【发布时间】：2018-05-03 14:23:42
【问题描述】：
到目前为止，我已经测试了将原始 BGR8 格式的相机中的图像获取到一个 numpy 数组中，我现在可以访问数据，但是图像似乎有可见的图像伪影（垂直线等) 并且仅以灰度显示。
以下代码用于获取BGR8格式的图片：
image=ctrl.GetImageWindow(100,100, 20,20) # offset 100,100, 20x20 grid of pixels
data = numpy.array(image)
Data 返回以下 20 宽和 60 长的 numpy 数组 - 从一些测试中，第一个“行”是蓝色，第二个，绿色，第三个红色递归
49 48 49 57 68 76 62 59 46 54 62 58 68 64 45 60 65 51 56 70
76 72 62 62 66 59 65 62 53 65 62 67 75 58 59 57 67 64 64 63
54 64 55 67 67 61 64 43 66 60 59 73 48 74 88 77 65 54 69 57
80 59 42 56 79 51 53 67 64 40 53 68 74 83 60 81 53 37 42 72
61 71 73 75 79 63 64 66 70 60 64 61 68 64 56 60 60 61 67 61
60 62 69 83 66 64 76 63 62 72 66 70 58 61 77 83 76 71 75 63
58 75 74 61 67 54 58 59 55 46 54 61 52 81 56 59 53 66 45 50
49 60 67 63 64 66 76 63 69 62 71 66 67 63 57 55 61 54 63 63
74 62 64 73 59 64 56 68 67 54 65 70 60 52 53 59 71 66 63 68
34 56 53 57 65 52 65 65 75 73 72 59 40 61 64 72 54 72 66 55
59 63 65 69 63 60 70 68 67 59 60 69 69 74 69 64 64 60 63 66
75 66 73 61 52 65 53 58 58 44 51 56 75 56 61 53 52 62 62 60
54 41 39 38 49 29 48 58 60 72 56 53 52 57 66 68 65 70 54 77
59 69 59 78 70 66 71 63 76 74 67 63 64 63 59 68 68 61 63 55
68 63 68 64 53 65 63 63 49 55 53 60 60 51 66 69 49 55 54 52
71 61 58 47 69 48 45 55 51 69 65 72 79 58 77 60 65 69 56 62
61 46 54 62 75 77 68 64 73 69 66 64 55 63 68 62 65 71 67 59
72 69 69 63 68 64 59 59 70 65 55 69 46 54 70 66 62 60 65 52
61 57 67 71 85 62 50 73 63 80 59 71 105 78 57 80 73 74 79 70
66 54 65 60 55 55 66 56 55 57 68 66 51 64 49 47 51 53 62 66
73 57 61 63 72 73 61 68 52 64 58 62 58 61 69 72 69 82 80 60
77 61 69 57 76 59 40 57 55 62 60 45 71 57 64 54 81 81 63 71
68 59 66 54 73 64 78 69 63 66 73 72 57 74 56 55 50 48 61 62
52 61 68 64 71 70 71 74 67 70 52 69 56 63 81 62 55 64 72 62
76 75 75 54 79 49 46 42 31 51 48 56 65 55 58 43 61 75 52 69
60 54 56 63 65 69 79 68 67 72 64 66 71 67 62 61 66 61 67 58
58 63 68 63 56 59 63 55 58 62 52 70 60 68 71 67 59 63 61 59
86 59 56 78 66 67 46 69 45 52 70 73 58 71 67 50 55 51 60 71
64 65 58 52 60 68 63 59 71 85 57 53 64 62 69 60 54 62 70 58
51 53 71 60 59 76 80 69 64 76 74 62 67 61 56 57 69 66 62 67
