python-3.x - 检测到的颜色不等于颜色图像

Question

我试图通过 x 和 y 知道哪个是像素的颜色。颜色来自这张图片。

用 Photoshop 捕捉颜色我有这个颜色列表：

"#5D385A", "#6D3B47", "#6F5C4B", "#50717A", "#547057", "#4C6180", "#717080", "#705574", "#726B59", "#5E4854", "#415A4B", "#425A64", "#3A4E6F"

但是，当我尝试从图像中获取像素的颜色时，该颜色与之前的列表不匹配。而且，我有 95 种不同的颜色，而图像中只有 13 种不同的颜色。

我打开图像并使用此类从像素中获取颜色：

import PIL.Image
    
class Image:
    def __init__(self, file):
        self.image = PIL.Image.open(file).convert("RGB")

    def get_color(self, x, y):
        color = self.image.getpixel((x,y))
        color = ("#%02x%02x%02x" % color).upper()
        return color

这是我取颜色的位置的 x 和 y 的简短列表：

144, 74
140, 46
150, 53
85, 87
160, 48
147, 60
137, 49
149, 53
148, 60
143, 52
161, 30
166, 23
134, 38
146, 29
155, 40
129, 37
154, 66
153, 38
151, 33
128, 36

这怎么可能？当只有 13 种不同颜色时，如何从图像中获得 95 种不同颜色？

编辑我：

我已经从图像中的每个像素中获得了所有颜色，没有人拥有我使用 Photoshop 获得的颜色。

我有 256 种不同的颜色，这是找到它的列表和次数。

{'#885F7D': 15, '#541B47': 15, '#68355B': 819, '#65355D': 17, '#78384A': 19, '#7E3942': 19, '#7B3846': 4588, '#7C3346': 39, '#7D3046': 50, '#773F4C': 21, '#785A49': 4, '#775F49': 35, '#765C49': 17540, '#7A4648': 21, '#756349': 62, '#785B49': 56, '#7C3546': 14, '#765D49': 12, '#7A4F48': 14, '#7C3746': 29, '#785549': 7, '#775D4A': 8, '#785749': 8, '#551743': 1, '#6A3158': 39, '#68325A': 6, '#86617E': 1, '#66385D': 31, '#6C2C56': 6, '#6C2A56': 7, '#6D2B54': 3, '#678D97': 88, '#2C5B6A': 60, '#416C79': 43, '#3F717A': 7, '#43686A': 64, '#5C5F71': 32, '#465771': 3, '#5E5666': 14, '#5D4C66': 7, '#644160': 2, '#683C5F': 2, '#659197': 2, '#1C606C': 88, '#32767E': 61, '#227B84': 59, '#3A757A': 60, '#803342': 16, '#7D3745': 6, '#3A727B': 7374, '#3B7479': 3, '#36747C': 11, '#6C4450': 104, '#82303F': 18, '#852B3B': 28, '#694A56': 3, '#3D7179': 15, '#694E59': 15, '#7D3545': 11, '#387283': 30, '#3B717B': 17, '#3A727D': 16, '#7B5A48': 37, '#832B43': 11, '#3B7184': 21, '#2A7C66': 1, '#5D5D4E': 2, '#3B7180': 23, '#41715A': 6, '#45714D': 44, '#297D59': 6, '#407256': 32, '#417160': 13, '#437155': 5275, '#467055': 16, '#327A58': 7, '#68514E': 4, '#407756': 2, '#3C7356': 22, '#56654F': 17, '#437154': 15, '#387457': 30, '#3F7169': 14, '#4B6D54': 9, '#805C49': 105, '#735E4A': 10, '#7F5747': 63, '#755C49': 9, '#457154': 16, '#337558': 45, '#536B52': 18, '#735944': 95, '#7B614F': 96, '#5D6750': 36, '#437156': 43, '#69624D': 21, '#457151': 29, '#3D7172': 10, '#70604B': 10, '#487458': 2, '#45744D': 96, '#447352': 2, '#23596C': 2, '#3C6A7E': 59, '#3F696B': 41, '#64819B': 37, '#204D73': 92, '#3C5E82': 60, '#3A5E8A': 93, '#385B92': 1, '#3C6182': 4212, '#5D7F9A': 1, '#0C4A72': 2, '#305E82': 118, '#5C6982': 118, '#8F8D9B': 