object
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self.frame = 0
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self.cap = cv2.VideoCapture(path)
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self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
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self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees
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# self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493
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def _cv2_rotate(self, im):
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# Rotate a cv2 video manually
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if self.orientation == 0:
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return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)
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elif self.orientation == 180:
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return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE)
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elif self.orientation == 90:
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return cv2.rotate(im, cv2.ROTATE_180)
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return im
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def __len__(self):
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return self.nf # number of files
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