plt.imshow() returns TypeError when run with certain images, but not with others
Question:
I’m writing an image optimisation algorythm (I know it’s horribly inefficient, it’s just my 1st attempt), and here’s what I currently have. The directory of the project looks like this:
image_encoding.py takes an image and encodes it using the following logic:
if the pixel is new, it encodes its RGB value
else, it encodes the index of a pixel with the same color
If you need more insight, here’s my code for image_encoding.py(it writes the encoded image into image_code.txt):
# imports
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# reading the image and displaying it
image = mpimg.imread('./100x150.jpg')
size = [image.shape[0], image.shape[1]]
print(f'size of the image: {size}')
plt.imshow(image)
plt.show()
# flattening the image for further processing
new_image = []
for imgline in image:
for pixel in imgline:
new_image.append(list(pixel))
image = new_image
# encodind the image
print('started encoding')
image_coding = []
# for a simple 2x2 image of a black square, image_coding will look like this:
# [ [ 255, 255, 255 ], 0, 0, 0 ]
i = 0
for pixel in image:
i += 1
print(f'{str(i / len(image) * 100)[:5]}% done', end='r')
if pixel not in image_coding:
image_coding.append(pixel)
else:
image_coding.append(image_coding.index(pixel))
# write the image coding into a file
print('writing image coding into image_code.txt')
f = open("image_code.txt", "w")
f.write(str(image_coding))
f.close()
f = open("image_code.txt", "a")
f.write(f'n{size}')
f.close()
print('image successfully encoded into image_code.txt')
Then, the image_code.txt file will look like this:
[[67, 58, 41], [68, 59, 42], [70, 61, 44], [71, 62, 45], [72, 63, 46], 4, 4, 3, [75, 66, 49], 8, 8, 8, [73, 64, 47], 3, 2, [67, 60, 41], [66, 59, 40], [73, 68, 48], [82, 76, 60], [95, 89, 77], [113, 106, 100], [131, 123, 121], [137, 128, 133], [134, 124, 132], [125, 115, 126], [118, 108, 119], [105, 95, 104], [88, 78, 86], [77, 68, 73], [76, 67, 68], [85, 76, 77], [93, 88, 85], [86, 82, 79], [83, 82, 77], [77, 74, 69], [66, 63, 54], [61, 59, 47], [64, 62, 47], [66, 63, 48], [65, 62, 45], [78, 73, 54], [75, 70, 51], 41, [72, 67, 48], [70, 62, 49], [78, 70, 57], [82, 74, 63], [73, 65, 52], [76, 68, 55], [77, 70, 54], [77, 69, 56], 48, 48, [80, 72, 59], [86, 78, 67], [92, 84, 73], [89, 80, 71], [86, 77, 68], [81, 72, 63], [76, 67, 58], [71, 62, 55], [69, 60, 53], [67, 58, 51], [67, 59, 48], [68, 56, 42], [67, 56, 38], [65, 54, 36], [64, 53, 35], [66, 55, 37], [70, 59, 41], [75, 64, 46], [78, 67, 49], [79, 68, 50], 71, 71, [76, 65, 47], 70, [74, 63, 45], [73, 62, 44], 78, 3, 4, 12, 12, 2, 1, [66, 57, 40], [65, 56, 39], [62, 53, 36], [64, 55, 38], 89, 88, [59, 50, 33], [58, 49, 32], 93, 93, [57, 50, 31], 96, 96, [56, 49, 30], 1, [69, 60, 43], 3, 12, [74, 65, 48], 104, 104, 12, 104, 104, 104, 104, 12, 3, 101, [67, 60, 42], 16, [73, 68, 49], [83, 77, 63], [96, 89, 79], [115, 107, 104], [134, 125, 126], [143, 133, 141], [142, 132, 141], [140, 129, 143], [134, 123, 137], [121, 111, 122], [103, 93, 102], [90, 80, 88], [87, 78, 81], [93, 84, 87], [99, 93, 93], [95, 91, 90], [93, 92, 90], [89, 85, 82], [79, 76, 69], [73, 70, 61], [73, 71, 59], [73, 69, 57], [69, 66, 51], [78, 72, 56], [76, 70, 54], 140, [75, 69, 53], [71, 63, 50], 48, [79, 71, 60], 44, 48, 50, 45, 50, 48, [79, 71, 58], [84, 76, 65], [89, 81, 70], [90, 81, 72], [88, 79, 70], [84, 75, 66], [80, 71, 64], [76, 67, 60], [73, 64, 57], 60, [71, 61, 52], [72, 60, 46], 69, 65, 66, 66, [68, 57, 39], [71, 60, 42], 77, 70, 70, 77, 78, [72, 61, 43], 170, 69, [69, 58, 40], 3, 12, 104, 8, 4, 101, 87, [63, 54, 37], 88, 89, 86, 87, 88, 92, 93, 93, 96, 96, 96, 99, 101, 3, 12, 8, [76, 67, 50], [77, 68, 51], 204, 204, 8, 204, 204, 8, 104, 4, 2, [68, 61, 43], [64, 57, 41], [69, 63, 51], [79, 72, 62], [92, 85, 79], [111, 102, 103], [132, 123, 128], [146, 136, 147], [152, 141, 155], [158, 147, 164], [153, 142, 159], [140, 129, 145], [123, 112, 126], [107, 97, 108], [99, 89, 97], [100, 90, 98], [103, 97, 101], [103, 98, 102], [106, 101, 105], [104, 100, 101], [96, 92, 91], [92, 87, 84], [89, 84, 78], [84, 80, 71], [77, 73, 62], [81, 75, 63], [79, 73, 61], [82, 74, 61], 153, 144, 47, [76, 66, 54], [69, 59, 47], [74, 64, 52], [75, 65, 55], 249, 249, 249, [79, 69, 59], [85, 75, 66], [90, 80, 71], [93, 82, 76], [92, 81, 75], [91, 80, 74], [88, 77, 73], [84, 73, 69], [80, 69, 65], [77, 66, 62], [75, 65, 56], [76, 64, 50], 78, 179, 68, 67, 68, 169, 69, 176, 170, 69, 179, 169, 65, 68, 68, 4, 8, [78, 69, 52], [80, 71, 54], [79, 70, 53], 104, 0, 88, [60, 51, 34], 187, 0, 101, 87, [61, 52, 35], 92, 288, [58, 51, 32], 296, 96, 96, 2, 4, 104, 204, 282, 284, 284, 284, 205, 282, 282, 282, 204, 104, 4, [70, 63, 47], [63, 55, 42], [65, 58, 48], [70, 63, 55], [80, 72, 69], [98, 89, 92], [121, 111, 119], [142, 132, 143], [155, 144, 160], [167, 156, 173], [163, 152, 169], [154, 143, 160], [137, 126, 142], [119, 109, 120], [107, 97, 106], [103, 93, 101], [104, 94, 102], [106, 99, 106], [112, 107, 113], [115, 110, 116], [113, 108, 112], [111, 105, 107], [107, 101, 101], [99, 94, 91], [91, 86, 82], [90, 83, 77], [84, 77, 69], 158, 146, [69, 61, 48], 44, 248, [70, 60, 48], [70, 60, 50], 348, [72, 59, 50], 348, [73, 60, 51], [77, 67, 57], [88, 75, 67], [93, 83, 74], [97, 84, 78], [96, 85, 79], [97, 83, 80], [94, 83, 79], [93, 79, 76], [87, 76, 72], [85, 71, 68], [83, 70, 62], [79, 67, 53], 75, 176, 169, 68, 68, 169, 69, 170, 170, 69, 179, 65, 68, [251, 214, 185], [253, 217, 191], [255, 222, 196], [255, 224, 200], [255, 224, 199], [255, 222, 197], [251, 219, 196], [250, 218, 195], [255, 224, 203], 2857, [255, 227, 206], [255, 228, 207], [255, 228, 209], [254, 229, 209], [254, 227, 208], 2857, [247, 212, 182], [255, 226, 190], [255, 223, 187], [239, 205, 168], [243, 211, 173], [255, 234, 198], [245, 219, 186], [197, 172, 142], [59, 34, 12], 2433, 2468, [44, 24, 17], [62, 43, 37], [40, 22, 18], [37, 19, 17], [62, 49, 43], 823, 49, 2786, 2883, 2884, [87, 79, 68], 2985, [85, 76, 67], 158, [89, 80, 73], [95, 86, 79], [101, 92, 87], [104, 95, 90], [104, 94, 92], [102, 92, 90], [100, 91, 86], [87, 80, 70], [85, 79, 67], 240, 2798, 1714, [84, 78, 56], 1714, 1603, 1605, 1609, [70, 64, 42], 1609, 3006, 1609, 1708, 1605, [77, 71, 49], 1703, 3001, 1613, [89, 78, 58], [88, 77, 57], [76, 63, 46], 1971, [38, 21, 11], 2136, [43, 26, 18], [42, 23, 16], [53, 32, 27], [49, 26, 20], [26, 4, 0], 2532, [66, 40, 23], [134, 109, 89], [178, 154, 130], [197, 166, 137], [202, 157, 118], [216, 163, 121], [216, 164, 124], [216, 167, 127], [224, 176, 138], 2442, [227, 183, 148], [235, 191, 156], [239, 196, 