Optimize in time the image feature extractions algorithms #23
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… INTER_NEAREST is much quicker on hardware not equipped with image processing IPs.
…on some hardware platforms.
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@Alex-EEE can you take a look? |
| resize_size = (resize_size_w, resize_size_h) | ||
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| resized = cv2.resize(img, resize_size, interpolation=cv2.INTER_AREA) | ||
| resized = cv2.resize(img, resize_size, interpolation=cv2.INTER_NEAREST) |
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We feel Inter Area is the best trade off for performance and output, especially since the user should only be downsizing. But I'm open to hear your thoughts
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Hi Alex,
Sorry for the delay, I tested more extensively the two algorithms, and it appears that it is possible to get a very high similarity score between the two sets of extracted features, but only under certain circumstances.
So, in my opinion, you should keep the INTER_AREA.
edge_impulse_linux/image.py
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| if is_grayscale: | ||
| resized_img = cv2.cvtColor(resized_img, cv2.COLOR_BGR2GRAY) | ||
| pixels = np.array(resized_img).flatten().tolist() | ||
| if self.isGrayscale: |
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Although this looks like a member function, it's actually outside of the class and thus there's no self variable
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Hi,
My mistake, I corrected it and pushed the related commit.
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While working with code samples from this repository, I have seen that some parts are not optimized in time, and on some hardware the difference is significant.
On the Microchip SAMA7G54 is was able to divide by 25 the time taken for these operations. (From 500 ms to 20ms).