529 lines
22 KiB
Python
529 lines
22 KiB
Python
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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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 dataclasses import dataclass
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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 requests
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import torch
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from PIL import Image
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from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
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from ultralytics.utils import LOGGER, is_colab, is_kaggle, ops
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from ultralytics.utils.checks import check_requirements
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@dataclass
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class SourceTypes:
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"""Class to represent various types of input sources for predictions."""
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webcam: bool = False
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screenshot: bool = False
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from_img: bool = False
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tensor: bool = False
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class LoadStreams:
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"""
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Stream Loader for various types of video streams.
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Suitable for use with `yolo predict source='rtsp://example.com/media.mp4'`, supports RTSP, RTMP, HTTP, and TCP streams.
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Attributes:
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sources (str): The source input paths or URLs for the video streams.
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imgsz (int): The image size for processing, defaults to 640.
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vid_stride (int): Video frame-rate stride, defaults to 1.
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buffer (bool): Whether to buffer input streams, defaults to False.
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running (bool): Flag to indicate if the streaming thread is running.
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mode (str): Set to 'stream' indicating real-time capture.
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imgs (list): List of image frames for each stream.
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fps (list): List of FPS for each stream.
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frames (list): List of total frames for each stream.
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threads (list): List of threads for each stream.
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shape (list): List of shapes for each stream.
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caps (list): List of cv2.VideoCapture objects for each stream.
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bs (int): Batch size for processing.
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Methods:
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__init__: Initialize the stream loader.
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update: Read stream frames in daemon thread.
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close: Close stream loader and release resources.
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__iter__: Returns an iterator object for the class.
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__next__: Returns source paths, transformed, and original images for processing.
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__len__: Return the length of the sources object.
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"""
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def __init__(self, sources='file.streams', imgsz=640, vid_stride=1, buffer=False):
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"""Initialize instance variables and check for consistent input stream shapes."""
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torch.backends.cudnn.benchmark = True # faster for fixed-size inference
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self.buffer = buffer # buffer input streams
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self.running = True # running flag for Thread
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self.mode = 'stream'
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self.imgsz = imgsz
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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.fps = [0] * n # frames per second
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self.frames = [0] * n
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self.threads = [None] * n
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self.caps = [None] * n # video capture objects
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self.imgs = [[] for _ in range(n)] # images
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self.shape = [[] for _ in range(n)] # image shapes
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self.sources = [ops.clean_str(x) for x in sources] # clean source names for later
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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/LNwODJXcvt4'
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s = get_best_youtube_url(s)
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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 and (is_colab() or is_kaggle()):
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raise NotImplementedError("'source=0' webcam not supported in Colab and Kaggle notebooks. "
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"Try running 'source=0' in a local environment.")
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self.caps[i] = cv2.VideoCapture(s) # store video capture object
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if not self.caps[i].isOpened():
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raise ConnectionError(f'{st}Failed to open {s}')
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w = int(self.caps[i].get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(self.caps[i].get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps = self.caps[i].get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan
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self.frames[i] = max(int(self.caps[i].get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float(
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'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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success, im = self.caps[i].read() # guarantee first frame
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if not success or im is None:
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raise ConnectionError(f'{st}Failed to read images from {s}')
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self.imgs[i].append(im)
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self.shape[i] = im.shape
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self.threads[i] = Thread(target=self.update, args=([i, self.caps[i], s]), daemon=True)
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LOGGER.info(f'{st}Success ✅ ({self.frames[i]} frames of shape {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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self.bs = self.__len__()
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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 self.running and cap.isOpened() and n < (f - 1):
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if len(self.imgs[i]) < 30: # keep a <=30-image buffer
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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 not success:
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im = np.zeros(self.shape[i], dtype=np.uint8)
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LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.')
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cap.open(stream) # re-open stream if signal was lost
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if self.buffer:
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self.imgs[i].append(im)
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else:
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self.imgs[i] = [im]
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else:
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time.sleep(0.01) # wait until the buffer is empty
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def close(self):
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"""Close stream loader and release resources."""
