mirror of
https://github.com/283375/arcaea-offline-ocr.git
synced 2025-04-22 06:50:18 +00:00
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6 Commits
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fb4b1fb9b8 | |||
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17f6c2bac7 |
48
.github/workflows/build-and-draft-release.yml
vendored
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48
.github/workflows/build-and-draft-release.yml
vendored
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@ -0,0 +1,48 @@
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name: "Build and draft a release"
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on:
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workflow_dispatch:
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push:
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tags:
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- "v[0-9]+.[0-9]+.[0-9]+"
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permissions:
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contents: write
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discussions: write
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jobs:
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build-and-draft-release:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Set up Python environment
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uses: actions/setup-python@v5
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with:
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python-version: "3.x"
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- name: Build package
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run: |
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pip install build
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python -m build
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- name: Remove `v` in tag name
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uses: mad9000/actions-find-and-replace-string@5
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id: tagNameReplaced
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with:
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source: ${{ github.ref_name }}
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find: "v"
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replace: ""
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- name: Draft a release
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uses: softprops/action-gh-release@v2
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with:
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discussion_category_name: New releases
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draft: true
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generate_release_notes: true
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files: |
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dist/arcaea_offline_ocr-${{ steps.tagNameReplaced.outputs.value }}*.whl
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dist/arcaea-offline-ocr-${{ steps.tagNameReplaced.outputs.value }}.tar.gz
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@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
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[project]
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[project]
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name = "arcaea-offline-ocr"
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name = "arcaea-offline-ocr"
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version = "0.0.97"
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version = "0.0.99"
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authors = [{ name = "283375", email = "log_283375@163.com" }]
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authors = [{ name = "283375", email = "log_283375@163.com" }]
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description = "Extract your Arcaea play result from screenshot."
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description = "Extract your Arcaea play result from screenshot."
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readme = "README.md"
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readme = "README.md"
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@ -16,8 +16,8 @@ classifiers = [
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]
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]
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[project.urls]
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[project.urls]
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"Homepage" = "https://github.com/283375/arcaea-offline-ocr"
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"Homepage" = "https://github.com/ArcaeaOffline/core-ocr"
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"Bug Tracker" = "https://github.com/283375/arcaea-offline-ocr/issues"
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"Bug Tracker" = "https://github.com/ArcaeaOffline/core-ocr/issues"
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[tool.isort]
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[tool.isort]
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profile = "black"
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profile = "black"
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@ -25,3 +25,14 @@ src_paths = ["src/arcaea_offline_ocr"]
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[tool.pyright]
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[tool.pyright]
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ignore = ["**/__debug*.*"]
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ignore = ["**/__debug*.*"]
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[tool.pylint.main]
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# extension-pkg-allow-list = ["cv2"]
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generated-members = ["cv2.*"]
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[tool.pylint.logging]
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disable = [
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"missing-module-docstring",
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"missing-class-docstring",
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"missing-function-docstring"
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]
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@ -1,5 +1,5 @@
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from datetime import datetime
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from datetime import datetime
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from typing import Optional, Union
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from typing import Optional
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import attrs
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import attrs
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@ -67,8 +67,9 @@ class DeviceOcr:
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roi = self.masker.score(self.extractor.score)
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roi = self.masker.score(self.extractor.score)
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contours, _ = cv2.findContours(roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
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contours, _ = cv2.findContours(roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
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for contour in contours:
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for contour in contours:
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x, y, w, h = cv2.boundingRect(contour)
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if (
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if h < roi.shape[0] * 0.6:
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cv2.boundingRect(contour)[3] < roi.shape[0] * 0.6
