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refactor: remove needless module

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283375 2023-10-01 04:00:06 +08:00
parent 2b01f68a73
commit 0d8e4dea8e
Signed by: 283375
SSH Key Fingerprint: SHA256:UcX0qg6ZOSDOeieKPGokA5h7soykG61nz2uxuQgVLSk
5 changed files with 0 additions and 242 deletions
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1
src/arcaea_offline_ocr/__init__.py
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@ -1,5 +1,4 @@
from .crop import *
from .device import *
from .mask import *
from .ocr import *
from .utils import *

1
src/arcaea_offline_ocr/b30/chieri/v4/ocr.py
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@ -8,7 +8,6 @@ from PIL import Image
from ....crop import crop_xywh
from ....ocr import FixRects, ocr_digits_by_contour_knn, preprocess_hog
from ....phash_db import ImagePHashDatabase
from ....sift_db import SIFTDatabase
from ....types import Mat, cv2_ml_KNearest
from ....utils import construct_int_xywh_rect
from ...shared import B30OcrResultItem

119
src/arcaea_offline_ocr/mask.py
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@ -1,119 +0,0 @@
import cv2
import numpy as np
from .types import Mat
__all__ = [
"GRAY_MIN_HSV",
"GRAY_MAX_HSV",
"WHITE_MIN_HSV",
"WHITE_MAX_HSV",
"PFL_WHITE_MIN_HSV",
"PFL_WHITE_MAX_HSV",
"PST_MIN_HSV",
"PST_MAX_HSV",
"PRS_MIN_HSV",
"PRS_MAX_HSV",
"FTR_MIN_HSV",
"FTR_MAX_HSV",
"BYD_MIN_HSV",
"BYD_MAX_HSV",
"MAX_RECALL_PURPLE_MIN_HSV",
"MAX_RECALL_PURPLE_MAX_HSV",
"mask_gray",
"mask_white",
"mask_pfl_white",
"mask_pst",
"mask_prs",
"mask_ftr",
"mask_byd",
"mask_rating_class",
"mask_max_recall_purple",
]
GRAY_MIN_HSV = np.array([0, 0, 70], np.uint8)
GRAY_MAX_HSV = np.array([0, 0, 200], np.uint8)
GRAY_MIN_BGR = np.array([50] * 3, np.uint8)
GRAY_MAX_BGR = np.array([160] * 3, np.uint8)
WHITE_MIN_HSV = np.array([0, 0, 240], np.uint8)
WHITE_MAX_HSV = np.array([179, 10, 255], np.uint8)
PFL_WHITE_MIN_HSV = np.array([0, 0, 248], np.uint8)
PFL_WHITE_MAX_HSV = np.array([179, 10, 255], np.uint8)
PST_MIN_HSV = np.array([100, 50, 80], np.uint8)
PST_MAX_HSV = np.array([100, 255, 255], np.uint8)
PRS_MIN_HSV = np.array([43, 40, 75], np.uint8)
PRS_MAX_HSV = np.array([50, 155, 190], np.uint8)
FTR_MIN_HSV = np.array([149, 30, 0], np.uint8)
FTR_MAX_HSV = np.array([155, 181, 150], np.uint8)
BYD_MIN_HSV = np.array([170, 50, 50], np.uint8)
BYD_MAX_HSV = np.array([179, 210, 198], np.uint8)
MAX_RECALL_PURPLE_MIN_HSV = np.array([125, 0, 0], np.uint8)
MAX_RECALL_PURPLE_MAX_HSV = np.array([130, 100, 150], np.uint8)
def mask_gray(__img_bgr: Mat):
# bgr_value_equal_mask = all(__img_bgr[:, 1:] == __img_bgr[:, :-1], axis=1)
bgr_value_equal_mask = np.max(__img_bgr, axis=2) - np.min(__img_bgr, axis=2) <= 5
img_bgr = __img_bgr.copy()
img_bgr[~bgr_value_equal_mask] = np.array([0, 0, 0], __img_bgr.dtype)
img_bgr = cv2.erode(img_bgr, cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)))
img_bgr = cv2.dilate(img_bgr, cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1)))
return cv2.inRange(img_bgr, GRAY_MIN_BGR, GRAY_MAX_BGR)
def mask_white(img_hsv: Mat):
mask = cv2.inRange(img_hsv, WHITE_MIN_HSV, WHITE_MAX_HSV)
mask = cv2.dilate(mask, cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)))
return mask
def mask_pfl_white(img_hsv: Mat):
mask = cv2.inRange(img_hsv, PFL_WHITE_MIN_HSV, PFL_WHITE_MAX_HSV)
mask = cv2.dilate(mask, cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)))
return mask
def mask_pst(img_hsv: Mat):
mask = cv2.inRange(img_hsv, PST_MIN_HSV, PST_MAX_HSV)
mask = cv2.dilate(mask, (1, 1))
return mask
def mask_prs(img_hsv: Mat):
mask = cv2.inRange(img_hsv, PRS_MIN_HSV, PRS_MAX_HSV)
mask = cv2.dilate(mask, (1, 1))
return mask
def mask_ftr(img_hsv: Mat):
mask = cv2.inRange(img_hsv, FTR_MIN_HSV, FTR_MAX_HSV)
mask = cv2.dilate(mask, (1, 1))
return mask
def mask_byd(img_hsv: Mat):
mask = cv2.inRange(img_hsv, BYD_MIN_HSV, BYD_MAX_HSV)
mask = cv2.dilate(mask, (2, 2))
return mask
def mask_rating_class(img_hsv: Mat):
pst = mask_pst(img_hsv)
prs = mask_prs(img_hsv)
ftr = mask_ftr(img_hsv)
byd = mask_byd(img_hsv)
return cv2.bitwise_or(byd, cv2.bitwise_or(ftr, cv2.bitwise_or(pst, prs)))
def mask_max_recall_purple(img_hsv: Mat):
mask = cv2.inRange(img_hsv, MAX_RECALL_PURPLE_MIN_HSV, MAX_RECALL_PURPLE_MAX_HSV)
mask = cv2.dilate(mask, (2, 2))
return mask