57 65 73 54 55 52 50 54 65 78 70 62 59 77 71 57 69 55 81 78
60 70 51 61 75 72 66 57 59 51 61 62 63 61 65 67 57 67 70 56
62 56 82 68 58 56 74 63 65 69 68 69 79 74 65 53 61 58 64 70
44 56 63 75 58 55 65 72 73 65 65 76 76 56 51 76 75 63 51 56
73 73 61 69 67 74 68 66 64 59 73 60 68 69 63 66 59 66 62 53
57 62 73 65 62 61 57 50 63 75 68 58 54 65 74 54 68 60 71 63
64 50 81 67 39 36 70 84 77 68 64 56 63 66 73 81 63 50 22 24
74 71 59 60 77 71 49 51 59 58 69 77 65 72 51 53 48 53 40 27
55 53 56 62 65 73 68 74 69 67 68 47 43 58 72 63 67 56 69 60
61 61 78 67 47 70 88 76 70 76 80 70 60 72 52 67 64 36 25 9
70 79 53 59 75 73 59 50 51 60 62 68 74 61 59 56 44 31 20 5
62 49 51 67 65 54 67 76 74 71 57 54 61 48 64 64 64 57 63 63
61 67 69 60 46 68 60 58 54 73 72 75 68 59 46 55 43 34 32 5
63 68 58 71 64 49 60 68 52 56 63 56 56 66 52 43 35 24 14 6
49 56 57 64 71 66 68 62 71 65 66 57 68 60 66 72 67 59 64 60
76 74 69 45 41 68 66 68 76 77 50 67 65 77 62 46 28 21 25 7
64 59 63 64 73 67 62 58 55 48 63 61 60 40 33 22 26 20 19 23
57 56 59 69 75 73 49 72 67 69 70 68 70 58 57 56 63 58 56 54
79 56 68 66 43 73 67 63 74 65 75 50 38 42 42 24 23 4 22 27
60 55 63 67 66 57 70 64 71 58 48 46 44 18 20 8 13 13 21 20
60 58 61 72 64 62 62 55 58 72 68 64 72 63 67 67 66 66 67 57
69 58 55 65 48 60 64 64 68 57 50 22 18 44 24 18 24 19 34 35
64 68 66 73 72 71 65 60 67 48 39 22 25 9 1 8 17 21 20 22
62 61 61 54 62 60 62 53 50 59 58 64 67 63 72 74 71 59 59 45
75 50 55 57 81 64 72 60 59 40 29 19 18 47 27 20 24 25 19 43
68 68 69 65 57 64 63 44 51 47 29 14 16 6 0 16 25 24 13 17
65 76 69 56 62 67 70 71 62 62 67 75 70 61 66 74 63 57 68 59
72 66 70 70 65 60 81 41 25 32 20 21 6 10 17 17 27 18 29 43
58 54 60 57 54 58 49 27 32 27 13 2 18 14 17 22 25 26 32 22
67 75 75 74 81 76 66 79 80 58 60 65 60 63 55 42 55 60 66 79
cv2.imwrite("test-raw.png", data) 的输出如下图是灰色的（应该有一条红线）
我还尝试添加data = numpy.array(image).view(numpy.uint8)，输出如下：
Data 返回以下 80 宽和 60 长的 numpy 数组
49 0 0 0 62 0 0 0 49 0 0 0 45 0 0 0 58 0 0 0 63 0 0 0 51 0 0 0 59 0 0 0 50 0 0 0 45 0 0 0 45 0 0 0 38 0 0 0 59 0 0 0 62 0 0 0 42 0 0 0 39 0 0 0 45 0 0 0 57 0 0 0 74 0 0 0 58 0 0 0
