220 lines
7.1 KiB
Python
220 lines
7.1 KiB
Python
from functools import partial
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import cv2
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import numpy as np
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from skimage.metrics import structural_similarity
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from mower.static import avatar, portrait
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from mower.utils import typealias as tp
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from mower.utils.image import cmatch, cropimg, loadres, thres2
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from mower.utils.log import logger
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from mower.utils.matcher import GOOD_DISTANCE_LIMIT, flann, search_params
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from mower.utils.vector import in_scope
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FAST = cv2.FastFeatureDetector_create()
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ORB = cv2.ORB_create(nfeatures=1000000, nlevels=1)
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FLANN_INDEX_LSH = 6
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index_params = dict(
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algorithm=FLANN_INDEX_LSH,
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table_number=1,
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key_size=18,
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multi_probe_level=1,
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)
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flann1 = cv2.FlannBasedMatcher(index_params, search_params)
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def fast_keypoints(img: tp.GrayImage, mask: tp.GrayImage | None = None):
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return ORB.compute(img, FAST.detect(img, mask))
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def segment_room_select(img: tp.Image) -> list[tp.Scope]:
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"基建房间内选干员"
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line1 = cropimg(img, ((600, 519), (1920, 520)))
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# 选中时的蓝色边框变白，信赖排序时的橙色变黑
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hsv = cv2.cvtColor(line1, cv2.COLOR_RGB2HSV)
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mask = cv2.inRange(hsv, (95, 230, 200), (105, 255, 230))
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line1 = cv2.cvtColor(line1, cv2.COLOR_RGB2GRAY)
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line1[mask > 0] = (255,)
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mask = cv2.inRange(hsv, (10, 0, 0), (20, 255, 255))
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line1[mask > 0] = (0,)
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line1 = thres2(line1, 100)
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line = line1[-1]
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prev = line[0]
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start = None
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name_x = []
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for i in range(1, line1.shape[1]):
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curr = line[i]
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if prev == 0 and curr == 255 and start and i - start > 186:
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name_x.append((i + 434, i + 574))
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elif prev == 255 and curr == 0:
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start = i
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prev = curr
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avatar_y = ((242, 317), (662, 737))
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avatar_p = []
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for x in name_x:
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for y in avatar_y:
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avatar_p.append(tuple(zip(x, y)))
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empty = loadres("choose_agent/empty")
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if avatar_p and cmatch(cropimg(img, avatar_p[-1]), empty, 20):
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avatar_p.pop()
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logger.debug(avatar_p)
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return avatar_p
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def segment_team(img: tp.Image) -> list[tp.Scope]:
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"编队界面"
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result = [
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((190 + 232 * x, 235 + 433 * y), (330 + 232 * x, 310 + 433 * y))
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for x in range(6)
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for y in range(2)
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] + [((1582, 668), (1722, 743))]
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res = loadres("choose_agent/no_oper")
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for i in range(13):
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if cmatch(cropimg(img, result[i]), res):
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gray = cropimg(cv2.cvtColor(img, cv2.COLOR_RGB2GRAY), result[i])
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res_gray = cv2.cvtColor(res, cv2.COLOR_RGB2GRAY)
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ssim = structural_similarity(gray, res_gray)
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logger.debug(f"cmatch+SSIM:{i=} {ssim=}")
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if ssim >= 0.9:
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break
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return result[:i]
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def segment_team_select(img: tp.Image) -> list[tp.Scope]:
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"作战编队选干员"
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line1 = cropimg(img, ((545, 510), (1920, 511)))
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# 选中时的蓝色边框变白，信赖排序时的橙色变黑
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hsv = cv2.cvtColor(line1, cv2.COLOR_RGB2HSV)
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mask = cv2.inRange(hsv, (95, 230, 200), (105, 255, 230))
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line1 = cv2.cvtColor(line1, cv2.COLOR_RGB2GRAY)
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line1[mask > 0] = (255,)
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mask = cv2.inRange(hsv, (10, 0, 0), (20, 255, 255))
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line1[mask > 0] = (0,)
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line1 = thres2(line1, 100)
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line = line1[-1]
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prev = line[0]
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start = None
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name_x = []
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for i in range(1, line1.shape[1]):
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curr = line[i]
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if prev == 0 and curr == 255 and start and i - start > 90:
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name_x.append((i + 381, i + 521))
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elif prev == 255 and curr == 0:
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start = i
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prev = curr