11
src/arcaea_offline_ocr/ocr.py
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@ -7,7 +7,6 @@ import numpy as np
from numpy.linalg import norm
from .crop import crop_xywh
from .mask import mask_byd, mask_ftr, mask_prs, mask_pst
from .types import Mat, cv2_ml_KNearest
__all__ = [
@ -199,13 +198,3 @@ def ocr_digits_by_contour_knn(
) -> int:
samples = ocr_digits_by_contour_get_samples(__roi_gray, size)
return ocr_digit_samples_knn(samples, knn_model, k)
def ocr_rating_class(roi_hsv: Mat):
mask_results = [
mask_pst(roi_hsv),
mask_prs(roi_hsv),
mask_ftr(roi_hsv),
mask_byd(roi_hsv),
]
return max(enumerate(mask_results), key=lambda e: np.count_nonzero(e[1]))[0]

110
src/arcaea_offline_ocr/sift_db.py
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@ -1,110 +0,0 @@
import io
import sqlite3
from gzip import GzipFile
from typing import Tuple
import cv2
import numpy as np
from .types import Mat
class SIFTDatabase:
def __init__(self, db_path: str, load: bool = True):
self.__db_path = db_path
self.__tags = []
self.__descriptors = []
self.__size = None
self.__sift = cv2.SIFT_create()
self.__bf_matcher = cv2.BFMatcher()
if load:
self.load_db()
@property
def db_path(self):
return self.__db_path
@db_path.setter
def db_path(self, value):
self.__db_path = value
@property
def tags(self):
return self.__tags
@property
def descriptors(self):
return self.__descriptors
@property
def size(self):
return self.__size
@size.setter
def size(self, value: Tuple[int, int]):
self.__size = value
@property
def sift(self):
return self.__sift
@property
def bf_matcher(self):
return self.__bf_matcher
def load_db(self):
conn = sqlite3.connect(self.db_path)
with conn:
cursor = conn.cursor()
size_str = cursor.execute(
"SELECT value FROM properties WHERE id = 'size'"
).fetchone()[0]
sizr_str_arr = size_str.split(", ")
self.size = tuple(int(s) for s in sizr_str_arr)
tag__descriptors_bytes = cursor.execute(
"SELECT tag, descriptors FROM sift"
).fetchall()
gzipped = int(
cursor.execute(
"SELECT value FROM properties WHERE id = 'gzip'"
).fetchone()[0]
)
for tag, descriptor_bytes in tag__descriptors_bytes:
buffer = io.BytesIO(descriptor_bytes)
self.tags.append(tag)
if gzipped == 0:
self.descriptors.append(np.load(buffer))
else:
gzipped_buffer = GzipFile(None, "rb", fileobj=buffer)
self.descriptors.append(np.load(gzipped_buffer))
def lookup_img(
self,
__img: Mat,
*,
sift=None,
bf=None,
) -> Tuple[str, float]:
sift = sift or self.sift
bf = bf or self.bf_matcher
img = __img.copy()
if self.size is not None:
img = cv2.resize(img, self.size)
_, descriptors = sift.detectAndCompute(img, None)
good_results = []
for des in self.descriptors:
matches = bf.knnMatch(descriptors, des, k=2)
good = sum(m.distance < 0.75 * n.distance for m, n in matches)
good_results.append(good)
best_match_index = max(enumerate(good_results), key=lambda i: i[1])[0]
return (
self.tags[best_match_index],
good_results[best_match_index] / len(descriptors),
)