162], [243, 202, 170], [249, 208, 176], [251, 211, 176], [251, 208, 174], [250, 207, 172], [254, 211, 176], [255, 216, 181], [251, 215, 183], [251, 216, 188], [253, 218, 190], 2851, [255, 222, 195], 3052, [253, 219, 194], [249, 217, 192], [253, 221, 198], [253, 223, 199], [254, 223, 202], [252, 224, 202], [252, 224, 203], [250, 223, 202], [250, 221, 203], [251, 220, 199], [241, 204, 177], [255, 227, 194], [255, 231, 194], [249, 214, 174], [239, 206, 163], [242, 210, 169], [245, 217, 177], [246, 220, 185], [156, 132, 104], [70, 48, 27], 2433, [35, 15, 8], [43, 24, 20], [26, 8, 8], [31, 12, 14], [53, 38, 35], [64, 54, 44], 47, 46, 46, 46, 2987, 57, [85, 76, 69], 2794, [87, 78, 73], [93, 84, 79], [99, 89, 87], 2993, [106, 96, 95], 3093, [106, 96, 94], [93, 86, 76], [89, 83, 71], [84, 78, 66], 241, 2601, 1602, 2602, 1803, 1690, 2193, 2189, 1907, 1990, 1606, 1690, 1297, 1715, 1296, 2580, 1694, [97, 89, 68], [79, 68, 48], [49, 38, 20], [28, 15, 0], [26, 12, 0], [39, 22, 12], [46, 29, 19], [45, 27, 17], [42, 20, 9], [43, 19, 7], [33, 7, 0], [55, 28, 9], [129, 98, 77], [184, 154, 128], [196, 165, 137], [201, 165, 133], [202, 154, 116], [217, 166, 123], [217, 168, 127], [214, 166, 126], [223, 177, 141], [232, 188, 153], [237, 194, 160], [243, 200, 166], [242, 201, 169], [243, 204, 173], [246, 207, 176], [249, 210, 179], [251, 210, 178], 3043, [250, 210, 175], [250, 209, 177], [250, 213, 184], [248, 211, 184], [247, 210, 184], [249, 213, 187], [252, 218, 193], [254, 220, 195], 3054, [251, 216, 194], [248, 216, 193], 3156, [249, 217, 196], [248, 217, 196], [248, 215, 196], [246, 213, 194], [244, 211, 192], [245, 210, 190], [252, 215, 188], [253, 214, 183], [246, 208, 172], [250, 212, 173], [255, 219, 175], [241, 207, 162], [235, 202, 159], [252, 220, 181], [255, 229, 195], [136, 109, 82], [48, 21, 2], [33, 9, 0], [28, 8, 1], [19, 1, 0], [33, 17, 17], [56, 41, 36], [65, 53, 41], [78, 66, 52], 2177, 2420, 146, 2317, [82, 73, 66], 159, 159, [84, 75, 68], 441, 2893, 2994, 3095, [108, 98, 96], [108, 99, 94], [95, 87, 76], [91, 83, 70], 2884, 153, 154, 242, 2515, 50, 2694, 2688, 1623, 47, [76, 68, 57], 48, 2886, 2884, 2886, 2278, 1621, 2182, [100, 90, 78], [50, 41, 26], [37, 27, 15], [45, 33, 21], [39, 27, 15], 1940, 1448, [30, 13, 0], 2264, [44, 20, 0], [76, 49, 20], [132, 101, 72], [179, 144, 114], [199, 163, 131], [208, 169, 138], [218, 175, 143], [217, 171, 137], [221, 176, 137], 3136, [222, 176, 140], [220, 176, 141], [224, 181, 146], [234, 191, 157], 3139, [235, 194, 162], [236, 195, 163], [236, 197, 166], [237, 198, 167], [240, 201, 170], [244, 205, 172], [247, 208, 175], [248, 209, 180], [249, 209, 184], [250, 209, 189], [251, 210, 190], [250, 212, 191], [251, 215, 193], [252, 217, 195], 2954, [252, 220, 199], [250, 218, 197], [251, 219, 198], 3257, [253, 218, 198], [252, 215, 196], [249, 211, 192], [246, 208, 189], [244, 206, 185], [244, 208, 182], [245, 210, 180], [246, 210, 178], [245, 205, 170], [241, 199, 161], [239, 196, 154], [242, 197, 155], [243, 200, 158], [255, 213, 175], [233, 195, 159], [144, 110, 82], [49, 23, 0], [28, 7, 0], [40, 24, 9], [42, 32, 22], [50, 38, 26], 749, [108, 91, 71], [111, 95, 79], [89, 77, 61], 1922, 2415, [78, 71, 61], [80, 73, 63], [77, 68, 59], 58, [87, 77, 67], [91, 81, 71], [94, 84, 75], [97, 87, 78], [102, 91, 85], [106, 96, 87], 2918, [98, 84, 71], [93, 79, 66], [87, 73, 60], 2317, 2317, 2317, [79, 70, 61], 3303, [78, 69, 60], 3288, 59, [75, 66, 57], 59, 158, 56, [91, 82, 73], [101, 92, 83], [105, 96, 87], [97, 88, 79], [57, 47, 37], [37, 27, 17], [44, 34, 24], [52, 42, 32], [46, 34, 22], [34, 22, 8], [30, 14, 0], [41, 25, 2], [55, 35, 10], [77, 54, 23], [121, 92, 58], [168, 135, 100], [199, 160, 127], [207, 167, 132], [211, 166, 135], [215, 170, 137], [216, 171, 138], 3236, [223, 179, 144], 3334, [222, 179, 144], [225, 182, 147], 3238, [242, 199, 165], 3240, 3240, [235, 196, 165], 3243, 3244, 3141, [245, 209, 177], [246, 209, 182], [247, 206, 184], [248, 207, 189], [249, 208, 190], 3261, [249, 214, 194], 3158, 3256, [250, 219, 198], 3063, 3255, 3255, [254, 219, 199], [254, 216, 197], [252, 214, 195], [253, 212, 194], 3251, [245, 211, 186], [243, 212, 184], [245, 210, 182], 3141, [241, 197, 162], [239, 194, 155], [242, 193, 153], [242, 195, 153], [246, 201, 160], [254, 212, 174], [216, 180, 146], [127, 98, 68], [56, 35, 8], [49, 32, 12], [62, 51, 33], [63, 50, 33], [97, 79, 57], [126, 105, 84], [127, 110, 90], [98, 85, 68], 1921, 1623, [77, 70, 60], 3287, 3303, 46, [89, 76, 67], 1317, 3291, 355, 3293, 1319, [97, 83, 70], [97, 81, 66], [92, 76, 61], [88, 72, 57], [77, 68, 61], [78, 69, 62], [81, 72, 65], 2794, 2794, 3186, 3402, 159, 3400, 3400, 3087, [93, 84, 77], [101, 92, 85], 724, [108, 99, 92], 157, 3317, [31, 21, 11], [36, 26, 16], [39, 29, 19], [48, 36, 24], [48, 36, 22], [45, 29, 13], [62, 46, 23], [86, 66, 41], [120, 97, 66], [165, 136, 104], [196, 163, 128], [209, 170, 137], [208, 167, 135], 3330, [213, 168, 135], 3332, [221, 177, 142], [226, 182, 147], 3038, [226, 183, 149], [228, 185, 151], 3238, 3040, [234, 193, 161], 3440, 3342, 3243, 3244, [244, 205, 174], [245, 208, 179], [247, 210, 183], [245, 207, 184], [245, 209, 187], [247, 211, 189], [248, 213, 191], 3156, [248, 218, 194], [248, 220, 196], [249, 221, 197], [250, 222, 198], 3455, [250, 220, 196], 2954, [253, 218, 196], [254, 218, 196], [255, 218, 197], [254, 219, 197], [247, 216, 195], [247, 217, 193], [245, 213, 188], [242, 207, 179], [239, 198, 166], [236, 193, 158], [235, 189, 153], [235, 190, 151], [243, 199, 160], [253, 213, 177], [252, 216, 182], [183, 152, 121], [78, 52, 25], [42, 22, 0], [63, 46, 26], [72, 55, 35], [111, 91, 67], [133, 113, 89], [133, 116, 96], [101, 88, 71], 248, 1623, 3286, [78, 71, 63], 3186, 2987, 2819, 2819, 834, [89, 78, 72], 258, [96, 83, 75], [95, 81, 68], 1973, [90, 77, 61], [87, 74, 58], 3400, [79, 70, 63], 2794, 2793, [87, 78, 71], 3087, 2794, 3402, 3402, 3402, 2793, 3411, [105, 96, 89], 725, 3414, 159, [35, 25, 16], 3419, 3419, [32, 22, 12], [45, 32, 23], [55, 43, 29], [59, 43, 27], 1052, [114, 94, 69], [146, 123, 92], [180, 151, 119], [195, 162, 129], [200, 161, 128], [204, 163, 131], [213, 168, 137], [216, 171, 140], [217, 172, 139], [222, 178, 143], [228, 184, 149], [231, 187, 152], [231, 188, 154], 3536, 3238, 3138, 3440, 3440, 3342, 3243, [241, 202, 171], [245, 206, 175], [246, 209, 180], 3149, [250, 214, 190], [249, 214, 192], 3155, 2955, [251, 221, 197], [251, 223, 199], [251, 225, 200], [252, 226, 201], [248, 222, 197], [247, 221, 196], [247, 219, 195], [249, 219, 195], 2954, 3056, [255, 223, 201], [255, 226, 205], 2958, [252, 225, 204], 3063, 3466, [240, 205, 175], [235, 