cleanup

digging_main.py

@@ -1,182 +0,0 @@
-import random
-from time import time
-from window_capture import WindowCapture
-from vision import Vision
-import cv2 as cv
-import pytesseract
-from hsvfilter import HsvFilter
-from config_file import UserConfigs
-# import pyautogui
-import pydirectinput
-import keyboard
-from tresh_util import super_tresh_main, super_tresh_needle
-
-
-def run():
-    # initialize the user-class
-    config = UserConfigs()
-
-    # PLOT_TO_USE = "business"
-    PLOT_TO_USE = "main_plot"
-    GRID_SIZE_WIDTH = 30
-    GRID_SIZE_DEPTH = 24
-    DIG_TIME = 150
-
-    # initialize the StunWindowCapture class
-    try:
-        capture_window = WindowCapture(
-            None, "dig", config)
-        video_mode = False
-    except:
-        # StunWindowCapture.list_window_names()
-        # print("Game not running, switching to video mode")
-        # capture_window = cv.VideoCapture("snip_slam.mp4")
-        video_mode = True
-
-    # plot_to_use = "business"
-    plot_to_use = "main_plot"
-
-    # initialize the StunVision class
-    vision_stun = Vision()
-    # initialize the StunOverlay class
-    hsv_filter = HsvFilter(0, 0, 124, 15, 255, 168, 0, 255, 0, 0)
-
-    loop_time = time()
-    event_time = 0.0
-    pointstore = []
-    max_results = 0
-    pause = True
-    while True:
-        if keyboard.is_pressed('p') == True:
-            pause = True
-            print('q pressed')
-        elif keyboard.is_pressed('o') == True:
-            pause = False
-            print('o pressed')
-        if pause:
-            # cv.waitKey(500)
-            print("pausing")
-            continue
-
-        if video_mode:
-            break
-        else:
-            try:
-                # get an updated image of the game
-                screenshot = capture_window.get_screenshot()
-                # screenshot = cv.imread("buffbar.jpg")
-            except:
-                capture_window.release()
-                print("Game window not available - shutting down application")
-                break
-        # cv.imshow("screenshot", screenshot)
-        # cv.waitKey(150)
-        # continue
-
-        needles = []
-        if PLOT_TO_USE == "business":
-            needles.append(cv.imread("dig/Brown0.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/1.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/2.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/3.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/4.jpg", cv.IMREAD_UNCHANGED))
-        else:
-            needles.append(cv.imread("dig/H1.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/H2.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/H3.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/H4.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/D1.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/D2.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/D3.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/D3.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/D4.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/D5.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/D6.jpg", cv.IMREAD_UNCHANGED))
-            needles.append(cv.imread("dig/D7.jpg", cv.IMREAD_UNCHANGED))
-
-        for needle in needles:
-            # do object detection
-            screenshot = capture_window.get_screenshot()
-            rectangles = vision_stun.find(screenshot, needle, 0.8, 1)
-            # draw the detection results onto the original image
-            if len(rectangles) == 0:
-                continue
-            #output_image = vision_stun.draw_rectangles(screenshot, rectangles)
-            #cv.imshow("output_image", output_image)
-            #cv.waitKey(150)
-
-            # only trigger ocr reading if a stun is detected
-            points = vision_stun.get_click_points(rectangles)
-            for point in points:
-                # 46 + 1 * 30 1410 == 1815 - 405             return [1410, 1128, 402, 22]
-                # 44 + 1 * 30 1350 == 1790 - 440
-                #                 left_border = 402, 432
-                #                 right_border = 1812, 1782
-                #                 upper_border = 22, 50
-                #                 lower_border = 1150, 1120
-
-                size = rectangles[0][2] + 1
-
-                left = int(round(rectangles[0][0] / size, 0))  # 4
-                down = int(round(rectangles[0][1] / size, 0))  # 23
-                offset_left = config.returnDiggingWindowPos()[2]
-                offset_down = config.returnDiggingWindowPos()[3]
-                # 167 1055 start
-                # 3x47 left 26x right to 30
-                # 1x down 22x up to 24
-
-                # start 167, end 167 - (47 * 3), step -47
-                start_left = point[0] - (size * left)
-                start_up = point[1] - (size * down)
-                for f in range(start_up, start_up + (size * GRID_SIZE_DEPTH), size):
-                    for i in range(start_left, start_left + (size * GRID_SIZE_WIDTH), size):
-                        pydirectinput.moveTo(i + offset_left, f + offset_down)
-                        pydirectinput.mouseDown()
-                        w = random.randint(1, 50)
-                        cv.waitKey(DIG_TIME + w)
-                        pydirectinput.mouseUp()
-                        if keyboard.is_pressed('p') == True or pause == True:
-                            pause = True
-                            break
-
-                    if PLOT_TO_USE == "main_plot":
-                        screenshot = capture_window.get_screenshot()
-                        rectangles = vision_stun.find(screenshot, cv.imread("dig/ok_button.jpg", cv.IMREAD_UNCHANGED), 0.5,
-                                                  1)
-                        # draw the detection results onto the original image
-                        output_image = vision_stun.draw_rectangles(screenshot, rectangles)
-                        if len(rectangles) == 1:
-                            pointis = vision_stun.get_click_points(rectangles)
-                            for pointi in pointis:
-                                pydirectinput.moveTo(pointi[0] + offset_left, pointi[1] + offset_down)
-                                pydirectinput.mouseDown()
-                                w = random.randint(1, 50)
-                                cv.waitKey(DIG_TIME + w)
-                                pydirectinput.mouseUp()
-
-                            if keyboard.is_pressed('p') == True or pause == True:
-                                pause = True
-                                break
-                        if keyboard.is_pressed('p') == True or pause == True:
-                            pause = True
-                            break
-                    if keyboard.is_pressed('p') == True or pause == True:
-                        pause = True
-                        break
-                if keyboard.is_pressed('p') == True or pause == True:
-                    pause = True
-                    break
-            if keyboard.is_pressed('p') == True or pause == True:
-                pause = True
-                break
-
-
-
-        # debug the loop rate
-        print('FPS {}'.format(1 / (time() - loop_time)))
-        loop_time = time()
-        cv.waitKey(150)
-
-
-if __name__ == "__main__":
-    run()

