AutoShape PosixPath support (#4047)
* AutoShape PosixPath support
Usage example:
````python
from pathlib import Path
model = ...
file = Path('data/images/zidane.jpg')
results = model(file)
```
* Update common.py
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@@ -1,7 +1,7 @@
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# YOLOv5 common modules
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# YOLOv5 common modules
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from copy import copy
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from copy import copy
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from pathlib import Path
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from pathlib import Path, PosixPath
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import math
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import math
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import numpy as np
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import numpy as np
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@@ -232,8 +232,8 @@ class AutoShape(nn.Module):
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@torch.no_grad()
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@torch.no_grad()
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def forward(self, imgs, size=640, augment=False, profile=False):
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def forward(self, imgs, size=640, augment=False, profile=False):
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# Inference from various sources. For height=640, width=1280, RGB images example inputs are:
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# Inference from various sources. For height=640, width=1280, RGB images example inputs are:
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# filename: imgs = 'data/images/zidane.jpg'
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# filename: imgs = 'data/images/zidane.jpg' # str or PosixPath
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# URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
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# URI: = 'https://ultralytics.com/images/zidane.jpg'
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# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
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# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
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# PIL: = Image.open('image.jpg') # HWC x(640,1280,3)
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# PIL: = Image.open('image.jpg') # HWC x(640,1280,3)
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# numpy: = np.zeros((640,1280,3)) # HWC
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# numpy: = np.zeros((640,1280,3)) # HWC
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@@ -251,8 +251,8 @@ class AutoShape(nn.Module):
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shape0, shape1, files = [], [], [] # image and inference shapes, filenames
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shape0, shape1, files = [], [], [] # image and inference shapes, filenames
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for i, im in enumerate(imgs):
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for i, im in enumerate(imgs):
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f = f'image{i}' # filename
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f = f'image{i}' # filename
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if isinstance(im, str): # filename or uri
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if isinstance(im, (str, PosixPath)): # filename or uri
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im, f = Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im), im
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im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
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im = np.asarray(exif_transpose(im))
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im = np.asarray(exif_transpose(im))
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elif isinstance(im, Image.Image): # PIL Image
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elif isinstance(im, Image.Image): # PIL Image
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im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
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im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
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