Predictor support (#65)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>single_channel
Ayush Chaurasia
2 years ago
committed by
GitHub
parent
479992093c
commit
e6737f1207
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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 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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# predictor engine by Ultralytics
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"""
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Run prection on images, videos, directories, globs, YouTube, webcam, streams, etc.
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Usage - sources:
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$ yolo task=... mode=predict model=s.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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screen # screenshot
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path/ # directory
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list.txt # list of images
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list.streams # list of streams
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'path/*.jpg' # glob
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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Usage - formats:
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$ yolo task=... mode=predict --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s_openvino_model # OpenVINO
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yolov5s.engine # TensorRT
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yolov5s.mlmodel # CoreML (macOS-only)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow GraphDef
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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yolov5s_paddle_model # PaddlePaddle
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"""
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import platform
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from pathlib import Path
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import cv2
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import torch
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from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadScreenshots, LoadStreams
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from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS, check_dataset, check_dataset_yaml
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from ultralytics.yolo.utils import LOGGER, ROOT, TQDM_BAR_FORMAT, colorstr, ops
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from ultralytics.yolo.utils.checks import check_file, check_imshow
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from ultralytics.yolo.utils.configs import get_config
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from ultralytics.yolo.utils.files import increment_path
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from ultralytics.yolo.utils.modeling.autobackend import AutoBackend
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from ultralytics.yolo.utils.plotting import Annotator
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from ultralytics.yolo.utils.torch_utils import check_img_size, select_device, smart_inference_mode
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DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
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class BasePredictor:
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def __init__(self, config=DEFAULT_CONFIG, overrides={}):
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self.args = get_config(config, overrides)
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self.save_dir = increment_path(Path(self.args.project) / self.args.name, exist_ok=self.args.exist_ok)
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(self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
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self.done_setup = False
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# Usable if setup is done
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self.model = None
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self.data = self.args.data # data_dict
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self.device = None
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self.dataset = None
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self.vid_path, self.vid_writer = None, None
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self.view_img = None
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self.annotator = None
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self.data_path = None
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def preprocess(self, img):
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pass
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|
def get_annotator(self, img):
|
||||||
|
raise NotImplementedError("get_annotator function needs to be implemented")
|
||||||
|
|
||||||
|
def write_results(self, pred, batch, print_string):
|
||||||
|
raise NotImplementedError("print_results function needs to be implemented")
|
||||||
|
|
||||||
|
def postprocess(self, preds, img, orig_img):
|
||||||
|
return preds
|
||||||
|
|
||||||
|
def setup(self, source=None, model=None):
|
||||||
|
# source
|
||||||
|
source = str(source or self.args.source)
|
||||||
|
self.save_img = not self.args.nosave and not source.endswith('.txt')
|