26, '#646473': 3, '#7B7482': 118, '#5A7169': 14, '#39714D': 12, '#727182': 2691, '#797189': 13, '#3E724D': 1, '#3B7155': 51, '#885947': 1, '#7D5744': 1, '#866251': 1, '#4F7056': 51, '#675C48': 106, '#707289': 10, '#736E6C': 11, '#746B51': 12, '#756C58': 116, '#82705C': 27, '#135941': 27, '#235D44': 105, '#255B44': 24, '#1D5943': 45, '#2B5C46': 108, '#2B5C45': 8, '#746C58': 5469, '#2E5C46': 17561, '#7E705B': 32, '#4F634D': 10, '#7B6E5A': 32, '#45614B': 14, '#707584': 117, '#6E788C': 1, '#72716E': 1, '#75677E': 117, '#746684': 1, '#766D59': 26, '#3D5F49': 11, '#255943': 33, '#957890': 39, '#7A5174': 117, '#7C4B7C': 2, '#775E6A': 62, '#727152': 39, '#726C58': 32, '#365E47': 12, '#683F63': 37, '#7A5476': 4212, '#79507A': 37, '#766166': 38, '#7A6D57': 15, '#6E6B56': 13, '#2D5D46': 5, '#696A54': 4, '#2C5B45': 8, '#626852': 8, '#305C46': 24, '#2E5C44': 26, '#7E577B': 2, '#7C567A': 55, '#7A517A': 58, '#784F79': 1, '#5F3855': 1, '#724F68': 57, '#727053': 59, '#856C77': 89, '#51303E': 91, '#62444F': 56, '#60404E': 1, '#767558': 56, '#654654': 7521, '#623F53': 16, '#674B54': 7, '#747057': 25, '#746B58': 40, '#623E53': 15, '#654754': 40, '#757158': 11, '#6F6C56': 2, '#644554': 29, '#613D53': 16, '#6B5555': 15, '#6F5E56': 15, '#756D57': 11, '#634354': 7, '#634153': 13, '#716457': 7, '#644254': 7, '#654354': 4, '#305C48': 3, '#726C59': 2, '#7E7055': 6, '#817155': 7, '#48615F': 4, '#0A5649': 1, '#2E5C3E': 26, '#135669': 2, '#2C5B68': 34, '#2B5C53': 21, '#2E5C41': 58, '#415F60': 3, '#0F5667': 5, '#2C5B64': 4676, '#2C5B66': 19, '#2C5B5B': 17, '#2E5C4D': 8, '#175966': 7, '#375D61': 2, '#61675B': 1, '#2F5B64': 20, '#2C5B60': 16, '#2F5B4A': 3, '#55675E': 2, '#2E5C4A': 8, '#275C64': 23, '#674654': 10, '#385260': 1, '#684553': 26, '#1C5E66': 46, '#564D59': 5, '#3D5660': 8, '#4F4F5B': 10, '#5E4A57': 7, '#365961': 5, '#47525D': 8, '#5C4B57': 4, '#614756': 2, '#5A4759': 36, '#504A60': 10, '#404B67': 7, '#2C5667': 18, '#8B6B75': 1, '#2B4D71': 876, '#2D5D62': 18, '#7C6D7B': 1, '#58728D': 16, '#0A365F': 16, '#21553E': 4, '#335F4B': 1, '#35624D': 20, '#3D6752': 4}

我什么都不懂。怎么可能没有一个像素具有我在 Photoshop 中获得的颜色？

编辑二：

使用相同的代码，我得到了另一个图像的颜色图。这是图像：

您可以在此图像中看到的主要颜色是：

"#F50A22", "#00EC83", "#00A200", "#0007A4", "#9D132B", "#734500", "#6230FF", "#F42AFF", "#BEFF00", "#EC7800", "#65DCD1", "#FF6D00" : "#004500"

执行测试，我怎么说，相同的代码。我知道所有这些颜色都可以在图像中找到！在第一张图片中没有人如何。

结果是：

Colors matched: {'#F50A22': 2245, '#00EC83': 9437, '#00A200': 21039, '#0007A4': 8772, '#9D132B': 99, '#734500': 2970, '#6230FF': 112, '#F42AFF': 5271, '#BEFF00': 2380, '#EC7800': 3076, '#65DCD1': 6503, '#FF6D00': 4709, '#004500': 6612}
 colors matched: 13