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): # h < score_component_h * 0.6
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roi = cv2.fillPoly(roi, [contour], [0])
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roi = cv2.fillPoly(roi, [contour], [0])
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return ocr_digits_by_contour_knn(roi, self.knn_model)
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return ocr_digits_by_contour_knn(roi, self.knn_model)
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@ -79,6 +80,7 @@ class DeviceOcr:
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self.masker.rating_class_prs(roi),
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self.masker.rating_class_prs(roi),
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self.masker.rating_class_ftr(roi),
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self.masker.rating_class_ftr(roi),
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self.masker.rating_class_byd(roi),
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self.masker.rating_class_byd(roi),
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self.masker.rating_class_etr(roi),
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]
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]
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return max(enumerate(results), key=lambda i: np.count_nonzero(i[1]))[0]
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return max(enumerate(results), key=lambda i: np.count_nonzero(i[1]))[0]
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@ -6,6 +6,8 @@ from .common import DeviceRoisMasker
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class DeviceRoisMaskerAuto(DeviceRoisMasker):
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class DeviceRoisMaskerAuto(DeviceRoisMasker):
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# pylint: disable=abstract-method
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@staticmethod
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@staticmethod
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def mask_bgr_in_hsv(roi_bgr: Mat, hsv_lower: Mat, hsv_upper: Mat):
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def mask_bgr_in_hsv(roi_bgr: Mat, hsv_lower: Mat, hsv_upper: Mat):
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return cv2.inRange(
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return cv2.inRange(
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@ -32,6 +34,9 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
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BYD_HSV_MIN = np.array([170, 50, 50], np.uint8)
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BYD_HSV_MIN = np.array([170, 50, 50], np.uint8)
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BYD_HSV_MAX = np.array([179, 210, 198], np.uint8)
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BYD_HSV_MAX = np.array([179, 210, 198], np.uint8)
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ETR_HSV_MIN = np.array([130, 60, 80], np.uint8)
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ETR_HSV_MAX = np.array([140, 145, 180], np.uint8)
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TRACK_LOST_HSV_MIN = np.array([170, 75, 90], np.uint8)
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TRACK_LOST_HSV_MIN = np.array([170, 75, 90], np.uint8)
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TRACK_LOST_HSV_MAX = np.array([175, 170, 160], np.uint8)
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TRACK_LOST_HSV_MAX = np.array([175, 170, 160], np.uint8)
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@ -85,6 +90,10 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
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def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
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def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
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return cls.mask_bgr_in_hsv(roi_bgr, cls.BYD_HSV_MIN, cls.BYD_HSV_MAX)
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return cls.mask_bgr_in_hsv(roi_bgr, cls.BYD_HSV_MIN, cls.BYD_HSV_MAX)
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@classmethod
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def rating_class_etr(cls, roi_bgr: Mat) -> Mat:
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return cls.mask_bgr_in_hsv(roi_bgr, cls.ETR_HSV_MIN, cls.ETR_HSV_MAX)
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@classmethod
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@classmethod
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def max_recall(cls, roi_bgr: Mat) -> Mat:
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def max_recall(cls, roi_bgr: Mat) -> Mat:
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return cls.gray(roi_bgr)
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return cls.gray(roi_bgr)
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@ -116,7 +125,7 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
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class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
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class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
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PFL_HSV_MIN = np.array([0, 0, 248], np.uint8)
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PFL_HSV_MIN = np.array([0, 0, 248], np.uint8)
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PFL_HSV_MAX = np.array([179, 10, 255], np.uint8)
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PFL_HSV_MAX = np.array([179, 40, 255], np.uint8)
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SCORE_HSV_MIN = np.array([0, 0, 180], np.uint8)
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SCORE_HSV_MIN = np.array([0, 0, 180], np.uint8)
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SCORE_HSV_MAX = np.array([179, 255, 255], np.uint8)
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SCORE_HSV_MAX = np.array([179, 255, 255], np.uint8)
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@ -133,6 +142,9 @@ class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
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BYD_HSV_MIN = np.array([170, 50, 50], np.uint8)
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BYD_HSV_MIN = np.array([170, 50, 50], np.uint8)
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BYD_HSV_MAX = np.array([179, 210, 198], np.uint8)
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BYD_HSV_MAX = np.array([179, 210, 198], np.uint8)
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ETR_HSV_MIN = np.array([130, 60, 80], np.uint8)
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ETR_HSV_MAX = np.array([140, 145, 180], np.uint8)
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MAX_RECALL_HSV_MIN = np.array([125, 0, 0], np.uint8)
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MAX_RECALL_HSV_MIN = np.array([125, 0, 0], np.uint8)
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MAX_RECALL_HSV_MAX = np.array([145, 100, 150], np.uint8)
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MAX_RECALL_HSV_MAX = np.array([145, 100, 150], np.uint8)
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@ -184,6 +196,10 @@ class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
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def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
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def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
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return cls.mask_bgr_in_hsv(roi_bgr, cls.BYD_HSV_MIN, cls.BYD_HSV_MAX)
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return cls.mask_bgr_in_hsv(roi_bgr, cls.BYD_HSV_MIN, cls.BYD_HSV_MAX)
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@classmethod
