mirror of
https://github.com/283375/arcaea-offline-ocr.git
synced 2025-04-19 05:20:17 +00:00
243 lines
9.0 KiB
Python
243 lines
9.0 KiB
Python
from math import floor
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from typing import List, Optional, Tuple
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import cv2
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import numpy as np
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from ....crop import crop_xywh
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from ....ocr import (
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FixRects,
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ocr_digits_by_contour_knn,
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preprocess_hog,
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resize_fill_square,
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)
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from ....phash_db import ImagePhashDatabase
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from ....utils import construct_int_xywh_rect
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from ...shared import B30OcrResultItem
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from .colors import *
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from .rois import ChieriBotV4Rois
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class ChieriBotV4Ocr:
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def __init__(
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self,
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score_knn: cv2.ml.KNearest,
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pfl_knn: cv2.ml.KNearest,
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phash_db: ImagePhashDatabase,
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factor: Optional[float] = 1.0,
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):
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self.__score_knn = score_knn
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self.__pfl_knn = pfl_knn
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self.__phash_db = phash_db
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self.__rois = ChieriBotV4Rois(factor)
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@property
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def score_knn(self):
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return self.__score_knn
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@score_knn.setter
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def score_knn(self, knn_digits_model: cv2.ml.KNearest):
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self.__score_knn = knn_digits_model
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@property
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def pfl_knn(self):
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return self.__pfl_knn
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@pfl_knn.setter
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def pfl_knn(self, knn_digits_model: cv2.ml.KNearest):
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self.__pfl_knn = knn_digits_model
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@property
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def phash_db(self):
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return self.__phash_db
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@phash_db.setter
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def phash_db(self, phash_db: ImagePhashDatabase):
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self.__phash_db = phash_db
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@property
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def rois(self):
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return self.__rois
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@property
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def factor(self):
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return self.__rois.factor
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@factor.setter
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def factor(self, factor: float):
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self.__rois.factor = factor
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def set_factor(self, img: cv2.Mat):
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self.factor = img.shape[0] / 4400
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def ocr_component_rating_class(self, component_bgr: cv2.Mat) -> int:
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rating_class_rect = construct_int_xywh_rect(
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self.rois.component_rois.rating_class_rect
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)
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rating_class_roi = crop_xywh(component_bgr, rating_class_rect)
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rating_class_roi = cv2.cvtColor(rating_class_roi, cv2.COLOR_BGR2HSV)
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rating_class_masks = [
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cv2.inRange(rating_class_roi, PRS_MIN_HSV, PRS_MAX_HSV),
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cv2.inRange(rating_class_roi, FTR_MIN_HSV, FTR_MAX_HSV),
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cv2.inRange(rating_class_roi, BYD_MIN_HSV, BYD_MAX_HSV),
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] # prs, ftr, byd only
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rating_class_results = [np.count_nonzero(m) for m in rating_class_masks]
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if max(rating_class_results) < 70:
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return 0
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else:
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return max(enumerate(rating_class_results), key=lambda i: i[1])[0] + 1
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def ocr_component_song_id(self, component_bgr: cv2.Mat):
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jacket_rect = construct_int_xywh_rect(
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self.rois.component_rois.jacket_rect, floor
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)
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jacket_roi = cv2.cvtColor(
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crop_xywh(component_bgr, jacket_rect), cv2.COLOR_BGR2GRAY
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)
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return self.phash_db.lookup_jacket(jacket_roi)[0]
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def ocr_component_score_knn(self, component_bgr: cv2.Mat) -> int:
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# sourcery skip: inline-immediately-returned-variable
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score_rect = construct_int_xywh_rect(self.rois.component_rois.score_rect)
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score_roi = cv2.cvtColor(
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crop_xywh(component_bgr, score_rect), cv2.COLOR_BGR2GRAY
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)
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_, score_roi = cv2.threshold(
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score_roi, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU
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)
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if score_roi[1][1] == 255:
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score_roi = 255 - score_roi
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contours, _ = cv2.findContours(
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score_roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
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)
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for contour in contours:
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rect = cv2.boundingRect(contour)
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if rect[3] > score_roi.shape[0] * 0.5:
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continue
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score_roi = cv2.fillPoly(score_roi, [contour], 0)
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return ocr_digits_by_contour_knn(score_roi, self.score_knn)
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def find_pfl_rects(self, component_pfl_processed: cv2.Mat) -> List[List[int]]:
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# sourcery skip: inline-immediately-returned-variable
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pfl_roi_find = cv2.morphologyEx(
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component_pfl_processed,
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cv2.MORPH_CLOSE,
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cv2.getStructuringElement(cv2.MORPH_RECT, [10, 1]),
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)
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pfl_contours, _ = cv2.findContours(
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pfl_roi_find, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
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)
