Created
December 9, 2020 09:56
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| def Scan(self): | |
| try: | |
| dota = gw.getWindowsWithTitle("Dota 2")[0] | |
| dota.activate() | |
| except IndexError as error: | |
| pass | |
| time.sleep(0.2) | |
| pyautogui.click(x=347, y=798) | |
| pyautogui.click(x=348, y=798) | |
| pyautogui.click(x=349, y=798) | |
| self.progressbar.setText("Occupying mouse for taking pictures, please don't use it...") | |
| self.progressbar.setVisible(True) | |
| os.chdir("C:/autochess_data") | |
| screenshot = pyautogui.screenshot() | |
| screenshot = np.asarray(screenshot) | |
| player_status_list = [screenshot[438:449, 1705:1754], screenshot[525:536, 1705:1754], | |
| screenshot[613:624, 1705:1754], screenshot[701:712, 1705:1754], | |
| screenshot[788:799, 1705:1754], | |
| screenshot[875:886, 1705:1754]] | |
| failed_icon = cv2.imread("C:/autochess_data/failed_icon.png") | |
| transparent_failed_icon = cv2.imread("C:/autochess_data/transparent_failed_icon.png") | |
| num_of_lost_players = 0 | |
| for i in range(0, len(player_status_list)): | |
| if max(cv2.matchTemplate(player_status_list[i], failed_icon, cv2.TM_CCOEFF_NORMED)) > 0.8 or max( | |
| cv2.matchTemplate(player_status_list[i], transparent_failed_icon, cv2.TM_CCOEFF_NORMED)) > 0.8: | |
| num_of_lost_players = 6 - i | |
| break | |
| keyboard.press_and_release("f3") | |
| time.sleep(0.5) | |
| on_stage_heroes = pyautogui.screenshot() | |
| keyboard.press_and_release("f3") | |
| on_stage_heroes.save("on_stage_heroes.png") | |
| time.sleep(0.3) | |
| count = 0 | |
| pyautogui.click(x=1602, y=233) | |
| pyautogui.click(x=1602, y=233) | |
| time.sleep(TIMESLEEP) | |
| img = pyautogui.screenshot(region=(527, 619, 848, 212)) | |
| count += 1 | |
| img.save(f"{count}.jpg") | |
| pyautogui.click(x=1602, y=320) | |
| pyautogui.click(x=1602, y=320) | |
| time.sleep(TIMESLEEP) | |
| img = pyautogui.screenshot(region=(527, 619, 848, 212)) | |
| count += 1 | |
| img.save(f"{count}.jpg") | |
| if num_of_lost_players < 6: | |
| pyautogui.click(x=1602, y=409) | |
| pyautogui.click(x=1602, y=409) | |
| time.sleep(TIMESLEEP) | |
| img = pyautogui.screenshot(region=(527, 619, 848, 212)) | |
| count += 1 | |
| img.save(f"{count}.jpg") | |
| else: | |
| img = cv2.imread("C:/autochess_data/Lost.png") | |
| count += 1 | |
| cv2.imwrite(f"{count}.jpg", img) | |
| if num_of_lost_players < 5: | |
| pyautogui.click(x=1602, y=497) | |
| pyautogui.click(x=1602, y=497) | |
| time.sleep(TIMESLEEP) | |
| img = pyautogui.screenshot(region=(527, 619, 848, 212)) | |
| count += 1 | |
| img.save(f"{count}.jpg") | |
| else: | |
| img = cv2.imread("C:/autochess_data/Lost.png") | |
| count += 1 | |
| cv2.imwrite(f"{count}.jpg", img) | |
| if num_of_lost_players < 4: | |
| pyautogui.click(x=1602, y=586) | |
| pyautogui.click(x=1602, y=586) | |
| time.sleep(TIMESLEEP) | |
| img = pyautogui.screenshot(region=(527, 619, 848, 212)) | |
| count += 1 | |
| img.save(f"{count}.jpg") | |
| else: | |
| img = cv2.imread("C:/autochess_data/Lost.png") | |
| count += 1 | |
| cv2.imwrite(f"{count}.jpg", img) | |