196, 163], [231, 191, 156], [228, 188, 152], [243, 203, 167], [241, 203, 167], [249, 213, 179], [224, 191, 160], [147, 120, 91], [92, 66, 41], [91, 69, 45], [111, 89, 65], [112, 90, 66], [123, 103, 79], [121, 104, 84], [97, 84, 67], 246, 3303, [82, 75, 67], [75, 68, 60], [88, 79, 72], [90, 81, 74], [95, 82, 76], [94, 81, 75], [90, 79, 75], [89, 78, 74], [91, 80, 78], 256, 1230, [95, 82, 66], 1037, 3498, [79, 70, 65], 732, [85, 76, 71], [88, 79, 74], 3603, [86, 77, 72], [84, 75, 70], 732, 3089, 3089, 3603, 441, [102, 93, 88], [118, 109, 104], 629, 60, [32, 22, 13], [51, 41, 31], [60, 50, 41], 1824, [43, 30, 21], 2721, [63, 47, 32], [94, 77, 57], [125, 105, 80], [148, 124, 96], [172, 143, 111], [182, 149, 116], [192, 153, 120], [205, 164, 132], [217, 172, 143], [220, 175, 144], [220, 175, 142], [224, 180, 145], [230, 185, 152], [234, 189, 156], 3238, 3238, [235, 192, 160], [236, 193, 161], [234, 193, 163], [235, 194, 164], 3242, [238, 199, 168], [242, 203, 174], [247, 208, 179], [249, 212, 183], [250, 215, 187], 2953, 3057, [254, 224, 200], [253, 225, 201], [254, 226, 202], [254, 228, 203], 3653, [254, 230, 204], [249, 225, 199], [249, 223, 198], 3657, 3456, 2863, [255, 227, 203], [255, 229, 206], [255, 231, 210], [255, 235, 216], [255, 233, 216], 2960, [250, 222, 200], [246, 214, 189], [239, 205, 177], [233, 198, 168], [229, 193, 161], [230, 194, 160], [236, 200, 166], [240, 205, 173], [246, 213, 180], [227, 196, 167], [168, 139, 109], [121, 91, 63], [116, 90, 63], [108, 84, 58], [111, 91, 66], [115, 98, 78], 3483, [80, 70, 58], 2317, [85, 78, 70], [77, 70, 64], 630, [96, 87, 82], [101, 87, 84], [100, 86, 83], [96, 85, 83], [95, 83, 83], [97, 85, 87], [99, 88, 86], [99, 86, 77], [97, 85, 71], 1321, 1428, [81, 72, 67], 3186, 3602, 3504, 3089, 3504, 3602, 3189, [91, 82, 77], 3411, 3708, 3504, [95, 86, 81], 724, 2893, [60, 51, 44], [47, 37, 28], [55, 45, 35], [58, 48, 39], [40, 30, 21], [37, 24, 15], 2721, 1350, [97, 80, 60], [118, 98, 74], [138, 114, 86], [161, 132, 100], [177, 144, 111], [195, 156, 125], [210, 169, 137], [221, 176, 147], [222, 177, 146], [224, 179, 146], 3038, [231, 186, 153], [235, 190, 157], [236, 193, 159], 3138, [237, 194, 162], 3738, 3641, [236, 195, 165], 3243, 3244, [245, 206, 177], [249, 210, 181], 2948, 3050, 2952, [255, 225, 201], 3749, 3651, 3651, 3555, 3555, 3555, 3554, 3554, 3555, 3652, 2755, [255, 231, 207], [255, 233, 209], [255, 233, 212], [255, 235, 218], 3665, [255, 230, 211], 2958, [251, 223, 201], [247, 217, 191], [241, 210, 182], [236, 205, 176], [221, 188, 157], [238, 205, 174], [232, 199, 168], [234, 201, 170], [249, 216, 185], [214, 181, 150], [143, 110, 79], [102, 73, 43], [103, 79, 51], [103, 83, 58], [118, 101, 81], 964, [87, 77, 65], 3288, 341, [82, 75, 69], [96, 86, 84], 3091, [104, 90, 89], [105, 91, 91], [102, 90, 90], [103, 91, 93], [105, 93, 97], [108, 96, 98], [106, 93, 87], 1419, [100, 87, 78], 1318, 3501, 2317, 3186, 158, 2793, 157, 2989, 56, 3589, [96, 87, 78], [96, 87, 80], 57, 2989, [103, 94, 85], 3504, [48, 39, 30], [52, 42, 33], [42, 32, 23], [43, 32, 26], [33, 23, 14], [33, 20, 11], 2777, [76, 60, 45], [93, 76, 56], [107, 87, 63], [128, 104, 76], [155, 126, 96], 3727, [197, 158, 127], [210, 169, 139], [222, 177, 148], [227, 182, 151], [228, 183, 150], [229, 184, 151], [232, 187, 154], 3735, 3639, [238, 195, 163], 2456, 2456, 2457, [238, 197, 167], [239, 200, 171], 3644, [247, 207, 181], [252, 212, 186], [254, 217, 190], [255, 220, 192], 2953, [253, 221, 196], [252, 220, 195], [250, 220, 194], [249, 219, 193], [245, 218, 191], [244, 217, 190], 3854, [248, 221, 194], [249, 222, 195], [251, 224, 197], 2656, 2660, [255, 228, 203], 3861, 2758, [255, 225, 207], [253, 224, 208], [254, 225, 207], [255, 227, 209], [255, 230, 209], [255, 228, 206], 3553, [247, 220, 193], [235, 205, 177], [239, 210, 180], [232, 201, 172], 3774, [250, 217, 186], 3265, [189, 154, 124], [124, 93, 64], [99, 73, 46], [91, 71, 46], [114, 98, 75], [126, 113, 96], 2079, 2317, [84, 77, 71], [85, 77, 74], [94, 84, 83], [97, 87, 86], [104, 90, 90], [107, 92, 95], [106, 94, 98], [108, 95, 102], [112, 99, 108], [116, 103, 110], [116, 105, 103], [114, 103, 99], [111, 100, 96], [108, 97, 93], 2693, 2693, 146, 46, 2790, 155, 55, 1684, [88, 80, 69], 729, [99, 91, 80], 2985, 54, 729, 2515, [40, 32, 21], [27, 17, 8], [25, 15, 6], [46, 35, 29], [45, 34, 28], 1744, 3279, 1349, [87, 70, 52], [102, 82, 58], [123, 99, 71], [152, 123, 93], [176, 143, 112], [193, 154, 123], [206, 165, 135], 3830, [232, 187, 158], [231, 186, 155], 3734, 3834, 3635, 3639, [239, 196, 164], [239, 195, 166], 3938, 3841, [239, 198, 168], [240, 201, 172], [243, 204, 175], [248, 208, 182], [253, 213, 187], [255, 219, 192], [255, 221, 195], [253, 217, 193], [251, 217, 192], [249, 215, 190], [247, 213, 188], 3364, [241, 209, 184], [242, 208, 183], [239, 207, 182], [246, 212, 187], 3668, 3152, 2854, 2953, [255, 223, 198], 2953, [255, 220, 198], [250, 215, 196], [248, 215, 198], [251, 220, 200], [255, 226, 206], 3763, [255, 236, 213], [255, 233, 210], [255, 229, 204], [254, 228, 201], [235, 208, 179], [234, 204, 176], [242, 211, 182], [248, 214, 186], [249, 214, 184], [206, 169, 140], [130, 96, 68], [95, 69, 42], [79, 59, 34], [103, 87, 64], [128, 115, 96], [107, 97, 85], 2889, [87, 80, 74], [83, 75, 72], [91, 81, 80], [95, 85, 84], [103, 88, 91], 3891, 3893, [111, 98, 105], [116, 103, 113], [120, 107, 116], [127, 115, 119], [125, 113, 113], [122, 110, 112], [120, 108, 108], 50, 48, 48, 50, 153, 2883, 730, 1820, [88, 80, 67], [99, 91, 78], 2278, [84, 76, 63], [95, 87, 74], [96, 88, 75], [67, 59, 46], [34, 26, 13], [29, 19, 10], [39, 28, 22], [40, 29, 23], 4017, [47, 34, 26], [51, 38, 29], [60, 44, 31], [73, 56, 38], [96, 76, 52], [118, 94, 68], 2261, [163, 130, 99], [191, 152, 121], [219, 178, 148], [231, 185, 159], [225, 180, 151], [228, 183, 152], [230, 185, 154], [234, 189, 158], [235, 190, 159], [234, 190, 161], [235, 191, 162], 2456, [240, 196, 167], [239, 198, 170], [241, 200, 172], [243, 203, 177], [250, 210, 184], [255, 216, 190], 2850, [255, 218, 192], [252, 215, 189], [249, 211, 188], [250, 212, 189], 4048, 3448, [240, 202, 179], [237, 199, 176], [239, 198, 176], [238, 200, 177], [245, 204, 182], 3448, [250, 209, 187], [248, 210, 187], [247, 209, 186], 4060, 4049, [252, 214, 191], [247, 209, 188], 3263, [243, 206, 187], [247, 215, 194], 2958, [255, 237, 215], [255, 242, 219], [255, 242, 216], 4071, [255, 236, 209], 3871, 3974, [234, 200, 173], 3050, [236, 199, 172], [158, 124, 96], [96, 70, 43], [68, 48, 23], [78, 62, 39], [121, 108, 89], 826, 3305, 219, 3887, [88, 78, 77], [93, 83, 82], [102, 87, 90], [105, 90, 93], [106, 93, 100], [113, 