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self.running = False # stop flag for Thread
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for thread in self.threads:
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if thread.is_alive():
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thread.join(timeout=5) # Add timeout
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for cap in self.caps: # Iterate through the stored VideoCapture objects
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try:
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cap.release() # release video capture
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except Exception as e:
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LOGGER.warning(f'WARNING ⚠️ Could not release VideoCapture object: {e}')
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cv2.destroyAllWindows()
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def __iter__(self):
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"""Iterates through YOLO image feed and re-opens unresponsive streams."""
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self.count = -1
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return self
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def __next__(self):
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"""Returns source paths, transformed and original images for processing."""
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self.count += 1
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images = []
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for i, x in enumerate(self.imgs):
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# Wait until a frame is available in each buffer
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while not x:
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if not self.threads[i].is_alive() or cv2.waitKey(1) == ord('q'): # q to quit
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self.close()
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raise StopIteration
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time.sleep(1 / min(self.fps))
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x = self.imgs[i]
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if not x:
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LOGGER.warning(f'WARNING ⚠️ Waiting for stream {i}')
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# Get and remove the first frame from imgs buffer
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if self.buffer:
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images.append(x.pop(0))
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# Get the last frame, and clear the rest from the imgs buffer
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else:
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images.append(x.pop(-1) if x else np.zeros(self.shape[i], dtype=np.uint8))
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x.clear()
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return self.sources, images, None, ''
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def __len__(self):
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"""Return the length of the sources object."""
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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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"""
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YOLOv8 screenshot dataloader.
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This class manages the loading of screenshot images for processing with YOLOv8.
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Suitable for use with `yolo predict source=screen`.
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Attributes:
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source (str): The source input indicating which screen to capture.
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imgsz (int): The image size for processing, defaults to 640.
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screen (int): The screen number to capture.
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left (int): The left coordinate for screen capture area.
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top (int): The top coordinate for screen capture area.
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width (int): The width of the screen capture area.
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height (int): The height of the screen capture area.
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mode (str): Set to 'stream' indicating real-time capture.
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frame (int): Counter for captured frames.
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sct (mss.mss): Screen capture object from `mss` library.
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bs (int): Batch size, set to 1.
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monitor (dict): Monitor configuration details.
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Methods:
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__iter__: Returns an iterator object.
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__next__: Captures the next screenshot and returns it.
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"""
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def __init__(self, source, imgsz=640):
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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 # noqa
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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.imgsz = imgsz
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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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self.bs = 1
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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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"""Returns an iterator of the object."""
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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.asarray(self.sct.grab(self.monitor))[:, :, :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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self.frame += 1
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return [str(self.screen)], [im0], None, s # screen, img, vid_cap, string
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class LoadImages:
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"""
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YOLOv8 image/video dataloader.
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This class manages the loading and pre-processing of image and video data for YOLOv8. It supports loading from
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various formats, including single image files, video files, and lists of image and video paths.
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Attributes:
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imgsz (int): Image size, defaults to 640.
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files (list): List of image and video file paths.
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nf (int): Total number of files (images and videos).
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video_flag (list): Flags indicating whether a file is a video (True) or an image (False).
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mode (str): Current mode, 'image' or 'video'.
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vid_stride (int): Stride for video frame-rate, defaults to 1.
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bs (int): Batch size, set to 1 for this class.
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cap (cv2.VideoCapture): Video capture object for OpenCV.
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frame (int): Frame counter for video.
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frames (int): Total number of frames in the video.
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count (int): Counter for iteration, initialized at 0 during `__iter__()`.
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Methods:
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_new_video(path): Create a new cv2.VideoCapture object for a given video path.
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"""
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def __init__(self, path, imgsz=640, vid_stride=1):
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"""Initialize the Dataloader and raise FileNotFoundError if file not found."""