equipment_main.py

@@ -3,13 +3,10 @@ from time import time
 from window_capture import WindowCapture
 from vision import Vision
 import cv2 as cv
-import pytesseract
 from hsvfilter import HsvFilter
 from config_file import UserConfigs
-# import pyautogui
 import pydirectinput
 import keyboard
-from tresh_util import super_tresh_main, super_tresh_needle
 
 EMITTER_MAIN = "main"
 EMITTER_MUSH = "mushroom"

sharpening.py

@@ -1,67 +0,0 @@
-import cv2
-import numpy as np
-
-
-
-image  = cv2.imread("dig/digging.jpg")
-
-cv2.imshow("Image", image)
-
-gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-cv2.imshow("gray", gray)
-
-blur = cv2.GaussianBlur(gray, (5,5), 0)
-cv2.imshow("blur", blur)
-
-thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
-
-#thresh = cv2.bitwise_not(thresh)
-
-cv2.imshow("thresh", thresh)
-#cv2.waitKey()
-contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
-max_area = 0
-c = 0
-for i in contours:
-        area = cv2.contourArea(i)
-        if area > 1000:
-                if area > max_area:
-                    max_area = area
-                    best_cnt = i
-                    image = cv2.drawContours(image, contours, c, (0, 255, 0), 1)
-        c+=1
-
-mask = np.zeros((gray.shape),np.uint8)
-cv2.drawContours(mask,[best_cnt],0,255,-1)
-cv2.drawContours(mask,[best_cnt],0,0,1)
-cv2.imshow("mask", mask)
-
-out = np.zeros_like(gray)
-out[mask == 255] = gray[mask == 255]
-cv2.imshow("New image", out)
-
-blur = cv2.GaussianBlur(out, (5,5), 0)
-cv2.imshow("blur1", blur)
-
-thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
-thresh = cv2.bitwise_not(thresh)
-cv2.imshow("thresh1", thresh)
-
-contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
-squares = []
-c = 0
-for i in contours:
-        area = cv2.contourArea(i)
-        cnt_len = cv2.arcLength(i, True)
-        cnt = cv2.approxPolyDP(i, 0.01 * cnt_len, True)
-        if area > 1000/2:
-            squares.append(cnt)
-            #cv2.drawContours(image, contours, c, (0, 255, 0), 1)
-        c+=1
-cv2.drawContours(image, squares, -1, (0, 255, 0), 1)
-
-cv2.imshow("Final Image", image)
-cv2.waitKey(0)
-cv2.destroyAllWindows()