100, 107], [125, 112, 122], [135, 122, 132], [138, 127, 135], 637, [148, 137, 145], 636, 2085, 2085, 49, 49, 2277, 2502, 2378, [87, 80, 64], 2378, 2280, 4107, 2184, [92, 85, 69], [89, 82, 66], [61, 54, 38], 4015, 4016, [38, 27, 21], 4018, 4018, [48, 35, 29], [49, 36, 27], [55, 39, 26], [65, 48, 30], [98, 77, 56], [117, 93, 67], [141, 111, 83], [164, 130, 102], [192, 153, 124], [217, 176, 146], [229, 183, 157], [226, 180, 154], 3830, [223, 178, 147], [226, 181, 150], 3932, 4037, 3938, [243, 199, 170], [244, 200, 171], [238, 197, 169], [240, 199, 171], [245, 205, 179], [251, 211, 185], [255, 215, 189], 4044, [253, 216, 190], 2849, [255, 214, 192], [254, 212, 190], [250, 208, 186], [244, 202, 180], [240, 195, 174], [236, 191, 170], [235, 190, 169], 4154, [239, 194, 173], [241, 196, 175], [243, 198, 177], 4158, [240, 198, 176], 4160, [243, 201, 179], [246, 204, 182], [244, 199, 176], [244, 202, 178], [246, 205, 183], [247, 211, 187], 3950, [248, 218, 192], [245, 219, 194], [243, 219, 193], [236, 212, 186], [246, 220, 195], [254, 227, 200], 3851, [233, 201, 176], [236, 202, 175], [232, 196, 170], [187, 153, 126], [105, 79, 52], [70, 50, 25], [67, 51, 28], [105, 92, 75], [117, 107, 95], 157, [95, 88, 82], [86, 78, 75], [90, 80, 79], 3889, [106, 92, 92], [109, 94, 97], [108, 96, 100], [115, 102, 109], [127, 114, 123], [137, 124, 134], [145, 133, 145], [154, 144, 155], [161, 149, 161], [157, 147, 158], 2303, 2303, 2277, 2277, 2303, 2501, 2184, 2784, 2184, [86, 79, 63], 2502, 2502, [88, 81, 65], 2085, [52, 45, 29], [35, 27, 14], [34, 24, 15], [42, 31, 25], [43, 32, 28], [41, 30, 24], [45, 32, 26], [44, 31, 23], 2674, [66, 49, 31], [96, 75, 54], [112, 88, 62], [138, 108, 80], [166, 132, 104], [195, 156, 127], [214, 173, 145], [225, 179, 155], [225, 179, 153], [227, 182, 153]]
[150, 100]
As you can see, it has both integer and list values. Then, I pass the file into my image_decoding.py script. Here’s how it looks:
# imports
import matplotlib.pyplot as plt
# decoding
f = open("image_code.txt", "r")
Lines = f.readlines()
size = Lines[1].strip('][').split(', ')
image_coding = Lines[0][:-2].strip('][').split(', ')
size[0], size[1] = int(size[0]), int(size[1])
f.close()
print('started reshaping the list')
for i in range(len(image_coding)):
try:
value = image_coding[i]
if value[0] == '[':
image_coding[i] = [int(image_coding[i][1:]), int(
image_coding[i + 1]), int(image_coding[i + 2][:-1])]
if i != 0 and type(image_coding[i - 1]) != list and type(image_coding[i]) != list:
try:
image_coding[i] = int(image_coding[i])
except ValueError:
pass
except IndexError:
break
counter = 1
for j in image_coding:
if type(j) != int and j[-1] == ']':
image_coding.remove(j)
print(
f'Stage 1: {counter} / {len(image_coding)} ({str(counter / len(image_coding) * 100)[:7]}%) done', end='r')
counter += 1
counter = 1
for l in image_coding:
if type(l) == str:
image_coding.remove(l)
print(
f'Stage 2: {counter} / {len(image_coding)} ({str(counter / len(image_coding) * 100)[:7]}%) done', end='r')
counter += 1
print('started decoding')
i = 0
image = []
for pixel in image_coding:
i += 1
print(f'Decoding: {str(i / len(image_coding) * 100)[:7]}% done', end='r')
if type(pixel) == list:
image.append(pixel)
else:
image.append(image_coding[pixel])
# unflattening the image to display it correctly
print('nstarted unflattening')
new_image = []
for i in range(size[0]):
imageline = []
for j in range(size[1]):
imageline.append(image[i*size[1] + j])
new_image.append(imageline)
image = new_image
plt.imshow(image)
plt.show()
When running the script on the 50x100.jpg image (which looks like this):
The code works flawlessly. However, when I try to run it with the 100x100.jpg image, 100x150.jpg image or 300x200.jpg image, which look like this:
image_decoding.py returns the following TypeError:
Traceback (most recent call last):
File "c:UserspcDesktopProgrammingimageCodingimage_decoding.py", line 82, in <module>
plt.imshow(image)
File "C:UserspcAppDataLocalProgramsPythonPython310libsite-packagesmatplotlib_apideprecation.py", line 454, in wrapper
return func(*args, **kwargs)
File "C:UserspcAppDataLocalProgramsPythonPython310libsite-packagesmatplotlibpyplot.py", line 2611, in imshow
__ret = gca().imshow(
File "C:UserspcAppDataLocalProgramsPythonPython310libsite-packagesmatplotlib_apideprecation.py", line 454, in wrapper
return func(*args, **kwargs)
File "C:UserspcAppDataLocalProgramsPythonPython310libsite-packagesmatplotlib__init__.py", line 1423, in inner
return func(ax, *map(sanitize_sequence, args), **kwargs)
File "C:UserspcAppDataLocalProgramsPythonPython310libsite-packagesmatplotlibaxes_axes.py", line 5572, in imshow
im.set_data(X)
File "C:UserspcAppDataLocalProgramsPythonPython310libsite-packagesmatplotlibimage.py", line 701, in set_data
raise TypeError("Image data of dtype {} cannot be converted to "
TypeError: Image data of dtype object cannot be converted to float
P. S. There might be some really stupid bug in the code (e.g. missing bracket), I’m still quite new to this 🙂
Answers:
It’s quite a lot of code you have there… I hope my answer is clear without reposting everything 🙂
Your bug is in the file reading and parsing (recreating the original data-types etc.), so I have the following suggestion for you.
Python comes with a package called pickle that you can use to store python objects and read them back into memory.
I’d suggest the following changes to your code.
In image_encoding.py replace
f = open("image_code.txt", "w")
f.write(str(image_coding))
f.close()
f = open("image_code.txt", "a")
f.write(f'n{size}')
f.close()
with
import pickle # put that at the top of your file
with open("image_code.txt", "wb") as file:
pickle.dump({"image_coding": image_coding, "size": size}, file)
And then in image_decoding.py instead of
f = open("image_code.txt", "r")
Lines = f.readlines()
size = Lines[1].strip('][').split(', ')
image_coding = Lines[0][:-2].strip('][').split(', ')
size[0], size[1] = int(size[0]), int(size[1])
f.close()
print('started reshaping the list')
for i in range(len(image_coding)):
try:
value = image_coding[i]
if value[0] == '[':
image_coding[i] = [int(image_coding[i][1:]), int(
image_coding[i + 1]), int(image_coding[i + 2][:-1])]
if i != 0 and type(image_coding[i - 1]) != list and type(image_coding[i]) != list:
try:
image_coding[i] = int(image_coding[i])
except ValueError:
pass
except IndexError:
break
you can use:
import pickle
with open("test_data/image_code.txt", "rb") as file:
data = pickle.load(file)
size = data.get("size")
image_coding = data.get("image_coding")
and have your original variables back without checking for "[" and casting back to integers etc.