51 0 0 0 48 0 0 0 44 0 0 0 56 0 0 0 51 0 0 0 40 0 0 0 40 0 0 0 33 0 0 0 52 0 0 0 45 0 0 0 43 0 0 0 55 0 0 0 50 0 0 0 51 0 0 0 51 0 0 0 57 0 0 0 49 0 0 0 42 0 0 0 36 0 0 0 54 0 0 0
41 0 0 0 44 0 0 0 48 0 0 0 50 0 0 0 50 0 0 0 59 0 0 0 47 0 0 0 45 0 0 0 52 0 0 0 49 0 0 0 54 0 0 0 58 0 0 0 47 0 0 0 48 0 0 0 51 0 0 0 49 0 0 0 42 0 0 0 52 0 0 0 50 0 0 0 45 0 0 0
61 0 0 0 48 0 0 0 61 0 0 0 44 0 0 0 50 0 0 0 66 0 0 0 41 0 0 0 57 0 0 0 59 0 0 0 61 0 0 0 48 0 0 0 54 0 0 0 66 0 0 0 60 0 0 0 60 0 0 0 45 0 0 0 63 0 0 0 49 0 0 0 51 0 0 0 53 0 0 0
53 0 0 0 47 0 0 0 46 0 0 0 49 0 0 0 49 0 0 0 54 0 0 0 53 0 0 0 54 0 0 0 52 0 0 0 45 0 0 0 52 0 0 0 45 0 0 0 47 0 0 0 45 0 0 0 43 0 0 0 55 0 0 0 69 0 0 0 57 0 0 0 49 0 0 0 49 0 0 0
47 0 0 0 58 0 0 0 44 0 0 0 53 0 0 0 52 0 0 0 57 0 0 0 56 0 0 0 54 0 0 0 46 0 0 0 54 0 0 0 54 0 0 0 51 0 0 0 47 0 0 0 35 0 0 0 50 0 0 0 58 0 0 0 50 0 0 0 40 0 0 0 51 0 0 0 48 0 0 0
53 0 0 0 33 0 0 0 47 0 0 0 35 0 0 0 34 0 0 0 32 0 0 0 31 0 0 0 54 0 0 0 69 0 0 0 51 0 0 0 54 0 0 0 50 0 0 0 62 0 0 0 44 0 0 0 41 0 0 0 37 0 0 0 47 0 0 0 48 0 0 0 46 0 0 0 49 0 0 0
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71 0 0 0 36 0 0 0 40 0 0 0 48 0 0 0 37 0 0 0 43 0 0 0 41 0 0 0 33 0 0 0 39 0 0 0 37 0 0 0 63 0 0 0 54 0 0 0 53 0 0 0 48 0 0 0 45 0 0 0 50 0 0 0 37 0 0 0 47 0 0 0 57 0 0 0 49 0 0 0
48 0 0 0 55 0 0 0 50 0 0 0 56 0 0 0 53 0 0 0 55 0 0 0 48 0 0 0 56 0 0 0 52 0 0 0 51 0 0 0 46 0 0 0 45 0 0 0 54 0 0 0 58 0 0 0 49 0 0 0 46 0 0 0 48 0 0 0 49 0 0 0 52 0 0 0 52 0 0 0
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60 0 0 0 40 0 0 0 40 0 0 0 46 0 0 0 56 0 0 0 49 0 0 0 36 0 0 0 46 0 0 0 61 0 0 0 49 0 0 0 38 0 0 0 42 0 0 0 30 0 0 0 47 0 0 0 60 0 0 0 73 0 0 0 77 0 0 0 67 0 0 0 54 0 0 0 54 0 0 0
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50 0 0 0 58 0 0 0 61 0 0 0 47 0 0 0 43 0 0 0 59 0 0 0 65 0 0 0 57 0 0 0 39 0 0 0 47 0 0 0 60 0 0 0 56 0 0 0 49 0 0 0 54 0 0 0 52 0 0 0 50 0 0 0 47 0 0 0 50 0 0 0 52 0 0 0 47 0 0 0
50 0 0 0 35 0 0 0 44 0 0 0 50 0 0 0 30 0 0 0 55 0 0 0 51 0 0 0 57 0 0 0 54 0 0 0 49 0 0 0 60 0 0 0 57 0 0 0 48 0 0 0 40 0 0 0 59 0 0 0 40 0 0 0 47 0 0 0 34 0 0 0 53 0 0 0 62 0 0 0