| if num_of_lost_players < 3: | |
| pyautogui.click(x=1602, y=670) | |
| pyautogui.click(x=1602, y=670) | |
| pyautogui.moveTo(x=1546, y=620) | |
| time.sleep(0.6) | |
| img = pyautogui.screenshot(region=(527, 619, 848, 212)) | |
| count += 1 | |
| img.save(f"{count}.jpg") | |
| else: | |
| img = cv2.imread("C:/autochess_data/Lost.png") | |
| count += 1 | |
| cv2.imwrite(f"{count}.jpg", img) | |
| if num_of_lost_players < 2: | |
| pyautogui.click(x=1602, y=758) | |
| pyautogui.click(x=1602, y=758) | |
| pyautogui.moveTo(x=1546, y=708) | |
| time.sleep(0.6) | |
| img = pyautogui.screenshot(region=(527, 619, 848, 212)) | |
| count += 1 | |
| img.save(f"{count}.jpg") | |
| else: | |
| img = cv2.imread("C:/autochess_data/Lost.png") | |
| count += 1 | |
| cv2.imwrite(f"{count}.jpg", img) | |
| if num_of_lost_players < 1: | |
| pyautogui.click(x=1602, y=844) | |
| pyautogui.click(x=1602, y=844) | |
| pyautogui.moveTo(x=1546, y=794) | |
| time.sleep(0.6) | |
| img = pyautogui.screenshot(region=(527, 619, 848, 212)) | |
| count += 1 | |
| img.save(f"{count}.jpg") | |
| else: | |
| img = cv2.imread("C:/autochess_data/Lost.png") | |
| count += 1 | |
| cv2.imwrite(f"{count}.jpg", img) | |
| self.progressbar.setText("Feeding pictures to the neural network...") | |
| os.chdir("G:/darknet/") | |
| os.system( | |
| "G:\darknet/darknet.exe detector test G:\darknet/obj.data G:/darknet/thresh_cfg.cfg G:\darknetsave/yolov4-obj_last_25.weights -ext_output -dont_show -out result.json < data/train.txt") | |
| f = open("G:/darknet/result.json") | |
| data = json.load(f) | |
| all_chess_pieces_list = ([], [], [], [], [], [], [], []) | |
| for i in range(8): | |
| for hero in data[i]["objects"]: | |
| all_chess_pieces_list[i].append(hero["name"]) | |
| on_stage_heroes = cv2.imread("C:/autochess_data/on_stage_heroes.png") | |
| on_stage_heroes_grayed = cv2.cvtColor(on_stage_heroes, cv2.COLOR_BGR2GRAY) | |
| temp_on_stage_list = ([], [], [], [], [], [], [], []) | |
| os.chdir("C:/autochess_data/") | |
| count3 = 0 | |
| num_of_lost_players = 0 | |
| self.progressbar.setText("Template matching icons...") | |
| for i in range(0, 80 - num_of_lost_players * 10): | |
| if i % 10 == 0: | |
| count3 = 0 + int(i / 10) | |
| hero_level = "" | |
| level_icon_template = on_stage_heroes_grayed[ | |
| Y_COORDINATES_END[count3] + 1:Y_COORDINATES_END[count3] + 1 + LEVEL_ICON_HEIGHT, | |
| X_COORDINATES_START[i % 10]:X_COORDINATES_END[i % 10]] | |
| for level in level_dict: | |
| res = cv2.matchTemplate(level_dict[level], level_icon_template, cv2.TM_CCOEFF_NORMED) | |
| if max(res) > 0.7: | |
| hero_level = level | |
| break | |
| template = on_stage_heroes[Y_COORDINATES_START[count3]:Y_COORDINATES_END[count3], | |
| X_COORDINATES_START[i % 10]:X_COORDINATES_END[i % 10]] | |
| all_matches = [] | |
| for hero_name in namelist: | |
| cv2.waitKey(0) | |
| res = cv2.matchTemplate(hero_icon_dict[hero_name], template, cv2.TM_CCOEFF_NORMED) | |
| loc = np.where(res >= 0.65) | |
| if loc[0].size > 0: | |
| all_matches.append((hero_name, max(res))) | |