在图像中发现的其他颜色是：

Other colors: {'#FFFFFF': 1931, '#FCFFFD': 27, '#FAFFFB': 2, '#F7FEF9': 12, '#F4FEF7': 10, '#F6FEF8': 20, '#F6FDF8': 1, '#F9FEFA': 12, '#FBFEFC': 9, '#FEFFFE': 40, '#FAFEFB': 12, '#FBFFFC': 7, '#F3FEF6': 7, '#F4FDF6': 2, '#F5FDF7': 1, '#F2FDF5': 3, '#EEFDF2': 3, '#F2FDF6': 7, '#F4FEF8': 12, '#EFFDF4': 3, '#E5FCEC': 4, '#DAFAE5': 1, '#D3FAE0': 3, '#D4FAE0': 1, '#DAFAE4': 1, '#DFFBE8': 1, '#E9FCEF': 3, '#EDFDF2': 2, '#EFFDF3': 3, '#E2FBEA': 3, '#E2FCEA': 3, '#EFFEF3': 1, '#F2FEF5': 1, '#EDFCF1': 2, '#EBFDF0': 1, '#F1FDF4': 1, '#F3FEF7': 4, '#EDFDF1': 2, '#E7FCEE': 3, '#E3FCEB': 1, '#E0FCE9': 1, '#DCFBE6': 5, '#DAFBE5': 1, '#D9FAE4': 1, '#D9FAE3': 1, '#E3FCEC': 1, '#EEFDF3': 1, '#D7FAE2': 1, '#D1FADF': 1, '#D1FADE': 1, '#D6FAE2': 1, '#E1FBEA': 2, '#EBFDF1': 1, '#DFFBE9': 1, '#DEFBE7': 2, '#DBFBE5': 1, '#F6132A': 111, '#00EC84': 33, '#00EC85': 16, '#04EC86': 11, '#14EC87': 3, '#F40D23': 3, '#F20E24': 1, '#F50B22': 8, '#F11426': 2, '#F40C23': 1, '#EF1A28': 1, '#EE1B29': 1, '#F01827': 1, '#F21125': 1, '#F40D24': 1, '#F40E24': 1, '#774A03': 165, '#F40E23': 1, '#F50C22': 1, '#F6142A': 3, '#00EC82': 1, '#00EB82': 2, '#00EA7F': 1, '#00EB81': 1, '#6FE09C': 1, '#7E5416': 2, '#00A300': 78, '#00A500': 43, '#D9403B': 1, '#00AB16': 1, '#00A600': 40, '#00A700': 1123, '#5E2AFF': 2471, '#00B213': 2, '#00AA00': 6, '#7A4F0D': 3, '#6636FF': 2, '#00AE02': 2, '#00AC00': 3, '#00AB08': 2, '#00A800': 12, '#00A900': 8, '#00B317': 1, '#6C3CFF': 1, '#00AE00': 2, '#00AE14': 1, '#00A903': 1, '#7F55FE': 1, '#6CEE9F': 1, '#00AD00': 2, '#6CDCD2': 268, '#6A3CFE': 2, '#7549FF': 1, '#4ED688': 1, '#6B3DFF': 1, '#5E2BFF': 24, '#6839FD': 1, '#6231FE': 1, '#5E31FC': 2, '#00AF08': 1, '#00AC07': 1, '#6339FA': 1, '#5F33FB': 3, '#5F30FD': 3, '#00B10E': 1, '#656565': 1, '#00AB00': 2, '#00B02D': 2, '#6037F9': 1, '#5F2EFE': 2, '#5F3EF5': 1, '#5F32FC': 1, '#6040F4': 1, '#5F32FB': 2, '#6041F3': 1, '#6042F2': 1, '#7145FC': 1, '#5F2CFF': 10, '#6147EF': 1, '#6454EA': 1, '#6036F9': 1, '#685AEA': 1, '#00AF2F': 1, '#6B57EE': 1, '#00B110': 1, '#00AA02': 1, '#8ADBD3': 3, '#683CFB': 1, '#72DDD2': 3, '#6D47F8': 1, '#775EF3': 1, '#9CD7D1': 2, '#5E31FD': 1, '#00AB18': 1, '#82DCD3': 1, '#673EFB': 1, '#7450F9': 1, '#612EFF': 8, '#6236FB': 1, '#602CFF': 5, '#6B49F7': 1, '#602DFF': 7, '#5F2BFF': 6, '#6334FD': 1, '#2EEB8B': 1, '#704AFB': 1, '#6231FF': 