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avatar_y = ((229, 304), (645, 720))
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avatar_p = []
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for x in name_x:
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for y in avatar_y:
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avatar_p.append(tuple(zip(x, y)))
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empty = loadres("choose_agent/empty")
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if avatar_p and cmatch(cropimg(img, avatar_p[-1]), empty, 20):
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avatar_p.pop()
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logger.debug(avatar_p)
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return avatar_p
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def match_operator(
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gray: tp.GrayImage, segment: list[tp.Scope], model: list
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) -> list[tuple[str | None, tp.Scope]]:
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if len(segment) == 0:
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logger.debug(f"{len(segment)=}")
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return ()
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w = model["w"]
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h = model["h"]
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col = model["col"]
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name_list = model["name"]
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pt2 = model["pt"]
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des2 = model["des"]
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mask = np.zeros((1080, 1920), dtype=np.uint8)
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for seg in segment:
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cv2.rectangle(mask, seg[0], seg[1], (255,), -1)
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kp1, des1 = fast_keypoints(gray, mask)
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result = [{} for _ in range(len(segment))]
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for match in flann.knnMatch(des1, des2, k=2):
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if len(match) != 2:
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continue
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m, n = match
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if m.distance > GOOD_DISTANCE_LIMIT * n.distance:
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continue
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x, y = pt2[m.trainIdx]
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x, y = int(x / w), int(y / h)
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name = name_list[y * col + x]
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for i, seg in enumerate(segment):
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if in_scope(seg, kp1[m.queryIdx].pt):
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if name in result[i]:
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result[i][name] += 1
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else:
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result[i][name] = 1
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break
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result = [list(r.items()) for r in result]
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result = [max(r, key=lambda p: p[1]) if r else None for r in result]
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logger.debug(result)
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bad_index = []
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for i, r in enumerate(result):
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if r is None or r[1] < 10:
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bad_index.append(i)
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if bad_index:
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mask = np.zeros((1080, 1920), dtype=np.uint8)
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for i in bad_index:
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cv2.rectangle(mask, segment[i][0], segment[i][1], (255,), -1)
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kp1, des1 = fast_keypoints(gray, mask)
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result1 = [{} for _ in range(len(bad_index))]
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for match in flann1.knnMatch(des1, des2, k=2):
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if len(match) != 2:
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continue
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m, n = match
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if m.distance > GOOD_DISTANCE_LIMIT * n.distance:
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continue
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x, y = pt2[m.trainIdx]
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x, y = int(x / w), int(y / h)
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name = name_list[y * col + x]
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for i, j in enumerate(bad_index):
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if in_scope(segment[j], kp1[m.queryIdx].pt):
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if name in result1[i]:
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result1[i][name] += 1
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else:
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result1[i][name] = 1
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break
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result1 = [list(r.items()) for r in result1]
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result1 = [max(r, key=lambda p: p[1]) if r else None for r in result1]
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logger.debug(result1)
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for i, r in enumerate(result1):
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result[bad_index[i]] = r
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return list(zip([r[0] if r else None for r in result], segment))
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match_portrait = partial(match_operator, model=portrait)
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match_avatar = partial(match_operator, model=avatar)
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def operator_room_select(img: tp.Image) -> list[tuple[str | None, tp.Scope]]:
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"基建房间内选干员"
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gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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return match_portrait(gray, segment_room_select(img))
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def operator_team(img: tp.Image) -> list[tuple[str | None, tp.Scope]]:
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"编队界面"
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gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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return match_portrait(gray, segment_team(img))
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def operator_team_select(img: tp.Image) -> list[tuple[str | None, tp.Scope]]:
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"作战编队选干员"
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gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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return match_portrait(gray, segment_team_select(img))
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