| all_matches = sorted(all_matches, key=lambda match: match[1]) | |
| if len(all_matches) > 0: | |
| all_chess_pieces_list[count3].append(all_matches[-1][0] + hero_level) | |
| temp_on_stage_list[count3].append((all_matches[-1][0] + hero_level, Y_COORDINATES_START[count3], | |
| Y_COORDINATES_END[count3] + 1 + LEVEL_ICON_HEIGHT, | |
| X_COORDINATES_START[i % 10], X_COORDINATES_END[i % 10])) | |
| self.progressbar.setText("Tabulating the results...") | |
| flat_list = [item for sublist in all_chess_pieces_list for item in sublist] | |
| Dict = copy.deepcopy(RSCP_Dict) | |
| for hero in flat_list: | |
| if hero.endswith("2"): | |
| Dict[hero[:-1]][4] -= 3 | |
| elif hero.endswith("3"): | |
| Dict[hero[:-1]][4] -= 9 | |
| else: | |
| Dict[hero][4] -= 1 | |
| colors = [Qt.white, QtGui.QColor(170, 170, 255), QtGui.QColor(90, 90, 255), QtGui.QColor(249, 28, 249), | |
| QtGui.QColor(255, 151, 36)] | |
| row = 0 | |
| for hero in Dict: | |
| self.tableWidget.setItem(row, 4, QTableWidgetItem(str(Dict[hero][4]))) | |
| self.tableWidget.item(row, 4).setForeground(colors[int(Dict[hero][0][0]) - 1]) | |
| row += 1 | |
| CAS_dict_copy = copy.deepcopy(CAS_dict) | |
| on_stage_CAS_icons = on_stage_heroes[ | |
| on_stage_CAS_y_coors[7 - num_of_lost_players][0]: | |
| on_stage_CAS_y_coors[7 - num_of_lost_players][ | |
| 1], | |
| on_stage_CAS_x_coors[0]:on_stage_CAS_x_coors[1]] | |
| on_stage_CAS_icons_grayed = cv2.cvtColor(on_stage_CAS_icons, cv2.COLOR_BGR2GRAY) | |
| for CAS in CAS_names_and_icons: | |
| result = cv2.matchTemplate(on_stage_CAS_icons, CAS[1], cv2.TM_CCOEFF_NORMED) | |
| loc = np.where(result >= 0.9) | |
| if loc[0].size == 0: | |
| continue | |
| else: | |
| CAS_dict_copy[CAS[0]][0] = loc[0].size | |
| for i in range(loc[0].size): | |
| number_icon = on_stage_CAS_icons_grayed[loc[0][i] + 25:loc[0][i] + 37, loc[1][i] + 2:loc[1][i] + 19] | |
| result_list = [] | |
| for icon in CAS_numbers_and_icons: | |
| if cv2.matchTemplate(number_icon, icon[1], cv2.TM_CCOEFF_NORMED) > 0.85: | |
| result_list.append((icon[0], cv2.matchTemplate(number_icon, icon[1], cv2.TM_CCOEFF_NORMED))) | |
| result_list = sorted(result_list, key=lambda icon: icon[1], reverse=True) | |
| CAS_dict_copy[CAS[0]][1] += result_list[0][0] | |
| row = 0 | |
| for CAS in CAS_dict_copy: | |
| self.CAS_tableWidget.setItem(row, 0, QTableWidgetItem(CAS)) | |
| self.CAS_tableWidget.setItem(row, 1, QTableWidgetItem(str(CAS_dict_copy[CAS][0]))) | |
| self.CAS_tableWidget.setItem(row, 2, QTableWidgetItem(str(CAS_dict_copy[CAS][1]))) | |
| self.CAS_tableWidget.item(row, 0).setForeground(Qt.white) | |
| self.CAS_tableWidget.item(row, 1).setTextAlignment(Qt.AlignHCenter) | |
| self.CAS_tableWidget.item(row, 1).setForeground(Qt.white) | |
| self.CAS_tableWidget.item(row, 2).setTextAlignment(Qt.AlignHCenter) | |
| self.CAS_tableWidget.item(row, 2).setForeground(Qt.white) | |
| row += 1 | |
| global to_show_list | |
| to_show_list = all_chess_pieces_list | |
| global on_stage_list | |
| on_stage_list = temp_on_stage_list | |
| print(temp_on_stage_list) |
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