tresh_util.py

@@ -1,94 +0,0 @@
-import cv2
-import numpy as np
-
-
-def super_tresh_main(img):
-    image = img
-
-    # cv2.imshow("Image", image)
-
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    # cv2.imshow("gray", gray)
-
-    blur = cv2.GaussianBlur(gray, (5, 5), 0)
-    # cv2.imshow("blur", blur)
-
-    thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
-
-    # thresh = cv2.bitwise_not(thresh)
-
-    # cv2.imshow("thresh", thresh)
-    # cv2.waitKey()
-    contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
-    max_area = 0
-    c = 0
-    for i in contours:
-        area = cv2.contourArea(i)
-        if area > 1000:
-            if area > max_area:
-                max_area = area
-                best_cnt = i
-                image = cv2.drawContours(image, contours, c, (0, 255, 0), 1)
-        c += 1
-
-    mask = np.zeros((gray.shape), np.uint8)
-    cv2.drawContours(mask, [best_cnt], 0, 255, -1)
-    cv2.drawContours(mask, [best_cnt], 0, 0, 1)
-    # cv2.imshow("mask", mask)
-
-    out = np.zeros_like(gray)
-    out[mask == 255] = gray[mask == 255]
-    # cv2.imshow("New image", out)
-
-    blur = cv2.GaussianBlur(out, (5, 5), 0)
-    # cv2.imshow("blur1", blur)
-
-    thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
-    return cv2.bitwise_not(thresh)
-
-def super_tresh_needle(img):
-    image = img
-
-    # cv2.imshow("Image", image)
-
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    # cv2.imshow("gray", gray)
-
-    blur = cv2.GaussianBlur(gray, (5, 5), 0)
-    # cv2.imshow("blur", blur)
-
-    thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
-
-    return cv2.bitwise_not(thresh)
-'''
-    # cv2.imshow("thresh", thresh)
-    # cv2.waitKey()
-    contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
-    max_area = 0
-    c = 0
-    for i in contours:
-        area = cv2.contourArea(i)
-        if area > 1000:
-            if area > max_area:
-                max_area = area
-                best_cnt = i
-                image = cv2.drawContours(image, contours, c, (0, 255, 0), 1)
-        c += 1
-
-    mask = np.zeros((gray.shape), np.uint8)
-    cv2.drawContours(mask, [best_cnt], 0, 255, -1)
-    cv2.drawContours(mask, [best_cnt], 0, 0, 1)
-    # cv2.imshow("mask", mask)
-
-    out = np.zeros_like(gray)
-    out[mask == 255] = gray[mask == 255]
-    # cv2.imshow("New image", out)
-
-    blur = cv2.GaussianBlur(out, (5, 5), 0)
-    # cv2.imshow("blur1", blur)
-
-    thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
-    return cv2.bitwise_not(thresh)
-'''

utils.py

@@ -11,8 +11,6 @@ import cv2 as cv
 
 import pydirectinput
 import keyboard
-from tresh_util import super_tresh_main, super_tresh_needle
-
 
 def mse(imageA, imageB):
     # the 'Mean Squared Error' between the two images is the