ultralytics 8.0.26 new YOLOv5u models (#771)

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>

ultralytics/yolo/data/dataloaders/stream_loaders.py

@@ -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
 

ultralytics/yolo/data/scripts/download_weights.sh

@@ -1,22 +1,18 @@
 #!/bin/bash
 # Ultralytics YOLO 🚀, GPL-3.0 license
-# Download latest models from https://github.com/ultralytics/yolov5/releases
-# Example usage: bash data/scripts/download_weights.sh
+# Download latest models from https://github.com/ultralytics/assets/releases
+# Example usage: bash ultralytics/yolo/data/scripts/download_weights.sh
 # parent
-# └── yolov5
-#     ├── yolov5s.pt  ← downloads here
-#     ├── yolov5m.pt
+# └── weights
+#     ├── yolov8n.pt  ← downloads here
+#     ├── yolov8s.pt
 #     └── ...
 
 python - <<EOF
-from utils.downloads import attempt_download
+from ultralytics.yolo.utils.downloads import attempt_download_asset
 
-p5 = list('nsmlx')  # P5 models
-p6 = [f'{x}6' for x in p5]  # P6 models
-cls = [f'{x}-cls' for x in p5]  # classification models
-seg = [f'{x}-seg' for x in p5]  # classification models
-
-for x in p5 + p6 + cls + seg:
-    attempt_download(f'weights/yolov5{x}.pt')
+assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '-cls', '-seg')]
+for x in assets:
+    attempt_download_asset(f'weights/{x}')
 
 EOF