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parent = None
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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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parent = Path(path).parent
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path = Path(path).read_text().splitlines() # list of sources
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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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a = str(Path(p).absolute()) # do not use .resolve() https://github.com/ultralytics/ultralytics/issues/2912
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if '*' in a:
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files.extend(sorted(glob.glob(a, recursive=True))) # glob
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elif os.path.isdir(a):
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files.extend(sorted(glob.glob(os.path.join(a, '*.*')))) # dir
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elif os.path.isfile(a):
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files.append(a) # files (absolute or relative to CWD)
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elif parent and (parent / p).is_file():
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files.append(str((parent / p).absolute())) # files (relative to *.txt file parent)
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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.imgsz = imgsz
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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.vid_stride = vid_stride # video frame-rate stride
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self.bs = 1
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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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if self.nf == 0:
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raise FileNotFoundError(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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"""Returns an iterator object for VideoStream or ImageFolder."""
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self.count = 0
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return self
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def __next__(self):
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"""Return next image, path and metadata from dataset."""
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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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success, im0 = self.cap.retrieve()
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while not success:
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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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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}: '
|
||
|
|
|
||
|
|
return [path], [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)
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
"""Returns the number of files in the object."""
|
||
|
|
return self.nf # number of files
|
||
|
|
|
||
|
|
|
||
|
|
class LoadPilAndNumpy:
|
||
|
|
"""
|
||
|
|
Load images from PIL and Numpy arrays for batch processing.
|
||
|
|
|
||
|
|
This class is designed to manage loading and pre-processing of image data from both PIL and Numpy formats.
|
||
|
|
It performs basic validation and format conversion to ensure that the images are in the required format for
|
||
|
|
downstream processing.
|
||
|
|
|
||
|
|
Attributes:
|
||
|
|
paths (list): List of image paths or autogenerated filenames.
|
||
|
|
im0 (list): List of images stored as Numpy arrays.
|
||
|
|
imgsz (int): Image size, defaults to 640.
|
||
|
|
mode (str): Type of data being processed, defaults to 'image'.
|
||
|
|
bs (int): Batch size, equivalent to the length of `im0`.
|
||
|
|
count (int): Counter for iteration, initialized at 0 during `__iter__()`.
|
||
|
|
|
||
|
|
Methods:
|
||
|
|
_single_check(im): Validate and format a single image to a Numpy array.
|
||
|
|
"""
|
||
|
|
|
||
|
|
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.mode = 'image'
|
||
|
|
# Generate fake paths
|
||
|
|
self.bs = len(self.im0)
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _single_check(im):
|
||
|
|
"""Validate and format an image to numpy array."""
|
||
|
|
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 __len__(self):
|
||
|
|
"""Returns the length of the 'im0' attribute."""
|
||
|
|
return len(self.im0)
|
||
|
|
|
||
|
|
def __next__(self):
|
||
|
|
"""Returns batch paths, images, processed images, None, ''."""
|
||
|
|
if self.count == 1: # loop only once as it's batch inference
|
||
|
|
raise StopIteration
|
||
|
|
self.count += 1
|
||
|
|
return self.paths, self.im0, None, ''
|
||
|
|
|
||
|
|
def __iter__(self):
|
||
|
|
"""Enables iteration for class LoadPilAndNumpy."""
|
||
|
|
self.count = 0
|
||
|
|
return self
|
||
|
|
|
||
|
|
|
||
|
|
class LoadTensor:
|
||
|
|
"""
|
||
|
|
Load images from torch.Tensor data.
|
||
|
|
|
||
|
|
This class manages the loading and pre-processing of image data from PyTorch tensors for further processing.
|
||
|
|
|
||
|
|
Attributes:
|
||
|
|
im0 (torch.Tensor): The input tensor containing the image(s).
|
||
|
|
bs (int): Batch size, inferred from the shape of `im0`.
|
||
|
|
mode (str): Current mode, set to 'image'.
|
||
|
|
paths (list): List of image paths or filenames.
|
||
|
|
count (int): Counter for iteration, initialized at 0 during `__iter__()`.
|
||
|
|
|
||
|
|
Methods:
|
||
|
|
_single_check(im, stride): Validate and possibly modify the input tensor.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, im0) -> None:
|
||
|
|
"""Initialize Tensor Dataloader."""
