Initial commit

rest.py

@@ -0,0 +1,178 @@
+RGB_SCALE = 255
+CMYK_SCALE = 100
+
+
+def rgb_to_cmyk(r, g, b):
+    if (r, g, b) == (0, 0, 0):
+        # black
+        return 0, 0, 0, CMYK_SCALE
+
+    # rgb [0,255] -> cmy [0,1]
+    c = 1 - r / RGB_SCALE
+    m = 1 - g / RGB_SCALE
+    y = 1 - b / RGB_SCALE
+
+    # extract out k [0, 1]
+    min_cmy = min(c, m, y)
+    c = (c - min_cmy) / (1 - min_cmy)
+    m = (m - min_cmy) / (1 - min_cmy)
+    y = (y - min_cmy) / (1 - min_cmy)
+    k = min_cmy
+
+    # rescale to the range [0,CMYK_SCALE]
+    return c * CMYK_SCALE, m * CMYK_SCALE, y * CMYK_SCALE, k * CMYK_SCALE
+
+def filter_image(image, classifyer):
+    width, height = image.size
+    for x in range(width):
+        for y in range(height):
+            r,g,b = image.getpixel((x,y))
+            if classifyer(r,g,b):
+                image.putpixel((x,y), (255,0,0))
+    image.save('test.png')
+
+DELTA = 1
+
+def find_left(image,x,y,classifyer):
+    left = (x,y)                
+    loop = True
+    while loop:
+        nxt = left[0], left[1] - 1
+        rr,gg,bb = image.getpixel(nxt)
+        if not classifyer(rr,gg,bb):
+            nxt = left[0] - DELTA, left[1] -1
+            rr,gg,bb = image.getpixel(nxt)
+            if not classifyer(rr,gg,bb):
+                nxt = left[0] + DELTA, left[1] -1
+                rr,gg,bb = image.getpixel(nxt)
+                if not classifyer(rr,gg,bb):
+                    break
+        left = nxt
+    return left[1]
+
+def find_right(image,x,y,classifyer):
+    right = (x,y)                
+    loop = True
+    while loop:
+        nxt = right[0], right[1] + 1
+        rr,gg,bb = image.getpixel(nxt)
+        if not classifyer(rr,gg,bb):
+            nxt = right[0] - DELTA, right[1] + 1
+            rr,gg,bb = image.getpixel(nxt)
+            if not classifyer(rr,gg,bb):
+                nxt = right[0] + DELTA, right[1] + 1
+                rr,gg,bb = image.getpixel(nxt)
+                if not classifyer(rr,gg,bb):
+                    break
+        right = nxt
+    return right[1]
+
+def find_top(image, x, y, classifyer):
+    top = (x,y)                
+    loop = True
+    while loop:
+        nxt = top[0] - 1, top[1]
+        rr,gg,bb = image.getpixel(nxt)
+        if not classifyer(rr,gg,bb):
+            nxt = top[0] - 1, top[1] - DELTA
+            rr,gg,bb = image.getpixel(nxt)
+            if not classifyer(rr,gg,bb):
+                nxt = top[0] - 1, top[1] + DELTA
+                rr,gg,bb = image.getpixel(nxt)
+                if not classifyer(rr,gg,bb):
+                    break
+        top = nxt
+    return top[0]
+
+def find_bottom(image, x, y, classifyer):
+    bottom = (x,y)                
+    loop = True
+    while loop:
+        nxt = bottom[0] + 1, bottom[1]
+        rr,gg,bb = image.getpixel(nxt)
+        if not classifyer(rr,gg,bb):
+            nxt = bottom[0] + 1, bottom[1] - DELTA
+            rr,gg,bb = image.getpixel(nxt)
+            if not classifyer(rr,gg,bb):
+                nxt = bottom[0] + 1, bottom[1] + DELTA
+                rr,gg,bb = image.getpixel(nxt)
+                if not classifyer(rr,gg,bb):
+                    break
+        bottom = nxt
+    return bottom[0]
+
+def xy_to_bottom_right(x,y, objects):
+    "When x y is inside an object, return new xy for bottom right when search can be continued"
+    for topleft, bottomright in objects:
+        top, left = topleft
+        bottom, right = bottomright
