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Update model_handler.py #4665

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merged 5 commits into from
Jan 11, 2023
Merged

Update model_handler.py #4665

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fb48e9d
Update model_handler.py
waqarsqureshi May 31, 2022
5cf3033
Resolve conflicts
yasakova-anastasia Jan 11, 2023
08c5d04
Remove unnecessary comments
yasakova-anastasia Jan 11, 2023
6d6944f
Update Changelog
yasakova-anastasia Jan 11, 2023
eeeaa2d
Merge branch 'develop' into patch-1
nmanovic Jan 11, 2023
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
The corresponding arguments are keyword-only now.
(<https://github.com/opencv/cvat/pull/5502>)
- Windows Installation Instructions adjusted to work around <https://github.com/nuclio/nuclio/issues/1821>
- The contour detection function for semantic segmentation (<https://github.com/opencv/cvat/pull/4665>)

### Deprecated
- TDB
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22 changes: 10 additions & 12 deletions serverless/openvino/omz/intel/semantic-segmentation-adas-0001/nuclio/model_handler.py
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Expand Up @@ -4,11 +4,11 @@
# SPDX-License-Identifier: MIT

import os

import cv2
import numpy as np
from model_loader import ModelLoader
from shared import to_cvat_mask
from skimage.measure import approximate_polygon, find_contours


class ModelHandler:
Expand All @@ -29,26 +29,24 @@ def infer(self, image, threshold):

for i in range(len(self.labels)):
mask_by_label = np.zeros((width, height), dtype=np.uint8)

mask_by_label = ((mask == float(i))).astype(np.uint8)
mask_by_label = ((mask == float(i)) * 255).astype(np.uint8)
mask_by_label = cv2.resize(mask_by_label,
dsize=(image.width, image.height),
interpolation=cv2.INTER_CUBIC)
cv2.normalize(mask_by_label, mask_by_label, 0, 255, cv2.NORM_MINMAX)
interpolation=cv2.INTER_NEAREST)

contours = find_contours(mask_by_label, 0.8)
contours, _ = cv2.findContours(mask_by_label, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)

for contour in contours:
contour = np.flip(contour, axis=1)
contour = approximate_polygon(contour, tolerance=2.5)

x_min = max(0, int(np.min(contour[:,0])))
x_max = max(0, int(np.max(contour[:,0])))
y_min = max(0, int(np.min(contour[:,1])))
y_max = max(0, int(np.max(contour[:,1])))
if len(contour) < 3:
continue

x_min = max(0, int(np.min(contour[:,:,0])))
x_max = max(0, int(np.max(contour[:,:,0])))
y_min = max(0, int(np.min(contour[:,:,1])))
y_max = max(0, int(np.max(contour[:,:,1])))

cvat_mask = to_cvat_mask((x_min, y_min, x_max, y_max), mask_by_label)

results.append({
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