||||||
|
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
||||||
|
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
|
||||||
|
webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
|
||||||
|
screenshot = source.lower().startswith('screen')
|
||||||
|
if is_url and is_file:
|
||||||
|
source = check_file(source) # download
|
||||||
|
|
||||||
|
# data
|
||||||
|
if self.data:
|
||||||
|
if self.data.endswith(".yaml"):
|
||||||
|
self.data = check_dataset_yaml(self.data)
|
||||||
|
else:
|
||||||
|
self.data = check_dataset(self.data)
|
||||||
|
|
||||||
|
# model
|
||||||
|
device = select_device(self.args.device)
|
||||||
|
model = model or self.args.model
|
||||||
|
self.args.half &= device.type != 'cpu' # half precision only supported on CUDA
|
||||||
|
model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half) # NOTE: not passing data
|
||||||
|
stride, pt = model.stride, model.pt
|
||||||
|
imgsz = check_img_size(self.args.img_size, s=stride) # check image size
|
||||||
|
|
||||||
|
# Dataloader
|
||||||
|
bs = 1 # batch_size
|
||||||
|
if webcam:
|
||||||
|
self.view_img = check_imshow(warn=True)
|
||||||
|
self.dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
|
||||||
|
bs = len(self.dataset)
|
||||||
|
elif screenshot:
|
||||||
|
self.dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
|
||||||
|
else:
|
||||||
|
self.dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
|
||||||
|
self.vid_path, self.vid_writer = [None] * bs, [None] * bs
|
||||||
|
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
|
||||||
|
|
||||||
|
self.model = model
|
||||||
|
self.webcam = webcam
|
||||||
|
self.screenshot = screenshot
|
||||||
|
self.imgsz = imgsz
|
||||||
|
self.done_setup = True
|
||||||
|
self.device = device
|
||||||
|
|
||||||
|
return model
|
||||||
|
|
||||||
|
@smart_inference_mode()
|
||||||
|
def __call__(self, source=None, model=None):
|
||||||
|
if not self.done_setup:
|
||||||
|
model = self.setup(source, model)
|
||||||
|
else:
|
||||||
|
model = self.model
|
||||||
|
|
||||||
|
self.seen, self.windows, self.dt = 0, [], (ops.Profile(), ops.Profile(), ops.Profile())
|
||||||
|
for batch in self.dataset:
|
||||||
|
path, im, im0s, vid_cap, s = batch
|
||||||
|
visualize = increment_path(self.save_dir / Path(path).stem, mkdir=True) if self.args.visualize else False
|
||||||
|
with self.dt[0]:
|
||||||
|
im = self.preprocess(im)
|
||||||
|
if len(im.shape) == 3:
|
||||||
|
im = im[None] # expand for batch dim
|
||||||
|
|
||||||
|
# Inference
|
||||||
|
with self.dt[1]:
|
||||||
|
preds = model(im, augment=self.args.augment, visualize=visualize)
|
||||||
|
|
||||||
|
# postprocess
|
||||||
|
with self.dt[2]:
|
||||||
|
preds = self.postprocess(preds, im, im0s)
|
||||||
|
|
||||||
|
for i in range(len(im)):
|
||||||
|
if self.webcam:
|
||||||
|
path, im0s = path[i], im0s[i]
|
||||||
|
p = Path(path)
|
||||||
|
s += self.write_results(i, preds, (p, im, im0s))
|
||||||
|
|
||||||
|
if self.args.view_img:
|
||||||
|
self.show(p)
|
||||||
|
|
||||||
|
if self.save_img:
|
||||||
|
self.save_preds(vid_cap, i, str(self.save_dir / p.name))
|
||||||
|
|
||||||
|
# Print time (inference-only)
|
||||||
|
LOGGER.info(f"{s}{'' if len(preds) else '(no detections), '}{self.dt[1].dt * 1E3:.1f}ms")
|
||||||
|
|
||||||
|
# Print results
|
||||||
|
t = tuple(x.t / self.seen * 1E3 for x in self.dt) # speeds per image
|
||||||
|
LOGGER.info(
|
||||||
|
f'Speed: %.1fms pre-process, %.1fms inference, %.1fms postprocess per image at shape {(1, 3, *self.imgsz)}'
|
||||||
|
% t)
|
||||||
|
if self.args.save_txt or self.save_img:
|
||||||
|
s = f"\n{len(list(self.save_dir.glob('labels/*.txt')))} labels saved to {self.save_dir / 'labels'}" if self.args.save_txt else ''
|
||||||
|
LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
|
||||||
|
|
||||||
|
def show(self, p):
|
||||||
|
im0 = self.annotator.result()
|
||||||
|
if platform.system() == 'Linux' and p not in self.windows:
|
||||||
|
self.windows.append(p)
|
||||||
|
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
|
||||||
|
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
|
||||||
|
cv2.imshow(str(p), im0)
|
||||||
|
cv2.waitKey(1) # 1 millisecond
|
||||||
|
|
||||||
|
def save_preds(self, vid_cap, idx, save_path):
|
||||||
|
im0 = self.annotator.result()
|
||||||
|
# save imgs
|
||||||
|
if self.dataset.mode == 'image':
|
||||||
|
cv2.imwrite(save_path, im0)
|
||||||
|
else: # 'video' or 'stream'
|
||||||
|
if self.vid_path[idx] != save_path: # new video
|
||||||
|
self.vid_path[idx] = save_path
|
||||||
|
if isinstance(self.vid_writer[idx], cv2.VideoWriter):
|
||||||
|
self.vid_writer[idx].release() # release previous video writer
|
||||||
|
if vid_cap: # video
|
||||||
|
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
||||||
|
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||||
|
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||||
|
else: # stream
|
||||||
|
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||||
|
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
|
||||||
|
self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
||||||
|
self.vid_writer[idx].write(im0)
|
||||||
@ -0,0 +1,23 @@
|
|||||||
|
from pathlib import Path
|
||||||
|
from typing import Dict, Union
|
||||||
|
|
||||||
|
from omegaconf import DictConfig, OmegaConf
|
||||||
|
|
||||||
|
|
||||||
|
def get_config(config: Union[str, DictConfig], overrides: Union[str, Dict] = {}):
|
||||||
|
"""
|
||||||
|
Accepts yaml file name or DictConfig containing experiment configuration.