+        if top < x and x < bottom and left < y and y < right:
+            return bottomright
+    return (x,y)
+
+def merge_objects(objects):
+    for pos1 in range(len(objects)-1, 0 , -1):
+        for pos2 in range(len(objects)-1, 0 , -1):
+            if pos1 == pos2:
+                continue
+            obj1 = objects[pos1]
+            obj2 = objects[pos2]
+
+            t1, l1, b1, r1 = obj1[0] + obj1[1]
+            t2, l2, b2, r2 = obj2[0] + obj2[1]
+
+            if ((t1 <= t2 and t1 >= b2) and (l1 >= l2 and l1 <= r2)) or ((b1 >= t2 and b1 <= b2) and (r1 >= l2 and r1 <= r2)):
+                t = min(t1,t2)
+                l =  min(l1,l2)
+                b = max(b1, b2)
+                r = max(r1, r2)
+                objects[pos2] = ((t,l),(b, r))
+                objects.remove(obj1)
+                break
+
+    for pos1 in range(len(objects)-1, 0 , -1):
+        obj1 = objects[pos1]
+        t1, l1, b1, r1 = obj1[0] + obj1[1]
+        if b1 - t1 + r1 - l1 < 20:
+            objects.remove(obj1)
+    return objects
+        
+
+def recognize(image, classifyer, classifyer_loose):
+    objects = []
+    width, height = image.size
+    print(f"Image: {width}x{height}")
+    x = 0
+    y = 0
+    while x < width:
+        x = xy_to_bottom_right(x,y, objects)[0]
+        while y < height:
+            y = xy_to_bottom_right(x,y, objects)[1]
+            #print(f"Scanning {x}/{y}")
+            r,g,b = image.getpixel((x,y))
+            if classifyer(r,g,b):
+                top = find_top(image, x, y, classifyer_loose)
+                left = find_left(image, x,y, classifyer_loose)
+                bottom = find_bottom(image, x,y, classifyer_loose)
+                right = find_right(image, x,y, classifyer_loose)
+                objects.append(((top, left), (bottom, right)))
+                print(f"Found: {x}/{y} --> {top},{left}  {bottom},{right}")
+            y += 4
+        y = 0
+        x += 4
+    
+    draw = ImageDraw.Draw(image)
+    objects = merge_objects(objects)
+    for topleft, bottomright in objects:
+        top, left = topleft
+        bottom, right = bottomright
+        draw.rectangle((top,left,bottom,right), outline="#ff0000")
+    image.save('test.png')
+    return objects
+
+im = Image.open("Scrns/image_7.png")
+cl_loose = lambda r,g,b: b > 80 and r < 130 and g < 130
+cl_strict = lambda r,g,b: b > 110 and r < 130 and g < 130
+#filter_image(im, cl_loose)
+recognize(im, cl_strict, cl_loose)
+exit(0)

test.py

@@ -0,0 +1,162 @@
+from pyautogui import *
+import pyautogui
+import time
+import keyboard
+import random
+import win32api, win32con
+import time
+from PIL import Image, ImageDraw
+
+WAIT4CLICK = 0.04
+# see https://github.com/KianBrose/Image-Recognition-Botting-Tutorial/blob/master/README.txt
+
+#def click(x,y):
+#    win32api.SetCursorPos((x,y))
+#    win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN,0,0)
+#    win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP,0,0)
+
+
+# Positionen
+# Resourcen Bereich 60px hoch, 850px breit
+
+# Spielerfarben:
+# b = 255 -> Blau
+# r = 0 & rest nicht 0 -> cyan
+
+def merge_objects(objects):
+    for pos1 in range(len(objects)-1, 0 , -1):
+        for pos2 in range(len(objects)-1, 0 , -1):
+            if pos1 == pos2:
+                continue
+
+            t1, l1, b1, r1 = objects[pos1]
+            t2, l2, b2, r2 = objects[pos2]
+
+            if ((t1 <= t2 and t1 >= b2) and (l1 >= l2 and l1 <= r2)) or ((b1 >= t2 and b1 <= b2) and (r1 >= l2 and r1 <= r2)):
