How should I structure this code to break out of multiple nested loops at once?-CodePudding

I'm learning image detection and python writing a bot for a 2d game. There are certain targets we want to attack and certain targets we want to avoid. One target is displayed on the screen at a time out of a pool of 100 and you click next or attack depending on what you want to do. In doing this you're able to cycle through targets until you find one you want to attack. My code currently checks to see if it finds one of 24 different targets we want to avoid, if it does it avoids that target. Otherwise, it attacks that target.

At the moment my code:

1. Loads 24 different images of things we want to avoid into a NumPy array and checks with `py.locateCenterOnScreen` 
   if we are on the correct screen to start the video loop
2. Uses cv2.imshow to display a real-time view of what is happening for use later with cv2.rectangles
3. Continuously loops through the NumPy array to determine if any of the loaded images have been found
4. If anything has been found it will either print "avoid" or "attack" to the terminal.

There's also additional logging that I've not added to the below code as it isn't relevant. It simply logs the attack/avoid to a .txt file with time stamps.

My issue:

At the moment my object detection loop for i in image_list: never stops when it finds one of the 24 images, it loops over them again and again swapping away from the found image.

My question:

When one of the 24 images is detected, how do I stop my detection loop without stopping my cv2.imshow loop. For example, if image 5 is found. I want to keep image 5 highlighted with cv2.rectangle rather than only having it highlight for a fraction of a second when it comes up in the detection loop.

# Load Images 
def loadImages(directory):
    # Intialise empty array
    image_list = []
    # Add images to array
    for i in directory:
        img = cv.imread(i, cv.IMREAD_REDUCED_GRAYSCALE_2)
        image_list.append((img, i))
    return image_list

def objectDetection(image_list, threshold_value):
    # Video Loop
    while True:
        # Listener to check if attack! button displaying
        if py.locateCenterOnScreen('!attack.jpg', region = (1282, 931, 1585-1282, 1032-931), grayscale=True) != None:
            # Set attack_loop to false as haven't found anything yet, only set to true when something has been attacked
            attack_loop = False
            while attack_loop is False:
                # Grab PSS Window and find size 
                window = pygetwindow.getWindowsWithTitle('App')[0]
                x1 = window.left
                y1 = window.top
                height = window.height
                width = window.width
                x2 = x1   width
                y2 = y1   height
                # Actual Video Loop, cropped down to the specific window,
                # resized to 1/2 size, and converted to BGR for OpenCV
                haystack_img = ImageGrab.grab(bbox=(x1, y1, x2, y2))
                (width, height) = (haystack_img.width // 2, haystack_img.height // 2)
                haystack_img_resized = haystack_img.resize((width, height))
                crop = haystack_img_resized.crop((360,22,600,65))
                haystack_img_np = np.array(crop)
                haystack = cv.cvtColor(haystack_img_np, cv.COLOR_BGR2GRAY)
                cv.imshow("Screen", haystack)

                # Object Detection
                for i in image_list:
                    needle_img = i[0]
                    needle_name = i[1]
                    result = cv.matchTemplate(haystack, needle_img, cv.TM_CCORR_NORMED)
            
                    # Get the best match position
                    min_val, max_val, min_loc, max_loc = cv.minMaxLoc(result)

                    # Define top left and bottom right and threshold
                    (H, W) = i[0].shape[:2] 
                    top_left = max_loc
                    bottom_right = (top_left[0]   W, top_left[1]   H)
                    threshold = threshold_value
                    
                    # If something has been detected click keep looking code
                    if max_val >= threshold:
                        cv.rectangle(haystack, top_left, bottom_right, 255, 2)
                        print("avoid")
                    else:
                        print("attack")
                        if max_val >= threshold:print(max_val)
    
       
### Main Script ###

# load images to detect 
ships_to_avoid = loadImages(defeat)
# Run actual image detection
objectDetection(ships_to_avoid, 0.92)

CodePudding user response：

If you want to break an inner loop breaking also the outer loop I would suggest an approach like this:

for a in xrange(10):
    for b in xrange(20):
        if <condition>:
            # Break the inner loop...
            break
    else: # The loop ended without any break
        # Continue if the inner loop wasn't broken.
        continue
    # Inner loop was broken, break the outer.
    break

CodePudding user response：

You can simply stop the inner loop with the python word break inside your condition, so it will stop whenever it is True. This break will not affect the outer loop. For more info about stopping nested loops I would recommend you this article: 5 Ways To Break Out of Nested Loops in Python

CodePudding user response：

The full working code.

import pyautogui as py
import glob
import cv2 as cv
import os
import numpy as np
import pygetwindow
import time
from PIL import ImageGrab

os.chdir(r'C:\test')
avoid = glob.glob(r"C:\test\*.png")

# Load Images 
def loadImages(directory):
    # Intialise empty array
    image_list = []
    # Add images to array
    for i in directory:
        img = cv.imread(i, cv.IMREAD_REDUCED_GRAYSCALE_2)
        image_list.append((img, i))
    return image_list

def videoLoop():
    # Grab Window and find size 
    window = pygetwindow.getWindowsWithTitle('App')[0]
    x1 = window.left
    y1 = window.top
    height = window.height
    width = window.width
    x2 = x1   width
    y2 = y1   height
    # Actual Video Loop, cropped down to the specific window,
    # resized to 1/2 size, and converted to BGR for OpenCV
    haystack_img = ImageGrab.grab(bbox=(x1, y1, x2, y2))
    (width, height) = (haystack_img.width // 2, haystack_img.height // 2)
    haystack_img_resized = haystack_img.resize((width, height))
    crop = haystack_img_resized.crop((360,22,600,65))
    haystack_img_np = np.array(crop)
    haystack = cv.cvtColor(haystack_img_np, cv.COLOR_BGR2GRAY)
    cv.imshow("Screen", haystack)
    return haystack


def objectDetection(image_list, threshold, haystack, a):
    # Object Detection
    for x in range (0, a):
        for i in image_list:
            needle_img = i[0]
            needle_name = i[1]
            sliced_name = needle_name.split("\\")[-1]
            result = cv.matchTemplate(haystack, needle_img, cv.TM_CCORR_NORMED)

            # Get the best match position
            min_val, max_val, min_loc, max_loc = cv.minMaxLoc(result)

            # Define top left and bottom right and threshold
            (H, W) = i[0].shape[:2] 
            top_left = max_loc
            bottom_right = (top_left[0]   W, top_left[1]   H)
            
            # If something has been detected click keep looking code
            if max_val >= threshold:
                cv.rectangle(haystack, top_left, bottom_right, 255, 2)
                cv.imshow("Screen", haystack)
                keep_looking()

            if cv.waitKey(1) == ord('q'):
                cv.destroyAllWindows()
                break             


def keep_looking():
    py.moveTo(1715,1000,0.1)
    py.sleep(0.01)
    py.click()    


# load images to detect 
ships_to_avoid = loadImages(avoid)

while True:
    window = videoLoop()
    objectDetection(ships_to_avoid, 0.92, window, 10)