Hope that helps. If you have any question, dont hesitate to ask.
I’m writing an image optimisation algorythm (I know it’s horribly inefficient, it’s just my 1st attempt), and here’s what I currently have. The directory of the project looks like this:
image_encoding.py takes an image and encodes it using the following logic:
if the pixel is new, it encodes its RGB value
else, it encodes the index of a pixel with the same color
If you need more insight, here’s my code for image_encoding.py(it writes the encoded image into image_code.txt):
# imports
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# reading the image and displaying it
image = mpimg.imread('./100x150.jpg')
size = [image.shape[0], image.shape[1]]
print(f'size of the image: {size}')
plt.imshow(image)
plt.show()
# flattening the image for further processing
new_image = []
for imgline in image:
for pixel in imgline:
new_image.append(list(pixel))
image = new_image
# encodind the image
print('started encoding')
image_coding = []
# for a simple 2x2 image of a black square, image_coding will look like this:
# [ [ 255, 255, 255 ], 0, 0, 0 ]
i = 0
for pixel in image:
i += 1
print(f'{str(i / len(image) * 100)[:5]}% done', end='r')
if pixel not in image_coding:
image_coding.append(pixel)
else:
image_coding.append(image_coding.index(pixel))
# write the image coding into a file
print('writing image coding into image_code.txt')
f = open("image_code.txt", "w")
f.write(str(image_coding))
f.close()
f = open("image_code.txt", "a")
f.write(f'n{size}')
f.close()
print('image successfully encoded into image_code.txt')
Then, the image_code.txt file will look like this:
[[67, 58, 41], [68, 59, 42], [70, 61, 44], [71, 62, 45], [72, 63, 46], 4, 4, 3, [75, 66, 49], 8, 8, 8, [73, 64, 47], 3, 2, [67, 60, 41], [66, 59, 40], [73, 68, 48], [82, 76, 60], [95, 89, 77], [113, 106, 100], [131, 123, 121], [137, 128, 133], [134, 124, 132], [125, 115, 126], [118, 108, 119], [105, 95, 104], [88, 78, 86], [77, 68, 73], [76, 67, 68], [85, 76, 77], [93, 88, 85], [86, 82, 79], [83, 82, 77], [77, 74, 69], [66, 63, 54], [61, 59, 47], [64, 62, 47], [66, 63, 48], [65, 62, 45], [78, 73, 54], [75, 70, 51], 41, [72, 67, 48], [70, 62, 49], [78, 70, 57], [82, 74, 63], [73, 65, 52], [76, 68, 55], [77, 70, 54], [77, 69, 56], 48, 48, [80, 72, 59], [86, 78, 67], [92, 84, 73], [89, 80, 71], [86, 77, 68], [81, 72, 63], [76, 67, 58], [71, 62, 55], [69, 60, 53], [67, 58, 51], [67, 59, 48], [68, 56, 42], [67, 56, 38], [65, 54, 36], [64, 53, 35], [66, 55, 37], [70, 59, 41], [75, 64, 46], [78, 67, 49], [79, 68, 50], 71, 71, [76, 65, 47], 70, [74, 63, 45], [73, 62, 44], 78, 3, 4, 12, 12, 2, 1, [66, 57, 40], [65, 56, 39], [62, 53, 36], [64, 55, 38], 89, 88, [59, 50, 33], [58, 49, 32], 93, 93, [57, 50, 31], 96, 96, [56, 49, 30], 1, [69, 60, 43], 3, 12, [74, 65, 48], 104, 104, 12, 104, 104, 104, 104, 12, 3, 101, [67, 60, 42], 16, [73, 68, 49], [83, 77, 63], [96, 89, 79], [115, 107, 104], [134, 125, 126], [143, 133, 141], [142, 132, 141], [140, 129, 143], [134, 123, 137], [121, 111, 122], [103, 93, 102], [90, 80, 88], [87, 78, 81], [93, 84, 87], [99, 93, 93], [95, 91, 90], [93, 92, 90], [89, 85, 82], [79, 76, 69], [73, 70, 61], [73, 71, 59], [73, 69, 57], [69, 66, 51], [78, 72, 56], [76, 70, 54], 140, [75, 69, 53], [71, 63, 50], 48, [79, 71, 60], 44, 48, 50, 45, 50, 48, [79, 71, 58], [84, 76, 65], [89, 81, 70], [90, 81, 72], [88, 79, 70], [84, 75, 66], [80, 71, 64], [76, 67, 60], [73, 64, 57], 60, [71, 61, 52], [72, 60, 46], 69, 65, 66, 66, [68, 57, 39], [71, 60, 42], 77, 70, 70, 77, 78, [72, 61, 43], 170, 69, [69, 58, 40], 3, 12, 104, 8, 4, 101, 87, [63, 54, 37], 88, 89, 86, 87, 88, 92, 93, 93, 96, 96, 96, 99, 101, 3, 12, 8, [76, 67, 50], [77, 68, 51], 204, 204, 8, 204, 204, 8, 104, 4, 2, [68, 61, 43], [64, 57, 41], [69, 63, 51], [79, 72, 62], [92, 85, 79], [111, 102, 103], [132, 123, 128], [146, 136, 147], [152, 141, 155], [158, 147, 164], [153, 142, 159], [140, 129, 145], [123, 112, 126], [107, 97, 108], [99, 89, 97], [100, 90, 98], [103, 97, 101], [103, 98, 102], [106, 101, 105], [104, 100, 101], [96, 92, 91], [92, 87, 84], [89, 84, 78], [84, 80, 71], [77, 73, 62], [81, 75, 63], [79, 73, 61], [82, 74, 61], 153, 144, 47, [76, 66, 54], [69, 59, 47], [74, 64, 52], [75, 65, 55], 249, 249, 249, [79, 69, 59], [85, 75, 66], [90, 80, 71], [93, 82, 76], [92, 81, 75], [91, 80, 74], [88, 77, 73], [84, 73, 69], [80, 69, 65], [77, 66, 62], [75, 65, 56], [76, 64, 50], 78, 179, 68, 67, 68, 169, 69, 176, 170, 69, 179, 169, 65, 68, 68, 4, 8, [78, 69, 52], [80, 71, 54], [79, 70, 53], 104, 0, 88, [60, 51, 34], 187, 0, 101, 87, [61, 52, 35], 92, 288, [58, 51, 32], 296, 96, 96, 2, 4, 104, 204, 282, 284, 284, 284, 205, 282, 282, 282, 204, 104, 4, [70, 63, 47], [63, 55, 42], [65, 58, 48], [70, 63, 55], [80, 72, 69], [98, 89, 92], [121, 111, 119], [142, 132, 143], [155, 144, 160], [167, 156, 173], [163, 152, 169], [154, 143, 160], [137, 126, 142], [119, 109, 120], [107, 97, 106], [103, 93, 101], [104, 94, 102], [106, 99, 106], [112, 107, 113], [115, 110, 116], [113, 108, 112], [111, 105, 107], [107, 101, 101], [99, 94, 91], [91, 86, 82], [90, 83, 77], [84, 77, 69], 158, 146, [69, 61, 48], 44, 248, [70, 60, 48], [70, 60, 50], 348, [72, 59, 50], 348, [73, 60, 51], [77, 67, 57], [88, 75, 67], [93, 83, 74], [97, 84, 78], [96, 85, 79], [97, 83, 80], [94, 83, 79], [93, 79, 76], [87, 76, 72], [85, 71, 68], [83, 70, 62], [79, 67, 53], 75, 176, 169, 68, 68, 169, 69, 170, 170, 69, 179, 65, 68, [251, 214, 185], [253, 217, 191], [255, 222, 196], [255, 224, 200], [255, 224, 199], [255, 222, 197], [251, 219, 196], [250, 218, 195], [255, 224, 203], 2857, [255, 227, 206], [255, 228, 207], [255, 228, 209], [254, 229, 209], [254, 227, 208], 2857, [247, 212, 182], [255, 226, 190], [255, 223, 187], [239, 205, 168], [243, 211, 173], [255, 234, 198], [245, 219, 186], [197, 172, 142], [59, 34, 12], 2433, 2468, [44, 24, 17], [62, 43, 37], [40, 22, 18], [37, 19, 17], [62, 49, 43], 823, 49, 2786, 2883, 2884, [87, 79, 68], 2985, [85, 76, 67], 158, [89, 80, 73], [95, 86, 79], [101, 92, 87], [104, 95, 90], [104, 94, 92], [102, 92, 90], [100, 91, 86], [87, 80, 70], [85, 79, 67], 240, 2798, 1714, [84, 78, 56], 1714, 1603, 1605, 1609, [70, 64, 42], 1609, 3006, 1609, 1708, 1605, [77, 71, 49], 1703, 3001, 1613, [89, 78, 58], [88, 77, 57], [76, 63, 46], 1971, [38, 21, 11], 2136, [43, 26, 18], [42, 23, 16], [53, 32, 27], [49, 26, 20], [26, 4, 0], 2532, [66, 40, 23], [134, 109, 89], [178, 154, 130], [197, 166, 137], [202, 157, 118], [216, 163, 121], [216, 164, 124], [216, 167, 127], [224, 176, 138], 2442, [227, 183, 148], [235, 191, 156], [239, 196, 162], [243, 202, 170], [249, 208, 