|
||||||
|
Returns training args namespace
|
||||||
|
:param config: Optional file name or DictConfig object
|
||||||
|
"""
|
||||||
|
if isinstance(config, (str, Path)):
|
||||||
|
config = OmegaConf.load(config)
|
||||||
|
elif isinstance(config, Dict):
|
||||||
|
config = OmegaConf.create(config)
|
||||||
|
# override
|
||||||
|
if isinstance(overrides, str):
|
||||||
|
overrides = OmegaConf.load(overrides)
|
||||||
|
elif isinstance(overrides, Dict):
|
||||||
|
overrides = OmegaConf.create(overrides)
|
||||||
|
|
||||||
|
return OmegaConf.merge(config, overrides)
|
||||||
@ -0,0 +1,68 @@
|
|||||||
|
import hydra
|
||||||
|
import torch
|
||||||
|
|
||||||
|
from ultralytics.yolo.engine.predictor import BasePredictor
|
||||||
|
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
|
||||||
|
from ultralytics.yolo.utils import ops
|
||||||
|
from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
|
||||||
|
|
||||||
|
|
||||||
|
class ClassificationPredictor(BasePredictor):
|
||||||
|
|
||||||
|
def get_annotator(self, img):
|
||||||
|
return Annotator(img, example=str(self.model.names), pil=True)
|
||||||
|
|
||||||
|
def preprocess(self, img):
|
||||||
|
img = torch.Tensor(img).to(self.model.device)
|
||||||
|
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
|
||||||
|
return img
|
||||||
|
|
||||||
|
def write_results(self, idx, preds, batch):
|
||||||
|
p, im, im0 = batch
|
||||||
|
log_string = ""
|
||||||
|
if len(im.shape) == 3:
|
||||||
|
im = im[None] # expand for batch dim
|
||||||
|
self.seen += 1
|
||||||
|
im0 = im0.copy()
|
||||||
|
if self.webcam: # batch_size >= 1
|
||||||
|
log_string += f'{idx}: '
|
||||||
|
frame = self.dataset.cound
|
||||||
|
else:
|
||||||
|
frame = getattr(self.dataset, 'frame', 0)
|
||||||
|
|
||||||
|
self.data_path = p
|
||||||
|
# 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}')
|
||||||
|
log_string += '%gx%g ' % im.shape[2:] # print string
|
||||||
|
self.annotator = self.get_annotator(im0)
|
||||||
|
|
||||||
|
prob = preds[idx]
|
||||||
|
# Print results
|
||||||
|
top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices
|
||||||
|
log_string += f"{', '.join(f'{self.model.names[j]} {prob[j]:.2f}' for j in top5i)}, "
|
||||||
|
|
||||||
|
# write
|
||||||
|
text = '\n'.join(f'{prob[j]:.2f} {self.model.names[j]}' for j in top5i)
|
||||||
|
if self.save_img or self.args.view_img: # Add bbox to image
|
||||||
|
self.annotator.text((32, 32), text, txt_color=(255, 255, 255))
|
||||||
|
if self.args.save_txt: # Write to file
|
||||||
|
with open(f'{self.txt_path}.txt', 'a') as f:
|
||||||
|
f.write(text + '\n')
|
||||||
|
|
||||||
|
return log_string
|
||||||
|
|
||||||
|
|
||||||
|
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
|
||||||
|
def predict(cfg):
|
||||||
|
cfg.model = cfg.model or "squeezenet1_0"
|
||||||
|
sz = cfg.img_size
|
||||||
|
if type(sz) != int: # recieved listConfig
|
||||||
|
cfg.img_size = [sz[0], sz[0]] if len(cfg.img_size) == 1 else [sz[0], sz[1]] # expand
|
||||||
|
else:
|
||||||
|
cfg.img_size = [sz, sz]
|
||||||
|
predictor = ClassificationPredictor(cfg)
|
||||||
|
predictor()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
predict()
|
||||||
@ -1,2 +1,3 @@
|
|||||||
|
from ultralytics.yolo.v8.detect.predict import DetectionPredictor, predict
|
||||||
from ultralytics.yolo.v8.detect.train import DetectionTrainer, train
|
from ultralytics.yolo.v8.detect.train import DetectionTrainer, train
|
||||||
from ultralytics.yolo.v8.detect.val import DetectionValidator, val
|
from ultralytics.yolo.v8.detect.val import DetectionValidator, val
|
||||||
|
|||||||
@ -0,0 +1,97 @@
|
|||||||
|
import hydra
|
||||||
|
import torch
|
||||||
|
|
||||||
|
from ultralytics.yolo.engine.predictor import BasePredictor
|
||||||
|
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
|
||||||
|
from ultralytics.yolo.utils import ops
|
||||||
|
from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
|
||||||
|
|
||||||
|
|
||||||
|
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):
|
||||||
|
img = torch.from_numpy(img).to(self.model.device)
|
||||||
|
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
|
||||||
|
img /= 255 # 0 - 255 to 0.0 - 1.0
|
||||||
|
return img
|
||||||
|
|
||||||
|
def postprocess(self, preds, img, orig_img):
|
||||||
|
preds = ops.non_max_suppression(preds,
|
||||||
|
self.args.conf_thres,
|
||||||
|
self.args.iou_thres,
|
||||||
|
agnostic=self.args.agnostic_nms,
|
||||||
|
max_det=self.args.max_det)
|
||||||
|
|
||||||
|
for i, pred in enumerate(preds):
|
||||||
|