1, '#6738FE': 1, '#612DFF': 3, '#3FEB8F': 1, '#66DBD1': 5, '#67D8D2': 1, '#00AE2B': 1, '#65DAD2': 1, '#F42DFF': 15, '#FC67FF': 6, '#F246FA': 1, '#F84CFF': 7, '#6233FF': 1, '#6ADCD2': 22, '#6132FE': 1, '#FBFEFE': 2, '#F434FF': 5, '#F8FDFC': 1, '#68DCD1': 33, '#6034FE': 1, '#FB5DFF': 2, '#FAFEFD': 2, '#F2FBFA': 1, '#6442FA': 1, '#6031FF': 1, '#F539FF': 7, '#F5FCFC': 1, '#E7F9F6': 1, '#F02AFF': 5, '#EFFBF9': 2, '#DDF6F3': 1, '#5F2EFF': 1, '#DD2BFF': 1, '#E82AFF': 1, '#F32AFF': 8, '#F744FF': 3, '#E7F9F7': 1, '#CFF2EF': 1, '#6136FD': 1, '#5F2AFF': 1, '#DD2AFF': 1, '#E42AFF': 1, '#EC2AFF': 2, '#E1F7F4': 1, '#C3EFEA': 1, '#6031FE': 1, '#EA2AFF': 2, '#ED2AFF': 1, '#DAF6F2': 1, '#BAEEE7': 1, '#6DDDD3': 2, '#6937FF': 1, '#ED37FE': 1, '#D7F5F1': 2, '#B6EDE6': 1, '#69DDD2': 2, '#74DFD4': 1, '#81DED9': 1, '#EF2BFF': 1, '#B3ECE7': 1, '#7ED7D4': 1, '#F22AFF': 2, '#D9F5F2': 1, '#B7EDE7': 1, '#DB39FC': 1, '#F12EFF': 1, '#E0F7F4': 1, '#C2EFEA': 1, '#87DBD3': 1, '#E737FE': 1, '#E6F8F6': 2, '#CCF2EE': 1, '#84DCD3': 1, '#ECFAF9': 1, '#D8F5F2': 1, '#65DCD0': 5, '#69DBCF': 6, '#6ADCD1': 1, '#98D3CD': 1, '#F440FC': 1, '#F42CFF': 7, '#F4FCFB': 1, '#6FD9CC': 2, '#6FD9CB': 3, '#6BDBCF': 1, '#7ED7C8': 1, '#80D3C1': 1, '#F531FF': 3, '#F42BFF': 35, '#FDFEFE': 2, '#F8FDFD': 1, '#83D8CB': 1, '#7ED3C2': 1, '#FF7100': 78, '#FEFFFF': 1, '#97D2CC': 1, '#FF7000': 40, '#FF6E00': 40, '#FF7925': 1, '#F33FF7': 1, '#6FDDD2': 2, '#FF6B00': 8, '#F62DF4': 1, '#F52BFB': 1, '#FF7409': 1, '#F62DF3': 1, '#F52BFC': 3, '#A2CFCA': 1, '#F73FE3': 1, '#F52DF9': 1, '#F42AFE': 1, '#FF7400': 4, '#FF730E': 1, '#FC36D5': 1, '#F62DF1': 1, '#F52BFD': 1, '#F52CFF': 6, '#F52DFF': 13, '#76DDD3': 2, '#FF6C00': 8, '#F831EA': 1, '#F52BFA': 3, '#F632FF': 1, '#8DDAD2': 1, '#F836E6': 1, '#F52BF9': 2, '#A4CCC8': 1, '#FF6A08': 1, '#7ADDD3': 1, '#FF690B': 1, '#F42BFE': 1, '#92D9D2': 2, '#FF6E0B': 1, '#F031FA': 1, '#A7C8C5': 1, '#FF6429': 1, '#FF7200': 62, '#FF671A': 1, '#7EDCD3': 1, '#EC35F6': 1, '#6CDACE': 1, '#6DDBD0': 1, '#FF671C': 1, '#FF7104': 1, '#FF6911': 1, '#FF642C': 1, '#FF6B23': 1, '#FF6E13': 1, '#FF7300': 5, '#F530FF': 1, '#F532FF': 3, '#6DDDD2': 1, '#F533FF': 1, '#F635FF': 1, '#F537FF': 8, '#F539FE': 2, '#F538FF': 9, '#00AC1A': 4, '#FF780E': 1, '#004B04': 29, '#FF873C': 1, '#FF7C1B': 1, '#FF7606': 4, '#FF780C': 1, '#FF7502': 1, '#FF7504': 1, '#FF770A': 2, '#004A03': 