176], [251, 211, 176], [251, 208, 174], [250, 207, 172], [254, 211, 176], [255, 216, 181], [251, 215, 183], [251, 216, 188], [253, 218, 190], 2851, [255, 222, 195], 3052, [253, 219, 194], [249, 217, 192], [253, 221, 198], [253, 223, 199], [254, 223, 202], [252, 224, 202], [252, 224, 203], [250, 223, 202], [250, 221, 203], [251, 220, 199], [241, 204, 177], [255, 227, 194], [255, 231, 194], [249, 214, 174], [239, 206, 163], [242, 210, 169], [245, 217, 177], [246, 220, 185], [156, 132, 104], [70, 48, 27], 2433, [35, 15, 8], [43, 24, 20], [26, 8, 8], [31, 12, 14], [53, 38, 35], [64, 54, 44], 47, 46, 46, 46, 2987, 57, [85, 76, 69], 2794, [87, 78, 73], [93, 84, 79], [99, 89, 87], 2993, [106, 96, 95], 3093, [106, 96, 94], [93, 86, 76], [89, 83, 71], [84, 78, 66], 241, 2601, 1602, 2602, 1803, 1690, 2193, 2189, 1907, 1990, 1606, 1690, 1297, 1715, 1296, 2580, 1694, [97, 89, 68], [79, 68, 48], [49, 38, 20], [28, 15, 0], [26, 12, 0], [39, 22, 12], [46, 29, 19], [45, 27, 17], [42, 20, 9], [43, 19, 7], [33, 7, 0], [55, 28, 9], [129, 98, 77], [184, 154, 128], [196, 165, 137], [201, 165, 133], [202, 154, 116], [217, 166, 123], [217, 168, 127], [214, 166, 126], [223, 177, 141], [232, 188, 153], [237, 194, 160], [243, 200, 166], [242, 201, 169], [243, 204, 173], [246, 207, 176], [249, 210, 179], [251, 210, 178], 3043, [250, 210, 175], [250, 209, 177], [250, 213, 184], [248, 211, 184], [247, 210, 184], [249, 213, 187], [252, 218, 193], [254, 220, 195], 3054, [251, 216, 194], [248, 216, 193], 3156, [249, 217, 196], [248, 217, 196], [248, 215, 196], [246, 213, 194], [244, 211, 192], [245, 210, 190], [252, 215, 188], [253, 214, 183], [246, 208, 172], [250, 212, 173], [255, 219, 175], [241, 207, 162], [235, 202, 159], [252, 220, 181], [255, 229, 195], [136, 109, 82], [48, 21, 2], [33, 9, 0], [28, 8, 1], [19, 1, 0], [33, 17, 17], [56, 41, 36], [65, 53, 41], [78, 66, 52], 2177, 2420, 146, 2317, [82, 73, 66], 159, 159, [84, 75, 68], 441, 2893, 2994, 3095, [108, 98, 96], [108, 99, 94], [95, 87, 76], [91, 83, 70], 2884, 153, 154, 242, 2515, 50, 2694, 2688, 1623, 47, [76, 68, 57], 48, 2886, 2884, 2886, 2278, 1621, 2182, [100, 90, 78], [50, 41, 26], [37, 27, 15], [45, 33, 21], [39, 27, 15], 1940, 1448, [30, 13, 0], 2264, [44, 20, 0], [76, 49, 20], [132, 101, 72], [179, 144, 114], [199, 163, 131], [208, 169, 138], [218, 175, 143], [217, 171, 137], [221, 176, 137], 3136, [222, 176, 140], [220, 176, 141], [224, 181, 146], [234, 191, 157], 3139, [235, 194, 162], [236, 195, 163], [236, 197, 166], [237, 198, 167], [240, 201, 170], [244, 205, 172], [247, 208, 175], [248, 209, 180], [249, 209, 184], [250, 209, 189], [251, 210, 190], [250, 212, 191], [251, 215, 193], [252, 217, 195], 2954, [252, 220, 199], [250, 218, 197], [251, 219, 198], 3257, [253, 218, 198], [252, 215, 196], [249, 211, 192], [246, 208, 189], [244, 206, 185], [244, 208, 182], [245, 210, 180], [246, 210, 178], [245, 205, 170], [241, 199, 161], [239, 196, 154], [242, 197, 155], [243, 200, 158], [255, 213, 175], [233, 195, 159], [144, 110, 82], [49, 23, 0], [28, 7, 0], [40, 24, 9], [42, 32, 22], [50, 38, 26], 749, [108, 91, 71], [111, 95, 79], [89, 77, 61], 1922, 2415, [78, 71, 61], [80, 73, 63], [77, 68, 59], 58, [87, 77, 67], [91, 81, 71], [94, 84, 75], [97, 87, 78], [102, 91, 85], [106, 96, 87], 2918, [98, 84, 71], [93, 79, 66], [87, 73, 60], 2317, 2317, 2317, [79, 70, 61], 3303, [78, 69, 60], 3288, 59, [75, 66, 57], 59, 158, 56, [91, 82, 73], [101, 92, 83], [105, 96, 87], [97, 88, 79], [57, 47, 37], [37, 27, 17], [44, 34, 24], [52, 42, 32], [46, 34, 22], [34, 22, 8], [30, 14, 0], [41, 25, 2], [55, 35, 10], [77, 54, 23], [121, 92, 58], [168, 135, 100], [199, 160, 127], [207, 167, 132], [211, 166, 135], [215, 170, 137], [216, 171, 138], 3236, [223, 179, 144], 3334, [222, 179, 144], [225, 182, 147], 3238, [242, 199, 165], 3240, 3240, [235, 196, 165], 3243, 3244, 3141, [245, 209, 177], [246, 209, 182], [247, 206, 184], [248, 207, 189], [249, 208, 190], 3261, [249, 214, 194], 3158, 3256, [250, 219, 198], 3063, 3255, 3255, [254, 219, 199], [254, 216, 197], [252, 214, 195], [253, 212, 194], 3251, [245, 211, 186], [243, 212, 184], [245, 210, 182], 3141, [241, 197, 162], [239, 194, 155], [242, 193, 153], [242, 195, 153], [246, 201, 160], [254, 212, 174], [216, 180, 146], [127, 98, 68], [56, 35, 8], [49, 32, 12], [62, 51, 33], [63, 50, 33], [97, 79, 57], [126, 105, 84], [127, 110, 90], [98, 85, 68], 1921, 1623, [77, 70, 60], 3287, 3303, 46, [89, 76, 67], 1317, 3291, 355, 3293, 1319, [97, 83, 70], [97, 81, 66], [92, 76, 61], [88, 72, 57], [77, 68, 61], [78, 69, 62], [81, 72, 65], 2794, 2794, 3186, 3402, 159, 3400, 3400, 3087, [93, 84, 77], [101, 92, 85], 724, [108, 99, 92], 157, 3317, [31, 21, 11], [36, 26, 16], [39, 29, 19], [48, 36, 24], [48, 36, 22], [45, 29, 13], [62, 46, 23], [86, 66, 41], [120, 97, 66], [165, 136, 104], [196, 163, 128], [209, 170, 137], [208, 167, 135], 3330, [213, 168, 135], 3332, [221, 177, 142], [226, 182, 147], 3038, [226, 183, 149], [228, 185, 151], 3238, 3040, [234, 193, 161], 3440, 3342, 3243, 3244, [244, 205, 174], [245, 208, 179], [247, 210, 183], [245, 207, 184], [245, 209, 187], [247, 211, 189], [248, 213, 191], 3156, [248, 218, 194], [248, 220, 196], [249, 221, 197], [250, 222, 198], 3455, [250, 220, 196], 2954, [253, 218, 196], [254, 218, 196], [255, 218, 197], [254, 219, 197], [247, 216, 195], [247, 217, 193], [245, 213, 188], [242, 207, 179], [239, 198, 166], [236, 193, 158], [235, 189, 153], [235, 190, 151], [243, 199, 160], [253, 213, 177], [252, 216, 182], [183, 152, 121], [78, 52, 25], [42, 22, 0], [63, 46, 26], [72, 55, 35], [111, 91, 67], [133, 113, 89], [133, 116, 96], [101, 88, 71], 248, 1623, 3286, [78, 71, 63], 3186, 2987, 2819, 2819, 834, [89, 78, 72], 258, [96, 83, 75], [95, 81, 68], 1973, [90, 77, 61], [87, 74, 58], 3400, [79, 70, 63], 2794, 2793, [87, 78, 71], 3087, 2794, 3402, 3402, 3402, 2793, 3411, [105, 96, 89], 725, 3414, 159, [35, 25, 16], 3419, 3419, [32, 22, 12], [45, 32, 23], [55, 43, 29], [59, 43, 27], 1052, [114, 94, 69], [146, 123, 92], [180, 151, 119], [195, 162, 129], [200, 161, 128], [204, 163, 131], [213, 168, 137], [216, 171, 140], [217, 172, 139], [222, 178, 143], [228, 184, 149], [231, 187, 152], [231, 188, 154], 3536, 3238, 3138, 3440, 3440, 3342, 3243, [241, 202, 171], [245, 206, 175], [246, 209, 180], 3149, [250, 214, 190], [249, 214, 192], 3155, 2955, [251, 221, 197], [251, 223, 199], [251, 225, 200], [252, 226, 201], [248, 222, 197], [247, 221, 196], [247, 219, 195], [249, 219, 195], 2954, 3056, [255, 223, 201], [255, 226, 205], 2958, [252, 225, 204], 3063, 3466, [240, 205, 175], [235, 196, 163], [231, 191, 156], [228, 188, 