shape = orig_img[i].shape if self.webcam else orig_img.shape
|
||||||
|
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
|
||||||
|
|
||||||
|
return preds
|
||||||
|
|
||||||
|
def write_results(self, idx, preds, batch):
|
||||||
|
p, im, im0 = batch
|
||||||
|
log_string = ""
|
||||||
|
if len(im.shape) == 3:
|
||||||
|
im = im[None] # expand for batch dim
|
||||||
|
self.seen += 1
|
||||||
|
im0 = im0.copy()
|
||||||
|
if self.webcam: # batch_size >= 1
|
||||||
|
log_string += f'{idx}: '
|
||||||
|
frame = self.dataset.count
|
||||||
|
else:
|
||||||
|
frame = getattr(self.dataset, 'frame', 0)
|
||||||
|
|
||||||
|
self.data_path = p
|
||||||
|
# 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}')
|
||||||
|
log_string += '%gx%g ' % im.shape[2:] # print string
|
||||||
|
self.annotator = self.get_annotator(im0)
|
||||||
|
|
||||||
|
det = preds[idx]
|
||||||
|
if len(det) == 0:
|
||||||
|
return log_string
|
||||||
|
for c in det[:, 5].unique():
|
||||||
|
n = (det[:, 5] == c).sum() # detections per class
|
||||||
|
log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "
|
||||||
|
|
||||||
|
# write
|
||||||
|
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
|
||||||
|
for *xyxy, conf, cls in reversed(det):
|
||||||
|
if self.args.save_txt: # Write to file
|
||||||
|
xywh = (ops.xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
|
||||||
|
line = (cls, *xywh, conf) if self.args.save_conf else (cls, *xywh) # label format
|
||||||
|
with open(f'{self.txt_path}.txt', 'a') as f:
|
||||||
|
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||||
|
|
||||||
|
if self.save_img or self.args.save_crop or self.args.view_img: # Add bbox to image
|
||||||
|
c = int(cls) # integer class
|
||||||
|
label = None if self.args.hide_labels else (
|
||||||
|
self.model.names[c] if self.args.hide_conf else f'{self.model.names[c]} {conf:.2f}')
|
||||||
|
self.annotator.box_label(xyxy, label, color=colors(c, True))
|
||||||
|
if self.args.save_crop:
|
||||||
|
imc = im0.copy()
|
||||||
|
save_one_box(xyxy,
|
||||||
|
imc,
|
||||||
|
file=self.save_dir / 'crops' / self.model.model.names[c] / f'{self.data_path.stem}.jpg',
|
||||||
|
BGR=True)
|
||||||
|
|
||||||
|
return log_string
|
||||||
|
|
||||||
|
|
||||||
|
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
|
||||||
|
def predict(cfg):
|
||||||
|
cfg.model = cfg.model or "n.pt"
|
||||||
|
sz = cfg.img_size
|
||||||
|
if type(sz) != int: # recieved listConfig
|
||||||
|
cfg.img_size = [sz[0], sz[0]] if len(cfg.img_size) == 1 else [sz[0], sz[1]] # expand
|
||||||
|
else:
|
||||||
|
cfg.img_size = [sz, sz]
|
||||||
|
predictor = DetectionPredictor(cfg)
|
||||||
|
predictor()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
predict()
|
||||||
@ -1,2 +1,3 @@
|
|||||||
|
from ultralytics.yolo.v8.segment.predict import SegmentationPredictor, predict
|
||||||
from ultralytics.yolo.v8.segment.train import SegmentationTrainer, train
|
from ultralytics.yolo.v8.segment.train import SegmentationTrainer, train
|
||||||
from ultralytics.yolo.v8.segment.val import SegmentationValidator, val
|
from ultralytics.yolo.v8.segment.val import SegmentationValidator, val
|
||||||
|
|||||||
@ -0,0 +1,115 @@
|
|||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
import hydra
|
||||||
|
import torch
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from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
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from ultralytics.yolo.utils import ROOT, ops
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from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
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from ..detect.predict import DetectionPredictor
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class SegmentationPredictor(DetectionPredictor):
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def postprocess(self, preds, img, orig_img):
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masks = []
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if len(preds) == 2: # eval
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p, proto, = preds
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else: # len(3) train
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p, proto, _ = preds
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# TODO: filter by classes
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p = ops.non_max_suppression(p,
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self.args.conf_thres,
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self.args.iou_thres,