9, '#004A02': 5, '#F73FFF': 2, '#F435FF': 1, '#004700': 9, '#FF7A0F': 1, '#F52EFF': 1, '#F63BFF': 1, '#F638FF': 1, '#004600': 22, '#004B03': 3, '#004901': 8, '#FF7D1D': 1, '#F43EFB': 1, '#FF8533': 1, '#F62DF6': 1, '#FF7F24': 1, '#004902': 3, '#004900': 1, '#F441FC': 1, '#C1E057': 1, '#C2FD00': 5, '#C1F700': 1, '#C0FE00': 176, '#C4EE30': 2, '#C3E846': 1, '#C2FB00': 2, '#FEFEFE': 9, '#004C07': 11, '#B8FB00': 2, '#C3FB00': 1, '#FDFEFD': 5, '#BAFB00': 2, '#C5F11A': 2, '#B3F600': 2, '#BEFC00': 1, '#C1FD00': 7, '#FBFCFB': 3, '#BCDE52': 1, '#BBFE00': 9, '#FAFBFA': 2, '#B6F700': 1, '#BDFB00': 1, '#C3F800': 5, '#F331FF': 1, '#B2F500': 1, '#BDF900': 1, '#BDFD00': 1, '#BBFC00': 2, '#BDFE00': 10, '#C3EA40': 1, '#FCFDFC': 4, '#B5F600': 2, '#BCFD00': 7, '#C4E847': 1, '#CDFD09': 3, '#2337B3': 1, '#4251B6': 1, '#C5ED37': 1, '#D5FF3E': 17, '#0012A7': 625, '#004B06': 1, '#CFFE22': 5, '#B6F900': 2, '#C5FD00': 5, '#D3FF3C': 3, '#005010': 1, '#CBFD00': 5, '#C2FE00': 3, '#B8F900': 2, '#D2FE31': 8, '#C8FD00': 3, '#B9FA00': 2, '#C4FD00': 3, '#F8FBF9': 2, '#CCFE08': 3, '#F4F6F4': 2, '#C7FD00': 5, '#EBF1EC': 1, '#F8F9F7': 1, '#E1EAE4': 1, '#004701': 1, '#132AAF': 2, '#D5E2D8': 1, '#F1F5F2': 2, '#D1DFD5': 1, '#EC8417': 1, '#D4E1D7': 1, '#F3F5F3': 1, '#D9E4DC': 1, '#EB7800': 15, '#ED7B00': 133, '#F6F8F5': 1, '#DEE8E0': 1, '#E6EDE6': 1, '#FAFDFB': 1, '#EAF0EB': 1, '#EEF3EF': 1, '#EA7700': 2, '#F5F7F5': 1, '#C2E64E': 1, '#CAFD00': 2, '#F7FAF8': 1, '#E87700': 2, '#EA7800': 7, '#004C06': 2, '#CFFE20': 2, '#004A05': 1, '#E37600': 1, '#E67700': 1, '#00591D': 1, '#990A22': 2077, '#A6293C': 1, '#021EAA': 1, '#0007A3': 6, '#0009A1': 2, '#001697': 2, '#000B9F': 2, '#00119B': 1})
total other colors: 448

两张图片都是png的。

我怎么可能在第二张图片中找到了所有颜色，却没有找到在第一张图片中搜索到的任何颜色？

标签： python-3.xpython-imaging-library

Answer 1

you can see 13 colors yes! but the code doesn't because it's more precise than your eyes.

try zooming into the picture more, you'll see that between the colors there is another lighter one, which can consist of more than one color to go from one to the other, also I noticed some black and white at the left side "maybe it's just from your snipping tool or something"

but what I'm saying is, the code is right :)

you can try and create a photo using paint and only two colors with the fill tool, and make sure it's only one color without any gradient.