152], [243, 203, 167], [241, 203, 167], [249, 213, 179], [224, 191, 160], [147, 120, 91], [92, 66, 41], [91, 69, 45], [111, 89, 65], [112, 90, 66], [123, 103, 79], [121, 104, 84], [97, 84, 67], 246, 3303, [82, 75, 67], [75, 68, 60], [88, 79, 72], [90, 81, 74], [95, 82, 76], [94, 81, 75], [90, 79, 75], [89, 78, 74], [91, 80, 78], 256, 1230, [95, 82, 66], 1037, 3498, [79, 70, 65], 732, [85, 76, 71], [88, 79, 74], 3603, [86, 77, 72], [84, 75, 70], 732, 3089, 3089, 3603, 441, [102, 93, 88], [118, 109, 104], 629, 60, [32, 22, 13], [51, 41, 31], [60, 50, 41], 1824, [43, 30, 21], 2721, [63, 47, 32], [94, 77, 57], [125, 105, 80], [148, 124, 96], [172, 143, 111], [182, 149, 116], [192, 153, 120], [205, 164, 132], [217, 172, 143], [220, 175, 144], [220, 175, 142], [224, 180, 145], [230, 185, 152], [234, 189, 156], 3238, 3238, [235, 192, 160], [236, 193, 161], [234, 193, 163], [235, 194, 164], 3242, [238, 199, 168], [242, 203, 174], [247, 208, 179], [249, 212, 183], [250, 215, 187], 2953, 3057, [254, 224, 200], [253, 225, 201], [254, 226, 202], [254, 228, 203], 3653, [254, 230, 204], [249, 225, 199], [249, 223, 198], 3657, 3456, 2863, [255, 227, 203], [255, 229, 206], [255, 231, 210], [255, 235, 216], [255, 233, 216], 2960, [250, 222, 200], [246, 214, 189], [239, 205, 177], [233, 198, 168], [229, 193, 161], [230, 194, 160], [236, 200, 166], [240, 205, 173], [246, 213, 180], [227, 196, 167], [168, 139, 109], [121, 91, 63], [116, 90, 63], [108, 84, 58], [111, 91, 66], [115, 98, 78], 3483, [80, 70, 58], 2317, [85, 78, 70], [77, 70, 64], 630, [96, 87, 82], [101, 87, 84], [100, 86, 83], [96, 85, 83], [95, 83, 83], [97, 85, 87], [99, 88, 86], [99, 86, 77], [97, 85, 71], 1321, 1428, [81, 72, 67], 3186, 3602, 3504, 3089, 3504, 3602, 3189, [91, 82, 77], 3411, 3708, 3504, [95, 86, 81], 724, 2893, [60, 51, 44], [47, 37, 28], [55, 45, 35], [58, 48, 39], [40, 30, 21], [37, 24, 15], 2721, 1350, [97, 80, 60], [118, 98, 74], [138, 114, 86], [161, 132, 100], [177, 144, 111], [195, 156, 125], [210, 169, 137], [221, 176, 147], [222, 177, 146], [224, 179, 146], 3038, [231, 186, 153], [235, 190, 157], [236, 193, 159], 3138, [237, 194, 162], 3738, 3641, [236, 195, 165], 3243, 3244, [245, 206, 177], [249, 210, 181], 2948, 3050, 2952, [255, 225, 201], 3749, 3651, 3651, 3555, 3555, 3555, 3554, 3554, 3555, 3652, 2755, [255, 231, 207], [255, 233, 209], [255, 233, 212], [255, 235, 218], 3665, [255, 230, 211], 2958, [251, 223, 201], [247, 217, 191], [241, 210, 182], [236, 205, 176], [221, 188, 157], [238, 205, 174], [232, 199, 168], [234, 201, 170], [249, 216, 185], [214, 181, 150], [143, 110, 79], [102, 73, 43], [103, 79, 51], [103, 83, 58], [118, 101, 81], 964, [87, 77, 65], 3288, 341, [82, 75, 69], [96, 86, 84], 3091, [104, 90, 89], [105, 91, 91], [102, 90, 90], [103, 91, 93], [105, 93, 97], [108, 96, 98], [106, 93, 87], 1419, [100, 87, 78], 1318, 3501, 2317, 3186, 158, 2793, 157, 2989, 56, 3589, [96, 87, 78], [96, 87, 80], 57, 2989, [103, 94, 85], 3504, [48, 39, 30], [52, 42, 33], [42, 32, 23], [43, 32, 26], [33, 23, 14], [33, 20, 11], 2777, [76, 60, 45], [93, 76, 56], [107, 87, 63], [128, 104, 76], [155, 126, 96], 3727, [197, 158, 127], [210, 169, 139], [222, 177, 148], [227, 182, 151], [228, 183, 150], [229, 184, 151], [232, 187, 154], 3735, 3639, [238, 195, 163], 2456, 2456, 2457, [238, 197, 167], [239, 200, 171], 3644, [247, 207, 181], [252, 212, 186], [254, 217, 190], [255, 220, 192], 2953, [253, 221, 196], [252, 220, 195], [250, 220, 194], [249, 219, 193], [245, 218, 191], [244, 217, 190], 3854, [248, 221, 194], [249, 222, 195], [251, 224, 197], 2656, 2660, [255, 228, 203], 3861, 2758, [255, 225, 207], [253, 224, 208], [254, 225, 207], [255, 227, 209], [255, 230, 209], [255, 228, 206], 3553, [247, 220, 193], [235, 205, 177], [239, 210, 180], [232, 201, 172], 3774, [250, 217, 186], 3265, [189, 154, 124], [124, 93, 64], [99, 73, 46], [91, 71, 46], [114, 98, 75], [126, 113, 96], 2079, 2317, [84, 77, 71], [85, 77, 74], [94, 84, 83], [97, 87, 86], [104, 90, 90], [107, 92, 95], [106, 94, 98], [108, 95, 102], [112, 99, 108], [116, 103, 110], [116, 105, 103], [114, 103, 99], [111, 100, 96], [108, 97, 93], 2693, 2693, 146, 46, 2790, 155, 55, 1684, [88, 80, 69], 729, [99, 91, 80], 2985, 54, 729, 2515, [40, 32, 21], [27, 17, 8], [25, 15, 6], [46, 35, 29], [45, 34, 28], 1744, 3279, 1349, [87, 70, 52], [102, 82, 58], [123, 99, 71], [152, 123, 93], [176, 143, 112], [193, 154, 123], [206, 165, 135], 3830, [232, 187, 158], [231, 186, 155], 3734, 3834, 3635, 3639, [239, 196, 164], [239, 195, 166], 3938, 3841, [239, 198, 168], [240, 201, 172], [243, 204, 175], [248, 208, 182], [253, 213, 187], [255, 219, 192], [255, 221, 195], [253, 217, 193], [251, 217, 192], [249, 215, 190], [247, 213, 188], 3364, [241, 209, 184], [242, 208, 183], [239, 207, 182], [246, 212, 187], 3668, 3152, 2854, 2953, [255, 223, 198], 2953, [255, 220, 198], [250, 215, 196], [248, 215, 198], [251, 220, 200], [255, 226, 206], 3763, [255, 236, 213], [255, 233, 210], [255, 229, 204], [254, 228, 201], [235, 208, 179], [234, 204, 176], [242, 211, 182], [248, 214, 186], [249, 214, 184], [206, 169, 140], [130, 96, 68], [95, 69, 42], [79, 59, 34], [103, 87, 64], [128, 115, 96], [107, 97, 85], 2889, [87, 80, 74], [83, 75, 72], [91, 81, 80], [95, 85, 84], [103, 88, 91], 3891, 3893, [111, 98, 105], [116, 103, 113], [120, 107, 116], [127, 115, 119], [125, 113, 113], [122, 110, 112], [120, 108, 108], 50, 48, 48, 50, 153, 2883, 730, 1820, [88, 80, 67], [99, 91, 78], 2278, [84, 76, 63], [95, 87, 74], [96, 88, 75], [67, 59, 46], [34, 26, 13], [29, 19, 10], [39, 28, 22], [40, 29, 23], 4017, [47, 34, 26], [51, 38, 29], [60, 44, 31], [73, 56, 38], [96, 76, 52], [118, 94, 68], 2261, [163, 130, 99], [191, 152, 121], [219, 178, 148], [231, 185, 159], [225, 180, 151], [228, 183, 152], [230, 185, 154], [234, 189, 158], [235, 190, 159], [234, 190, 161], [235, 191, 162], 2456, [240, 196, 167], [239, 198, 170], [241, 200, 172], [243, 203, 177], [250, 210, 184], [255, 216, 190], 2850, [255, 218, 192], [252, 215, 189], [249, 211, 188], [250, 212, 189], 4048, 3448, [240, 202, 179], [237, 199, 176], [239, 198, 176], [238, 200, 177], [245, 204, 182], 3448, [250, 209, 187], [248, 210, 187], [247, 209, 186], 4060, 4049, [252, 214, 191], [247, 209, 188], 3263, [243, 206, 187], [247, 215, 194], 2958, [255, 237, 215], [255, 242, 219], [255, 242, 216], 4071, [255, 236, 209], 3871, 3974, [234, 200, 173], 3050, [236, 199, 172], [158, 124, 96], [96, 70, 43], [68, 48, 23], [78, 62, 39], [121, 108, 89], 826, 3305, 219, 3887, [88, 78, 77], [93, 83, 82], [102, 87, 90], [105, 90, 93], [106, 93, 100], [113, 100, 107], [125, 112, 122], [135, 122, 