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agnostic=self.args.agnostic_nms,
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max_det=self.args.max_det,
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nm=32)
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for i, pred in enumerate(p):
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shape = orig_img[i].shape if self.webcam else orig_img.shape
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if not len(pred):
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|
continue
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if self.args.retina_masks:
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
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masks.append(ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], shape[:2])) # HWC
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else:
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masks.append(ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)) # HWC
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
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|
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|
return (p, masks)
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|
|
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|
def write_results(self, idx, preds, batch):
|
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|
p, im, im0 = batch
|
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|
log_string = ""
|
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|
if len(im.shape) == 3:
|
||||||
|
im = im[None] # expand for batch dim
|
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|
self.seen += 1
|
||||||
|
if self.webcam: # batch_size >= 1
|
||||||
|
log_string += f'{idx}: '
|
||||||
|
frame = self.dataset.count
|
||||||
|
else:
|
||||||
|
frame = getattr(self.dataset, 'frame', 0)
|
||||||
|
|
||||||
|
self.data_path = p
|
||||||
|
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
|
||||||
|
self.annotator = self.get_annotator(im0)
|
||||||
|
|
||||||
|
preds, masks = preds
|
||||||
|
det = preds[idx]
|
||||||
|
if len(det) == 0:
|
||||||
|
return log_string
|
||||||
|
# Segments
|
||||||
|
mask = masks[idx]
|
||||||
|
if self.args.save_txt:
|
||||||
|
segments = [
|
||||||
|
ops.scale_segments(im0.shape if self.arg.retina_masks else im.shape[2:], x, im0.shape, normalize=True)
|
||||||
|
for x in reversed(ops.masks2segments(mask))]
|
||||||
|
|
||||||
|
# Print results
|
||||||
|
for c in det[:, 5].unique():
|
||||||
|
n = (det[:, 5] == c).sum() # detections per class
|
||||||
|
log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||||||
|
|
||||||
|
# Mask plotting
|
||||||
|
self.annotator.masks(
|
||||||
|
mask,
|
||||||
|
colors=[colors(x, True) for x in det[:, 5]],
|
||||||
|
im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(self.device).permute(2, 0, 1).flip(0).contiguous() /
|
||||||
|
255 if self.args.retina_masks else im[idx])
|
||||||
|
|
||||||
|
# Write results
|
||||||
|
for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
|
||||||
|
if self.args.save_txt: # Write to file
|
||||||
|
seg = segments[j].reshape(-1) # (n,2) to (n*2)
|
||||||
|
line = (cls, *seg, conf) if self.args.save_conf else (cls, *seg) # label format
|
||||||
|
with open(f'{self.txt_path}.txt', 'a') as f:
|
||||||
|
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||||
|
|
||||||
|
if self.save_img or self.args.save_crop or self.args.view_img:
|
||||||
|
c = int(cls) # integer class
|
||||||
|
label = None if self.args.hide_labels else (
|
||||||
|
self.model.names[c] if self.args.hide_conf else f'{self.model.names[c]} {conf:.2f}')
|
||||||
|
self.annotator.box_label(xyxy, label, color=colors(c, True))
|
||||||
|
# annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
|
||||||
|
if self.args.save_crop:
|
||||||
|
imc = im0.copy()
|
||||||
|
save_one_box(xyxy, imc, file=self.save_dir / 'crops' / self.model.names[c] / f'{p.stem}.jpg', BGR=True)
|
||||||
|
|
||||||
|
return log_string
|
||||||
|
|
||||||
|
|
||||||
|
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
|
||||||
|
def predict(cfg):
|
||||||
|
cfg.model = cfg.model or "n.pt"
|
||||||
|
sz = cfg.img_size
|
||||||
|
if type(sz) != int: # recieved listConfig
|
||||||
|
cfg.img_size = [sz[0], sz[0]] if len(cfg.img_size) == 1 else [sz[0], sz[1]] # expand
|
||||||
|
else:
|
||||||
|
cfg.img_size = [sz, sz]
|
||||||
|
predictor = SegmentationPredictor(cfg)
|
||||||
|
predictor()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
predict()
|
||||||
Loading…
Reference in new issue