from dataclasses import dataclass from typing import Any import cv2 import numpy as np from scipy import signal from module.base.utils import ( area_offset, area_pad, color_similarity_2d, crop, get_bbox, image_size, rgb2yuv ) from module.logger import logger from tasks.map.minimap.utils import ( convolve, cubic_find_maximum, image_center_crop, map_image_preprocess, peak_confidence ) from tasks.map.resource.resource import MapResource @dataclass class PositionPredictState: size: Any = None scale: Any = None search_area: Any = None search_image: Any = None result_mask: Any = None result: Any = None sim: Any = None loca: Any = None local_sim: Any = None local_loca: Any = None precise_sim: Any = None precise_loca: Any = None global_loca: Any = None class Minimap(MapResource): def init_position(self, position: tuple[int | float, int | float]): logger.info(f"init_position:{position}") self.position = position def _predict_position(self, image, scale=1.0): """ Args: image: scale: Returns: PositionPredictState: """ scale *= self.POSITION_SEARCH_SCALE local = cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC) size = np.array(image_size(image)) if sum(self.position) > 0: search_position = np.array(self.position, dtype=np.int64) search_position += self.POSITION_FEATURE_PAD search_size = np.array(image_size(local)) * self.POSITION_SEARCH_RADIUS search_half = (search_size // 2).astype(np.int64) search_area = area_offset((0, 0, *(search_half * 2)), offset=-search_half) search_area = area_offset(search_area, offset=np.multiply(search_position, self.POSITION_SEARCH_SCALE)) search_area = np.array(search_area).astype(np.int64) search_image = crop(self.assets_floor_feat, search_area, copy=False) result_mask = crop(self.assets_floor_outside_mask, search_area, copy=False) else: search_area = (0, 0, *image_size(local)) search_image = self.assets_floor_feat result_mask = self.assets_floor_outside_mask # if round(scale, 5) == self.POSITION_SEARCH_SCALE * 1.0: # Image.fromarray((local).astype(np.uint8)).save('local.png') # Image.fromarray((search_image).astype(np.uint8)).save('search_image.png') # Using mask will take 3 times as long # mask = self.get_circle_mask(local) # result = cv2.matchTemplate(search_image, local, cv2.TM_CCOEFF_NORMED, mask=mask) result = cv2.matchTemplate(search_image, local, cv2.TM_CCOEFF_NORMED) result_mask = image_center_crop(result_mask, size=image_size(result)) result[result_mask] = 0 _, sim, _, loca = cv2.minMaxLoc(result) # if round(scale, 3) == self.POSITION_SEARCH_SCALE * 1.0: # result[result <= 0] = 0 # Image.fromarray((result * 255).astype(np.uint8)).save('match_result.png') # Gaussian filter to get local maximum local_maximum = cv2.subtract(result, cv2.GaussianBlur(result, (5, 5), 0)) _, local_sim, _, local_loca = cv2.minMaxLoc(local_maximum) # if round(scale, 5) == self.POSITION_SEARCH_SCALE * 1.0: # local_maximum[local_maximum < 0] = 0 # local_maximum[local_maximum > 0.1] = 0.1 # Image.fromarray((local_maximum * 255 * 10).astype(np.uint8)).save('local_maximum.png') # Calculate the precise location using CUBIC # precise = crop(result, area=area_offset((-4, -4, 4, 4), offset=local_loca)) # precise_sim, precise_loca = cubic_find_maximum(precise, precision=0.05) # precise_loca -= 5 precise_loca = np.array((0, 0)) precise_sim = result[local_loca[1], local_loca[0]] state = PositionPredictState( size=size, scale=scale, search_area=search_area, search_image=search_image, result_mask=result_mask, result=result, sim=sim, loca=loca, local_sim=local_sim, local_loca=local_loca, precise_sim=precise_sim, precise_loca=precise_loca, ) # Location on search_image lookup_loca = precise_loca + local_loca + size * scale / 2 # Location on GIMAP global_loca = (lookup_loca + search_area[:2]) / self.POSITION_SEARCH_SCALE # Can't figure out why but the result_of_0.5_lookup_scale + 0.5 ~= result_of_1.0_lookup_scale global_loca += self.POSITION_MOVE_PATCH # Move to the origin point of map global_loca -= self.POSITION_FEATURE_PAD state.global_loca = global_loca return state def _predict_precise_position(self, state): """ Args: result (PositionPredictState): Returns: PositionPredictState """ size = state.size scale = state.scale search_area = state.search_area result = state.result loca = state.loca local_loca = state.local_loca precise = crop(result, area=area_offset((-4, -4, 4, 4), offset=loca)) precise_sim, precise_loca = cubic_find_maximum(precise, precision=0.05) precise_loca -= 5 state.precise_sim = precise_sim state.precise_loca = precise_loca # Location on search_image lookup_loca = precise_loca + local_loca + size * scale / 2 # Location on GIMAP global_loca = (lookup_loca + search_area[:2]) / self.POSITION_SEARCH_SCALE # Can't figure out why but the result_of_0.5_lookup_scale + 0.5 ~= result_of_1.0_lookup_scale global_loca += self.POSITION_MOVE_PATCH # Move to the origin point of map global_loca -= self.POSITION_FEATURE_PAD state.global_loca = global_loca return state def update_position(self, image): """ Get position on GIMAP, costs about 6.57ms. The following attributes will be set: - position_similarity - position - position_scene """ image = self.get_minimap(image, self.POSITION_RADIUS) image = map_image_preprocess(image) image &= self.get_circle_mask(image) best_sim = -1. best_scale = 1.0 best_state = None # Walking is in scale 1.20 # Running is in scale 1.25 scale_list = [1.00, 1.05, 1.10, 1.15, 1.20, 1.25] for scale in scale_list: state = self._predict_position(image, scale) # print([np.round(i, 3) for i in [scale, state.sim, state.local_sim, state.global_loca]]) if state.sim > best_sim: best_sim = state.sim best_scale = scale best_state = state best_state = self._predict_precise_position(best_state) self.position_similarity = round(best_state.precise_sim, 3) self.position_similarity_local = round(best_state.local_sim, 3) self.position = tuple(np.round(best_state.global_loca, 1)) self.position_scale = round(best_scale, 3) return self.position def update_direction(self, image): """ Get direction of character, costs about 0.64ms. The following attributes will be set: - direction_similarity - direction """ image = self.get_minimap(image, self.DIRECTION_RADIUS) image = color_similarity_2d(image, color=self.DIRECTION_ARROW_COLOR) try: area = area_pad(get_bbox(image, threshold=128), pad=-1) except IndexError: # IndexError: index 0 is out of bounds for axis 0 with size 0 logger.warning('No direction arrow on minimap') return image = crop(image, area=area) scale = self.DIRECTION_ROTATION_SCALE * self.DIRECTION_SEARCH_SCALE mapping = cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST) result = cv2.matchTemplate(self.ArrowRotateMap, mapping, cv2.TM_CCOEFF_NORMED) result = cv2.subtract(result, cv2.GaussianBlur(result, (5, 5), 0)) _, sim, _, loca = cv2.minMaxLoc(result) loca = np.array(loca) / self.DIRECTION_SEARCH_SCALE // (self.DIRECTION_RADIUS * 2) degree = int((loca[0] + loca[1] * 8) * 5) def to_map(x): return int((x * self.DIRECTION_RADIUS * 2 + self.DIRECTION_RADIUS) * self.POSITION_SEARCH_SCALE) # Row on ArrowRotateMapAll row = int(degree // 8) + 45 # Calculate +-1 rows to get result with a precision of 1 row = (row - 2, row + 3) # Convert to ArrowRotateMapAll and to be 5px larger row = (to_map(row[0]) - 5, to_map(row[1]) + 5) precise_map = self.ArrowRotateMapAll[row[0]:row[1], :] result = cv2.matchTemplate(precise_map, mapping, cv2.TM_CCOEFF_NORMED) result = cv2.subtract(result, cv2.GaussianBlur(result, (5, 5), 0)) def to_map(x): return int((x * self.DIRECTION_RADIUS * 2) * self.POSITION_SEARCH_SCALE) def get_precise_sim(d): y, x = divmod(d, 8) im = result[to_map(y):to_map(y + 1), to_map(x):to_map(x + 1)] _, sim, _, _ = cv2.minMaxLoc(im) return sim precise = np.array([[get_precise_sim(_) for _ in range(24)]]) precise_sim, precise_loca = cubic_find_maximum(precise, precision=0.1) precise_loca = degree // 8 * 8 - 8 + precise_loca[0] self.direction_similarity = round(precise_sim, 3) self.direction = precise_loca % 360 def update_rotation(self, image): """ Get direction of character, costs about 0.66ms. The following attributes will be set: - direction_similarity - direction """ d = self.MINIMAP_RADIUS * 2 scale = 1 # Extract minimap = self.get_minimap(image, radius=self.MINIMAP_RADIUS) _, _, v = cv2.split(rgb2yuv(minimap)) image = cv2.subtract(128, v) image = cv2.GaussianBlur(image, (3, 3), 0) # Expand circle into rectangle remap = cv2.remap(image, *self.RotationRemapData, cv2.INTER_LINEAR)[d * 1 // 10:d * 6 // 10].astype(np.float32) remap = cv2.resize(remap, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR) # Find derivative gradx = cv2.Scharr(remap, cv2.CV_32F, 1, 0) # import matplotlib.pyplot as plt # plt.imshow(gradx) # plt.show() # Magic parameters for scipy.find_peaks para = { # 'height': (50, 800), 'height': 35, # 'prominence': (0, 400), # 'width': (0, d * scale / 20), # 'distance': d * scale / 18, 'wlen': d * scale, } # plt.plot(gradx[d * 3 // 10]) # plt.show() # `l` for the left of sight area, derivative is positive # `r` for the right of sight area, derivative is negative l = np.bincount(signal.find_peaks(gradx.ravel(), **para)[0] % (d * scale), minlength=d * scale) r = np.bincount(signal.find_peaks(-gradx.ravel(), **para)[0] % (d * scale), minlength=d * scale) l, r = np.maximum(l - r, 0), np.maximum(r - l, 0) # plt.plot(l) # plt.plot(np.roll(r, -d * scale // 4)) # plt.show() conv0 = [] kernel = 2 * scale r_expanded = np.concatenate([r, r, r]) r_length = len(r) # Faster than nested calling np.roll() def roll_r(shift): return r_expanded[r_length - shift:r_length * 2 - shift] def convolve_r(ker, shift): return sum(roll_r(shift + i) * (ker - abs(i)) // ker for i in range(-ker + 1, ker)) for offset in range(-kernel + 1, kernel): result = l * convolve_r(ker=3 * kernel, shift=-d * scale // 4 + offset) # result = l * convolve(np.roll(r, -d * scale // 4 + offset), kernel=3 * scale) # minus = l * convolve(np.roll(r, offset), kernel=10 * scale) // 5 # if offset == 0: # plt.plot(result) # plt.plot(-minus) # plt.show() # result -= minus # result = convolve(result, kernel=3 * scale) conv0 += [result] # plt.figure(figsize=(20, 16)) # for row in conv0: # plt.plot(row) # plt.show() conv0 = np.maximum(conv0, 1) maximum = np.max(conv0, axis=0) rotation_confidence = round(peak_confidence(maximum), 3) if rotation_confidence > 0.3: # Good match result = maximum else: # Convolve again to reduce noice average = np.mean(conv0, axis=0) minimum = np.min(conv0, axis=0) result = convolve(maximum * average * minimum, 2 * scale) rotation_confidence = round(peak_confidence(maximum), 3) # plt.plot(maximum) # plt.plot(result) # plt.show() # Convert match point to degree degree = np.argmax(result) / (d * scale) * 360 + 135 degree = int(degree % 360) # +3 is a value obtained from experience # Don't know why but + 3 = rotation = degree + 3 self.rotation_confidence = rotation_confidence self.rotation = rotation def update(self, image, show_log=True): """ Update minimap, costs about 7.88ms. """ self.update_position(image) self.update_direction(image) self.update_rotation(image) if show_log: self.log_minimap() def log_minimap(self): # MiniMap P:(567.5, 862.8) (1.00x|0.439|0.157), D:303.8 (0.253), R:304 (0.846) logger.info( f'MiniMap ' f'P:({self.position[0]:.1f}, {self.position[1]:.1f}) ' f'({self.position_scale:.2f}x|{self.position_similarity:.3f}|{self.position_similarity_local:.3f}), ' f'D:{self.direction:.1f} ({self.direction_similarity:.3f}), ' f'R:{self.rotation} ({self.rotation_confidence:.3f})' ) if __name__ == '__main__': """ Run mimimap tracking test. """ from tasks.base.ui import UI # Uncomment this to use local srcmap instead of the pre-built one # MapResource.SRCMAP = '../srcmap/srcmap' self = Minimap() # Set plane, assume starting from Jarilo_AdministrativeDistrict self.set_plane('Jarilo_BackwaterPass', floor='F1') ui = UI('src') ui.device.disable_stuck_detection() # Set starter point. Starter point will be calculated if it's missing but may contain errors. # With starter point set, position is only searched around starter point and new position becomes new starter point. # self.init_position((337, 480)) while 1: ui.device.screenshot() self.update(ui.device.image) self.show_minimap()