132], [138, 127, 135], 637, [148, 137, 145], 636, 2085, 2085, 49, 49, 2277, 2502, 2378, [87, 80, 64], 2378, 2280, 4107, 2184, [92, 85, 69], [89, 82, 66], [61, 54, 38], 4015, 4016, [38, 27, 21], 4018, 4018, [48, 35, 29], [49, 36, 27], [55, 39, 26], [65, 48, 30], [98, 77, 56], [117, 93, 67], [141, 111, 83], [164, 130, 102], [192, 153, 124], [217, 176, 146], [229, 183, 157], [226, 180, 154], 3830, [223, 178, 147], [226, 181, 150], 3932, 4037, 3938, [243, 199, 170], [244, 200, 171], [238, 197, 169], [240, 199, 171], [245, 205, 179], [251, 211, 185], [255, 215, 189], 4044, [253, 216, 190], 2849, [255, 214, 192], [254, 212, 190], [250, 208, 186], [244, 202, 180], [240, 195, 174], [236, 191, 170], [235, 190, 169], 4154, [239, 194, 173], [241, 196, 175], [243, 198, 177], 4158, [240, 198, 176], 4160, [243, 201, 179], [246, 204, 182], [244, 199, 176], [244, 202, 178], [246, 205, 183], [247, 211, 187], 3950, [248, 218, 192], [245, 219, 194], [243, 219, 193], [236, 212, 186], [246, 220, 195], [254, 227, 200], 3851, [233, 201, 176], [236, 202, 175], [232, 196, 170], [187, 153, 126], [105, 79, 52], [70, 50, 25], [67, 51, 28], [105, 92, 75], [117, 107, 95], 157, [95, 88, 82], [86, 78, 75], [90, 80, 79], 3889, [106, 92, 92], [109, 94, 97], [108, 96, 100], [115, 102, 109], [127, 114, 123], [137, 124, 134], [145, 133, 145], [154, 144, 155], [161, 149, 161], [157, 147, 158], 2303, 2303, 2277, 2277, 2303, 2501, 2184, 2784, 2184, [86, 79, 63], 2502, 2502, [88, 81, 65], 2085, [52, 45, 29], [35, 27, 14], [34, 24, 15], [42, 31, 25], [43, 32, 28], [41, 30, 24], [45, 32, 26], [44, 31, 23], 2674, [66, 49, 31], [96, 75, 54], [112, 88, 62], [138, 108, 80], [166, 132, 104], [195, 156, 127], [214, 173, 145], [225, 179, 155], [225, 179, 153], [227, 182, 153]]
[150, 100]
As you can see, it has both integer and list values. Then, I pass the file into my image_decoding.py script. Here’s how it looks:
# imports
import matplotlib.pyplot as plt
# decoding
f = open("image_code.txt", "r")
Lines = f.readlines()
size = Lines[1].strip('][').split(', ')
image_coding = Lines[0][:-2].strip('][').split(', ')
size[0], size[1] = int(size[0]), int(size[1])
f.close()
print('started reshaping the list')
for i in range(len(image_coding)):
try:
value = image_coding[i]
if value[0] == '[':
image_coding[i] = [int(image_coding[i][1:]), int(
image_coding[i + 1]), int(image_coding[i + 2][:-1])]
if i != 0 and type(image_coding[i - 1]) != list and type(image_coding[i]) != list:
try:
image_coding[i] = int(image_coding[i])
except ValueError:
pass
except IndexError:
break
counter = 1
for j in image_coding:
if type(j) != int and j[-1] == ']':
image_coding.remove(j)
print(
f'Stage 1: {counter} / {len(image_coding)} ({str(counter / len(image_coding) * 100)[:7]}%) done', end='r')
counter += 1
counter = 1
for l in image_coding:
if type(l) == str:
image_coding.remove(l)
print(
f'Stage 2: {counter} / {len(image_coding)} ({str(counter / len(image_coding) * 100)[:7]}%) done', end='r')
counter += 1
print('started decoding')
i = 0
image = []
for pixel in image_coding:
i += 1
print(f'Decoding: {str(i / len(image_coding) * 100)[:7]}% done', end='r')
if type(pixel) == list:
image.append(pixel)
else:
image.append(image_coding[pixel])
# unflattening the image to display it correctly
print('nstarted unflattening')
new_image = []
for i in range(size[0]):
imageline = []
for j in range(size[1]):
imageline.append(image[i*size[1] + j])
new_image.append(imageline)
image = new_image
plt.imshow(image)
plt.show()
When running the script on the 50x100.jpg image (which looks like this):
The code works flawlessly. However, when I try to run it with the 100x100.jpg image, 100x150.jpg image or 300x200.jpg image, which look like this:
image_decoding.py returns the following TypeError:
Traceback (most recent call last):
File "c:UserspcDesktopProgrammingimageCodingimage_decoding.py", line 82, in <module>
plt.imshow(image)
File "C:UserspcAppDataLocalProgramsPythonPython310libsite-packagesmatplotlib_apideprecation.py", line 454, in wrapper
return func(*args, **kwargs)
File "C:UserspcAppDataLocalProgramsPythonPython310libsite-packagesmatplotlibpyplot.py", line 2611, in imshow
__ret = gca().imshow(
File "C:UserspcAppDataLocalProgramsPythonPython310libsite-packagesmatplotlib_apideprecation.py", line 454, in wrapper
return func(*args, **kwargs)
File "C:UserspcAppDataLocalProgramsPythonPython310libsite-packagesmatplotlib__init__.py", line 1423, in inner
return func(ax, *map(sanitize_sequence, args), **kwargs)
File "C:UserspcAppDataLocalProgramsPythonPython310libsite-packagesmatplotlibaxes_axes.py", line 5572, in imshow
im.set_data(X)
File "C:UserspcAppDataLocalProgramsPythonPython310libsite-packagesmatplotlibimage.py", line 701, in set_data
raise TypeError("Image data of dtype {} cannot be converted to "
TypeError: Image data of dtype object cannot be converted to float
P. S. There might be some really stupid bug in the code (e.g. missing bracket), I’m still quite new to this 🙂
It’s quite a lot of code you have there… I hope my answer is clear without reposting everything 🙂
Your bug is in the file reading and parsing (recreating the original data-types etc.), so I have the following suggestion for you.
Python comes with a package called pickle that you can use to store python objects and read them back into memory.
I’d suggest the following changes to your code.
In image_encoding.py replace
f = open("image_code.txt", "w")
f.write(str(image_coding))
f.close()
f = open("image_code.txt", "a")
f.write(f'n{size}')
f.close()
with
import pickle # put that at the top of your file
with open("image_code.txt", "wb") as file:
pickle.dump({"image_coding": image_coding, "size": size}, file)
And then in image_decoding.py instead of
f = open("image_code.txt", "r")
Lines = f.readlines()
size = Lines[1].strip('][').split(', ')
image_coding = Lines[0][:-2].strip('][').split(', ')
size[0], size[1] = int(size[0]), int(size[1])
f.close()
print('started reshaping the list')
for i in range(len(image_coding)):
try:
value = image_coding[i]
if value[0] == '[':
image_coding[i] = [int(image_coding[i][1:]), int(
image_coding[i + 1]), int(image_coding[i + 2][:-1])]
if i != 0 and type(image_coding[i - 1]) != list and type(image_coding[i]) != list:
try:
image_coding[i] = int(image_coding[i])
except ValueError:
pass
except IndexError:
break
you can use:
import pickle
with open("test_data/image_code.txt", "rb") as file:
data = pickle.load(file)
size = data.get("size")
image_coding = data.get("image_coding")
and have your original variables back without checking for "[" and casting back to integers etc.
Hope that helps. If you have any question, dont hesitate to ask.