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ultralytics 8.0.26 new YOLOv5u models (#771)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Adrian Boguszewski <adrian.boguszewski@intel.com>
This commit is contained in:
Glenn Jocher
2023-02-02 21:11:23 +04:00
committed by GitHub
parent b83374b42d
commit fa8811dcee
23 changed files with 85 additions and 52 deletions
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22
ultralytics/yolo/data/dataloaders/stream_loaders.py
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@ -13,7 +13,7 @@ import cv2
import numpy as np
import requests
import torch
from PIL import Image, ImageOps
from PIL import Image
from ultralytics.yolo.data.augment import LetterBox
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
@ -50,7 +50,7 @@ class LoadStreams:
s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL
s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam
if s == 0 and (is_colab() or is_kaggle()):
raise NotImplementedError("'source=0' webcam not supported in Colab and Kaggle notebooks."
raise NotImplementedError("'source=0' webcam not supported in Colab and Kaggle notebooks. "
"Try running 'source=0' in a local environment.")
cap = cv2.VideoCapture(s)
if not cap.isOpened():
@ -61,9 +61,11 @@ class LoadStreams:
self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback
self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback
_, self.imgs[i] = cap.read() # guarantee first frame
success, self.imgs[i] = cap.read() # guarantee first frame
if not success or self.imgs[i] is None:
raise ConnectionError(f'{st}Failed to read images from {s}')
self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
LOGGER.info(f"{st}Success ✅ ({self.frames[i]} frames of shape {w}x{h} at {self.fps[i]:.2f} FPS)")
self.threads[i].start()
LOGGER.info('') # newline
@ -221,15 +223,15 @@ class LoadImages:
self.mode = 'video'
for _ in range(self.vid_stride):
self.cap.grab()
ret_val, im0 = self.cap.retrieve()
while not ret_val:
success, im0 = self.cap.retrieve()
while not success:
self.count += 1
self.cap.release()
if self.count == self.nf: # last video
raise StopIteration
path = self.files[self.count]
self._new_video(path)
ret_val, im0 = self.cap.read()
success, im0 = self.cap.read()
self.frame += 1
# im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False
@ -330,14 +332,14 @@ 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 enumerate(source):
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 Exception(
"Unsupported type encountered! See docs for supported types https://docs.ultralytics.com/predict")
raise TypeError(f"type {type(im).__name__} is not a supported Ultralytics prediction source type. \n"
f"See https://docs.ultralytics.com/predict for supported source types.")
return files