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def rating_class_etr(cls, roi_bgr: Mat) -> Mat:
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return cls.mask_bgr_in_hsv(roi_bgr, cls.ETR_HSV_MIN, cls.ETR_HSV_MAX)
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@classmethod
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@classmethod
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def max_recall(cls, roi_bgr: Mat) -> Mat:
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def max_recall(cls, roi_bgr: Mat) -> Mat:
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return cls.mask_bgr_in_hsv(
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return cls.mask_bgr_in_hsv(
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@ -34,6 +34,10 @@ class DeviceRoisMasker:
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def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
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def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
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raise NotImplementedError()
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raise NotImplementedError()
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@classmethod
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def rating_class_etr(cls, roi_bgr: Mat) -> Mat:
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raise NotImplementedError()
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@classmethod
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@classmethod
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def max_recall(cls, roi_bgr: Mat) -> Mat:
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def max_recall(cls, roi_bgr: Mat) -> Mat:
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raise NotImplementedError()
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raise NotImplementedError()
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@ -36,7 +36,7 @@ class FixRects:
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if rect in consumed_rects:
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if rect in consumed_rects:
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continue
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continue
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x, y, w, h = rect
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x, _, w, h = rect
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# grab those small rects
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# grab those small rects
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if not img_height * 0.1 <= h <= img_height * 0.6:
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if not img_height * 0.1 <= h <= img_height * 0.6:
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continue
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continue
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@ -46,7 +46,7 @@ class FixRects:
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for other_rect in rects:
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for other_rect in rects:
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if rect == other_rect:
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if rect == other_rect:
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continue
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continue
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ox, oy, ow, oh = other_rect
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ox, _, ow, _ = other_rect
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if abs(x - ox) < tolerance and abs((x + w) - (ox + ow)) < tolerance:
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if abs(x - ox) < tolerance and abs((x + w) - (ox + ow)) < tolerance:
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group.append(other_rect)
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group.append(other_rect)
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@ -12,7 +12,8 @@ def phash_opencv(img_gray, hash_size=8, highfreq_factor=4):
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"""
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"""
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Perceptual Hash computation.
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Perceptual Hash computation.
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Implementation follows http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
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Implementation follows
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http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
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Adapted from `imagehash.phash`, pure opencv implementation
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Adapted from `imagehash.phash`, pure opencv implementation
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@ -69,14 +70,14 @@ class ImagePhashDatabase:
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self.partner_icon_ids: List[str] = []
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self.partner_icon_ids: List[str] = []
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self.partner_icon_hashes = []
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self.partner_icon_hashes = []
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for id, hash in zip(self.ids, self.hashes):
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for _id, _hash in zip(self.ids, self.hashes):
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id_splitted = id.split("||")
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id_splitted = _id.split("||")
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if len(id_splitted) > 1 and id_splitted[0] == "partner_icon":
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if len(id_splitted) > 1 and id_splitted[0] == "partner_icon":
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self.partner_icon_ids.append(id_splitted[1])
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self.partner_icon_ids.append(id_splitted[1])
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self.partner_icon_hashes.append(hash)
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self.partner_icon_hashes.append(_hash)
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else:
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else:
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self.jacket_ids.append(id)
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self.jacket_ids.append(_id)
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self.jacket_hashes.append(hash)
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self.jacket_hashes.append(_hash)
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def calculate_phash(self, img_gray: Mat):
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def calculate_phash(self, img_gray: Mat):
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return phash_opencv(
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return phash_opencv(
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@ -42,5 +42,5 @@ def apply_factor(item: T, factor: float) -> T:
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def apply_factor(item, factor: float):
|
def apply_factor(item, factor: float):
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if isinstance(item, (int, float)):
|
if isinstance(item, (int, float)):
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return item * factor
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return item * factor
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elif isinstance(item, Iterable):
|
if isinstance(item, Iterable):
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return item.__class__([i * factor for i in item])
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return item.__class__([i * factor for i in item])
|
||||||
|
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Block a user