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pfl_rects = [cv2.boundingRect(c) for c in pfl_contours]
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pfl_rects = [
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r for r in pfl_rects if r[3] > component_pfl_processed.shape[0] * 0.1
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]
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pfl_rects = sorted(pfl_rects, key=lambda r: r[1])
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pfl_rects_adjusted = [
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(
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max(rect[0] - 2, 0),
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rect[1],
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min(rect[2] + 2, component_pfl_processed.shape[1]),
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rect[3],
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)
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for rect in pfl_rects
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]
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return pfl_rects_adjusted
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def preprocess_component_pfl(self, component_bgr: cv2.Mat) -> cv2.Mat:
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pfl_rect = construct_int_xywh_rect(self.rois.component_rois.pfl_rect)
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pfl_roi = crop_xywh(component_bgr, pfl_rect)
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pfl_roi_hsv = cv2.cvtColor(pfl_roi, cv2.COLOR_BGR2HSV)
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# fill the pfl bg with background color
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bg_point = [round(i) for i in self.rois.component_rois.bg_point]
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bg_color = component_bgr[bg_point[1]][bg_point[0]]
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pure_bg_mask = cv2.inRange(pfl_roi_hsv, PURE_BG_MIN_HSV, PURE_BG_MAX_HSV)
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far_bg_mask = cv2.inRange(pfl_roi_hsv, FAR_BG_MIN_HSV, FAR_BG_MAX_HSV)
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lost_bg_mask = cv2.inRange(pfl_roi_hsv, LOST_BG_MIN_HSV, LOST_BG_MAX_HSV)
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pfl_roi[np.where(pure_bg_mask != 0)] = bg_color
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pfl_roi[np.where(far_bg_mask != 0)] = bg_color
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pfl_roi[np.where(lost_bg_mask != 0)] = bg_color
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# threshold
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pfl_roi = cv2.cvtColor(pfl_roi, cv2.COLOR_BGR2GRAY)
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# get threshold of blurred image, try ignoring the lines of bg bar
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pfl_roi_blurred = cv2.GaussianBlur(pfl_roi, (5, 5), 0)
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# pfl_roi_blurred = cv2.medianBlur(pfl_roi, 3)
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_, pfl_roi_blurred_threshold = cv2.threshold(
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pfl_roi_blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU
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)
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# and a threshold of the original roi
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_, pfl_roi_threshold = cv2.threshold(
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pfl_roi, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU
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)
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# turn thresholds into black background
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if pfl_roi_blurred_threshold[2][2] == 255:
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pfl_roi_blurred_threshold = 255 - pfl_roi_blurred_threshold
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if pfl_roi_threshold[2][2] == 255:
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pfl_roi_threshold = 255 - pfl_roi_threshold
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# return a bitwise_and result
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result = cv2.bitwise_and(pfl_roi_blurred_threshold, pfl_roi_threshold)
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result_eroded = cv2.erode(
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result, cv2.getStructuringElement(cv2.MORPH_CROSS, (2, 2))
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)
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return result_eroded if len(self.find_pfl_rects(result_eroded)) == 3 else result
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def ocr_component_pfl(
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self, component_bgr: cv2.Mat
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) -> Tuple[Optional[int], Optional[int], Optional[int]]:
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try:
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pfl_roi = self.preprocess_component_pfl(component_bgr)
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pfl_rects = self.find_pfl_rects(pfl_roi)
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pure_far_lost = []
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for pfl_roi_rect in pfl_rects:
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roi = crop_xywh(pfl_roi, pfl_roi_rect)
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digit_contours, _ = cv2.findContours(
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roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
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)
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digit_rects = [cv2.boundingRect(c) for c in digit_contours]
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digit_rects = FixRects.connect_broken(
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digit_rects, roi.shape[1], roi.shape[0]
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)
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digit_rects = FixRects.split_connected(roi, digit_rects)
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digit_rects = sorted(digit_rects, key=lambda r: r[0])
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digits = []
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for digit_rect in digit_rects:
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digit = crop_xywh(roi, digit_rect)
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digit = resize_fill_square(digit, 20)
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digits.append(digit)
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samples = preprocess_hog(digits)
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_, results, _, _ = self.pfl_knn.findNearest(samples, 4)
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results = [str(int(i)) for i in results.ravel()]
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pure_far_lost.append(int("".join(results)))
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return tuple(pure_far_lost)
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except Exception:
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return (None, None, None)
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def ocr_component(self, component_bgr: cv2.Mat) -> B30OcrResultItem:
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component_blur = cv2.GaussianBlur(component_bgr, (5, 5), 0)
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rating_class = self.ocr_component_rating_class(component_blur)
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song_id = self.ocr_component_song_id(component_bgr)
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# title = self.ocr_component_title(component_blur)
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# score = self.ocr_component_score(component_blur)
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score = self.ocr_component_score_knn(component_bgr)
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pure, far, lost = self.ocr_component_pfl(component_bgr)
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return B30OcrResultItem(
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song_id=song_id,
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rating_class=rating_class,
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# title=title,
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score=score,
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pure=pure,
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far=far,
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lost=lost,
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date=None,
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)
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def ocr(self, img_bgr: cv2.Mat) -> List[B30OcrResultItem]:
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self.set_factor(img_bgr)
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return [
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self.ocr_component(component_bgr)
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for component_bgr in self.rois.components(img_bgr)
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]
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