|
||
|
|
self.im0 = self._single_check(im0)
|
||
|
|
self.bs = self.im0.shape[0]
|
||
|
|
self.mode = 'image'
|
||
|
|
self.paths = [getattr(im, 'filename', f'image{i}.jpg') for i, im in enumerate(im0)]
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def _single_check(im, stride=32):
|
||
|
|
"""Validate and format an image to torch.Tensor."""
|
||
|
|
s = f'WARNING ⚠️ torch.Tensor inputs should be BCHW i.e. shape(1, 3, 640, 640) ' \
|
||
|
|
f'divisible by stride {stride}. Input shape{tuple(im.shape)} is incompatible.'
|
||
|
|
if len(im.shape) != 4:
|
||
|
|
if len(im.shape) != 3:
|
||
|
|
raise ValueError(s)
|
||
|
|
LOGGER.warning(s)
|
||
|
|
im = im.unsqueeze(0)
|
||
|
|
if im.shape[2] % stride or im.shape[3] % stride:
|
||
|
|
raise ValueError(s)
|
||
|
|
if im.max() > 1.0 + torch.finfo(im.dtype).eps: # torch.float32 eps is 1.2e-07
|
||
|
|
LOGGER.warning(f'WARNING ⚠️ torch.Tensor inputs should be normalized 0.0-1.0 but max value is {im.max()}. '
|
||
|
|
f'Dividing input by 255.')
|
||
|
|
im = im.float() / 255.0
|
||
|
|
|
||
|
|
return im
|
||
|
|
|
||
|
|
def __iter__(self):
|
||
|
|
"""Returns an iterator object."""
|
||
|
|
self.count = 0
|
||
|
|
return self
|
||
|
|
|
||
|
|
def __next__(self):
|
||
|
|
"""Return next item in the iterator."""
|
||
|
|
if self.count == 1:
|
||
|
|
raise StopIteration
|
||
|
|
self.count += 1
|
||
|
|
return self.paths, self.im0, None, ''
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
"""Returns the batch size."""
|
||
|
|
return self.bs
|
||
|
|
|
||
|
|
|
||
|
|
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/modes/predict for supported source types.')
|
||
|
|
|
||
|
|
return files
|
||
|
|
|
||
|
|
|
||
|
|
LOADERS = LoadStreams, LoadPilAndNumpy, LoadImages, LoadScreenshots # tuple
|
||
|
|
|
||
|
|
|
||
|
|
def get_best_youtube_url(url, use_pafy=True):
|
||
|
|
"""
|
||
|
|
Retrieves the URL of the best quality MP4 video stream from a given YouTube video.
|
||
|
|
|
||
|
|
This function uses the pafy or yt_dlp library to extract the video info from YouTube. It then finds the highest
|
||
|
|
quality MP4 format that has video codec but no audio codec, and returns the URL of this video stream.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
url (str): The URL of the YouTube video.
|
||
|
|
use_pafy (bool): Use the pafy package, default=True, otherwise use yt_dlp package.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
(str): The URL of the best quality MP4 video stream, or None if no suitable stream is found.
|
||
|
|
"""
|
||
|
|
if use_pafy:
|
||
|
|
check_requirements(('pafy', 'youtube_dl==2020.12.2'))
|
||
|
|
import pafy # noqa
|
||
|
|
return pafy.new(url).getbestvideo(preftype='mp4').url
|
||
|
|
else:
|
||
|
|
check_requirements('yt-dlp')
|
||
|
|
import yt_dlp
|
||
|
|
with yt_dlp.YoutubeDL({'quiet': True}) as ydl:
|
||
|
|
info_dict = ydl.extract_info(url, download=False) # extract info
|
||
|
|
for f in reversed(info_dict.get('formats', [])): # reversed because best is usually last
|
||
|
|
# Find a format with video codec, no audio, *.mp4 extension at least 1920x1080 size
|
||
|
|
good_size = (f.get('width') or 0) >= 1920 or (f.get('height') or 0) >= 1080
|
||
|
|
if good_size and f['vcodec'] != 'none' and f['acodec'] == 'none' and f['ext'] == 'mp4':
|
||
|
|
return f.get('url')
|