我用一个小窗口来检测马里奥,它由一个红色的方块表示。但是,该红色块由16×12像素组成。我想获取我找到的像素坐标,并根据图像中显示的窗口将其转换为正常的x/y坐标系:Actual frame,它应该是13×16网格(不是像素)。你知道吗
例如,如果马里奥盒子在屏幕的左上角,坐标应该是0,0。你知道吗
我也不知道如何制作网格。你知道吗
我使用的代码如下:
import numpy as np
from PIL import Image
class MarioPixels:
def __init__(self):
self.mario = np.array([
[[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0],
[248, 56, 0]
]]
)
self.height = len(self.mario) # specify number of pixels for columns in the frame
self.width = len(self.mario[0]) # specificy number of pixels representing a line in the frame
print(self.mario.shape)
# find difference in R, G and B values between what's in window and what's on the frame
def pixelDiff(self, p1, p2):
return abs(p1[0] - p2[0]), abs(p1[1] - p2[1]), abs(p1[2] - p2[2])
def isMario(self, window, pattern):
total = [0, 0, 0]
count = 0
for line in range(len(pattern)):
lineItem = pattern[line]
sample = window[line]
for pixelIdx in range(len(lineItem)):
count += 1
pixel1 = lineItem[pixelIdx]
pixel2 = sample[pixelIdx]
d1, d2, d3 = self.pixelDiff(pixel1, pixel2)
# print(pixelIdx)
total[0] = total[0] + d1 # sum of difference between all R values found between window and frame
total[1] = total[1] + d2 # sum of difference between all G values found between window and frame
total[2] = total[2] + d3 # sum of difference between all B values found between window and frame
# Mario has a red hat
# if line == 0 and pixelIdx == 4 and pixel2[0] != 248:
# return 1.0
rscore = total[0] / (
count * 255) # divided by count of all possible places the R difference could be calculated
gscore = total[1] / (
count * 255) # divided by count of all possible places the G difference could be calculated
bscore = total[2] / (
count * 255) # divided by count of all possible places the B difference could be calculated
return (
rscore + gscore + bscore) / 3.0 # averaged to find a value between 0 and 1. Num close to 0 means object(mario, pipe, etc.) is there,
# whereas, number close to 1 means object was not found.
def searchForMario(self, step, state, pattern):
height = self.height
width = self.width
x1 = 0
y1 = 0
x2 = width
y2 = height
imageIdx = 0
bestScore = 1.1
bestImage = None
bestx1, bestx2, besty1, besty2 = 0, 0, 0, 0
for y1 in range(0, 240 - height, 8): # steps in range row, jump by 8 rows
y2 = y1 + height
for x1 in range(0, 256 - width, 3): # jump by 3 columns
x2 = x1 + width
window = state[y1:y2, x1:x2, :]
score = self.isMario(window, pattern)
# print(imageIdx, score)
if score < bestScore:
bestScore = score
bestImageIdx = imageIdx
bestImage = Image.fromarray(window)
bestx1, bestx2, besty1, besty2 = x1, x2, y1, y2
imageIdx += 1
bestImage.save('testrgb' + str(step) + '_' + str(bestImageIdx) + '_' + str(bestScore) + '.png')
return bestx1, bestx2, besty1, besty2
它看起来像是有一个像素宽高比,所以每个像素块的宽度和高度是不同的。你知道吗
按照你的代码,你的像素空间是256x240像素,但你说它实际上代表一个13x16的网格。这意味着x域中的每个块是(256/13)或大约20像素,而y域中的每个块是(240/16)15像素。这意味着16x12像素的“Mario”所占的块还不到一个完整的块。看看你的形象,这似乎是一种可能性-灌木和云也占据不到一个街区。你知道吗
我建议您首先确保13x16网格是正确的(只是因为它似乎与您的像素大小不完全匹配,并且因为您范围内的跨距大小意味着块实际上可能是3x8像素)。然后,您可以尝试将网格添加到像素图像,只需将x坐标可被20整除的每个像素的值设置为黑色RGB像素的(0,0,0)值(以及y坐标可被15整除的值-使用模运算符%)。要得到“块”坐标,只需将x坐标除以20,y坐标除以15,然后四舍五入到最接近的整数(或使用//作为除法的一部分进行四舍五入)。你知道吗
我假设你的像素坐标也从左上角(0,0)到右下角(256,240)。你知道吗
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