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Optimize in time the image feature extractions algorithms #23

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Optimize in time the image feature extractions algorithms #23

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2ae613f
Update image resize interpolation from INTER_AREA to INTER_NEAREST as…
hakim-cherif-mchp Sep 6, 2024
9fc4ef4
Using Numpy arrays instead of lists as this is significantly quicker …
hakim-cherif-mchp Sep 6, 2024
e676286
Correct an error from if self.isGrayscale: to if is_grayscale:
hakim-cherif-mchp Oct 25, 2024
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47 changes: 24 additions & 23 deletions edge_impulse_linux/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,7 +92,7 @@ def get_features_from_image(self, img, crop_direction_x='center', crop_direction
# One dim will match the classifier size, the other will be larger
resize_size = (resize_size_w, resize_size_h)

resized = cv2.resize(img, resize_size, interpolation=cv2.INTER_AREA)
resized = cv2.resize(img, resize_size, interpolation=cv2.INTER_NEAREST)
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@Alex-EEE Alex-EEE Oct 24, 2024

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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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@hakim-cherif-mchp hakim-cherif-mchp Dec 24, 2024

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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.


if (crop_direction_x == 'center'):
crop_x = int((resize_size_w - EI_CLASSIFIER_INPUT_WIDTH) / 2) # 0 when same
Expand All @@ -117,19 +117,20 @@ def get_features_from_image(self, img, crop_direction_x='center', crop_direction

if self.isGrayscale:
cropped = cv2.cvtColor(cropped, cv2.COLOR_BGR2GRAY)
pixels = np.array(cropped).flatten().tolist()
pixels = np.array(cropped).flatten().astype(np.uint32)

features.extend((pixels << 16) + (pixels << 8) + pixels)

for p in pixels:
features.append((p << 16) + (p << 8) + p)
else:
pixels = np.array(cropped).flatten().tolist()
pixels = np.array(cropped).reshape(-1, 3)

for ix in range(0, len(pixels), 3):
r = pixels[ix + 0]
g = pixels[ix + 1]
b = pixels[ix + 2]
features.append((r << 16) + (g << 8) + b)
r = pixels[:, 0].astype(np.uint32)
g = pixels[:, 1].astype(np.uint32)
b = pixels[:, 2].astype(np.uint32)

features = (r << 16) + (g << 8) + b

features=features.tolist()
return features, cropped

def get_features_from_image_auto_studio_setings(self, img):
Expand Down Expand Up @@ -211,27 +212,27 @@ def get_features_from_image_with_studio_mode(img, mode, output_width, output_hei
offset = (in_frame_rows - new_height) // 2
cropped_img = img[offset:offset + new_height, :]

resized_img = cv2.resize(cropped_img, (output_width, output_height), interpolation=cv2.INTER_AREA)
resized_img = cv2.resize(cropped_img, (output_width, output_height), interpolation=cv2.INTER_NEAREST)
elif mode == 'fit-longest':
resized_img = resize_with_letterbox(img, output_width, output_height)
elif mode == 'squash':
resized_img = cv2.resize(img, (output_width, output_height), interpolation=cv2.INTER_AREA)
resized_img = cv2.resize(img, (output_width, output_height), interpolation=cv2.INTER_NEAREST)
else:
raise ValueError(f"Unsupported mode: {mode}")

if is_grayscale:
resized_img = cv2.cvtColor(resized_img, cv2.COLOR_BGR2GRAY)
pixels = np.array(resized_img).flatten().tolist()
cropped = cv2.cvtColor(cropped, cv2.COLOR_BGR2GRAY)
pixels = np.array(cropped).flatten().astype(np.uint32)

features.extend((pixels << 16) + (pixels << 8) + pixels)
else:
pixels = np.array(resized_img).reshape(-1, 3)

for p in pixels:
features.append((p << 16) + (p << 8) + p)
else:
pixels = np.array(resized_img).flatten().tolist()
r = pixels[:, 0].astype(np.uint32)
g = pixels[:, 1].astype(np.uint32)
b = pixels[:, 2].astype(np.uint32)

for ix in range(0, len(pixels), 3):
r = pixels[ix + 0]
g = pixels[ix + 1]
b = pixels[ix + 2]
features.append((r << 16) + (g << 8) + b)
features = (r << 16) + (g << 8) + b

features=features.tolist()
return features, resized_img