+                t = min(t1,t2)
+                l = min(l1,l2)
+                b = max(b1, b2)
+                r = max(r1, r2)
+                objects[pos2] = (t,l,b,r)
+                objects.remove(objects[pos1])
+                break
+    return objects
+
+def image_files_in_folder(folder):
+    return [os.path.join(folder, f) for f in os.listdir(folder) if re.match(r'.*\.(jpg|jpeg|png)', f, flags=re.I)]
+
+
+def wait_for_start():
+    start_started = False
+    while 1:
+        if pyautogui.locateOnScreen('images/startscreen.png', confidence=0.9) != None:
+            print("Startscreen found")
+            start_started = True
+            time.sleep(0.5)
+        elif start_started:
+            print("Proceeding")
+            return
+        else:
+            print("Startscreen not found")
+            time.sleep(0.5)
+            
+
+def zoomout():
+    time.sleep(0.5)
+    print("Zooming out")
+    pyautogui.click(x=500, y=500)
+    time.sleep(WAIT4CLICK)
+    while keyboard.is_pressed('q') == False:
+        pyautogui.scroll(-10)
+        time.sleep(WAIT4CLICK)
+    print("Zooming out done")
+
+def find_peasants():
+    print("Looking for peasants")
+    all_matches = []
+    for img_path in image_files_in_folder(os.path.join("images", "peasants")):
+        matches = list(pyautogui.locateAllOnScreen(img_path, confidence=0.8))
+        if len(matches) > 0:
+            all_matches += matches
+            print(f"Für Bild {img_path} wurden {len(matches)} matches gefunden: {matches}")
+    return all_matches
+
+def order_peasants(number):
+    print(f"Trying to order {number} peasants.")
+    pyautogui.press('h')
+    time.sleep(WAIT4CLICK)
+    for i in range(number):
+        pyautogui.press('q')
+        time.sleep(WAIT4CLICK)
+
+def assign_hotkeys():
+    pyautogui.press(',')
+    time.sleep(WAIT4CLICK)
+    pyautogui.hotkey('ctrl', '1')
+    time.sleep(WAIT4CLICK)
+
+    pyautogui.press('.')
+    time.sleep(WAIT4CLICK)
+    pyautogui.hotkey('ctrl', '2')
+    time.sleep(WAIT4CLICK)
+
+    pyautogui.press('.')
+    time.sleep(WAIT4CLICK)
+    pyautogui.hotkey('ctrl', '3')
+    time.sleep(WAIT4CLICK)
+
+    pyautogui.press('.')
+    time.sleep(WAIT4CLICK)
+    pyautogui.hotkey('ctrl', '4')
+    time.sleep(WAIT4CLICK)
+
+def build_houses():
+    pyautogui.press('h')
+    time.sleep(WAIT4CLICK)
+    pyautogui.press('up')
+    time.sleep(0.5)
+    pyautogui.press('2')
+    time.sleep(WAIT4CLICK)
+
+    pyautogui.press('q')
+    time.sleep(WAIT4CLICK)
+    pyautogui.press('q')
+    time.sleep(WAIT4CLICK)
+    pyautogui.click(650, 90)
+    time.sleep(WAIT4CLICK)
+
+    pyautogui.press('3')
+    time.sleep(WAIT4CLICK)
+    pyautogui.rightClick(650, 90)
+    time.sleep(WAIT4CLICK)
+
+    pyautogui.press('4')
+    time.sleep(WAIT4CLICK)
+
+    pyautogui.press('q')
+    time.sleep(WAIT4CLICK)
+    pyautogui.press('q')
+    time.sleep(WAIT4CLICK)
+    pyautogui.click(300, 200)
+    time.sleep(WAIT4CLICK)  
+
+wait_for_start()
+#zoomout() FUUU
+order_peasants(4)
+assign_hotkeys()
+build_houses()
+pyautogui.press('1')
+pyautogui.press('1')
+
+
+cnt = 0
+while False: #keyboard.is_pressed('q') == False:
+    time.sleep(1)
+    peasant_locations = merge_objects(find_peasants())
+    pic = pyautogui.screenshot()
+    draw = ImageDraw.Draw(pic)
+    
+    for x,y,a,b in peasant_locations:
+        draw.rectangle((x,y,x+a,y+b), outline="#ff0000")
+    pic.save(f"image_{cnt}.png")
+    cnt += 1