54 0 0 0 50 0 0 0 52 0 0 0 47 0 0 0 57 0 0 0 59 0 0 0 50 0 0 0 35 0 0 0 48 0 0 0 51 0 0 0 36 0 0 0 46 0 0 0 49 0 0 0 56 0 0 0 43 0 0 0 45 0 0 0 51 0 0 0 52 0 0 0 53 0 0 0 57 0 0 0
50 0 0 0 55 0 0 0 48 0 0 0 50 0 0 0 45 0 0 0 45 0 0 0 50 0 0 0 56 0 0 0 50 0 0 0 50 0 0 0 54 0 0 0 54 0 0 0 52 0 0 0 58 0 0 0 35 0 0 0 56 0 0 0 52 0 0 0 47 0 0 0 52 0 0 0 48 0 0 0
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55 0 0 0 51 0 0 0 45 0 0 0 52 0 0 0 54 0 0 0 59 0 0 0 52 0 0 0 44 0 0 0 41 0 0 0 45 0 0 0 48 0 0 0 55 0 0 0 54 0 0 0 40 0 0 0 42 0 0 0 51 0 0 0 52 0 0 0 59 0 0 0 52 0 0 0 51 0 0 0
41 0 0 0 49 0 0 0 56 0 0 0 50 0 0 0 41 0 0 0 52 0 0 0 54 0 0 0 45 0 0 0 58 0 0 0 53 0 0 0 58 0 0 0 47 0 0 0 55 0 0 0 62 0 0 0 66 0 0 0 55 0 0 0 53 0 0 0 44 0 0 0 44 0 0 0 48 0 0 0
50 0 0 0 52 0 0 0 62 0 0 0 62 0 0 0 43 0 0 0 62 0 0 0 47 0 0 0 50 0 0 0 51 0 0 0 51 0 0 0 52 0 0 0 61 0 0 0 66 0 0 0 52 0 0 0 36 0 0 0 49 0 0 0 50 0 0 0 44 0 0 0 46 0 0 0 48 0 0 0
50 0 0 0 48 0 0 0 44 0 0 0 51 0 0 0 54 0 0 0 58 0 0 0 54 0 0 0 48 0 0 0 54 0 0 0 53 0 0 0 48 0 0 0 48 0 0 0 50 0 0 0 53 0 0 0 45 0 0 0 48 0 0 0 49 0 0 0 53 0 0 0 53 0 0 0 53 0 0 0
55 0 0 0 61 0 0 0 56 0 0 0 60 0 0 0 50 0 0 0 39 0 0 0 41 0 0 0 47 0 0 0 56 0 0 0 54 0 0 0 53 0 0 0 49 0 0 0 42 0 0 0 55 0 0 0 55 0 0 0 53 0 0 0 53 0 0 0 53 0 0 0 53 0 0 0 54 0 0 0
56 0 0 0 62 0 0 0 65 0 0 0 59 0 0 0 60 0 0 0 52 0 0 0 52 0 0 0 52 0 0 0 58 0 0 0 67 0 0 0 59 0 0 0 56 0 0 0 51 0 0 0 32 0 0 0 35 0 0 0 40 0 0 0 41 0 0 0 39 0 0 0 42 0 0 0 46 0 0 0
58 0 0 0 50 0 0 0 49 0 0 0 56 0 0 0 50 0 0 0 54 0 0 0 62 0 0 0 47 0 0 0 56 0 0 0 51 0 0 0 54 0 0 0 54 0 0 0 56 0 0 0 54 0 0 0 56 0 0 0 51 0 0 0 40 0 0 0 55 0 0 0 51 0 0 0 45 0 0 0
40 0 0 0 52 0 0 0 45 0 0 0 51 0 0 0 57 0 0 0 49 0 0 0 41 0 0 0 33 0 0 0 51 0 0 0 51 0 0 0 52 0 0 0 40 0 0 0 50 0 0 0 52 0 0 0 44 0 0 0 59 0 0 0 56 0 0 0 59 0 0 0 45 0 0 0 50 0 0 0
45 0 0 0 34 0 0 0 54 0 0 0 48 0 0 0 46 0 0 0 50 0 0 0 64 0 0 0 50 0 0 0 54 0 0 0 53 0 0 0 70 0 0 0 62 0 0 0 36 0 0 0 46 0 0 0 60 0 0 0 54 0 0 0 39 0 0 0 57 0 0 0 47 0 0 0 32 0 0 0
62 0 0 0 56 0 0 0 48 0 0 0 48 0 0 0 50 0 0 0 49 0 0 0 50 0 0 0 64 0 0 0 55 0 0 0 48 0 0 0 48 0 0 0 53 0 0 0 56 0 0 0 54 0 0 0 60 0 0 0 54 0 0 0 49 0 0 0 48 0 0 0 40 0 0 0 27 0 0 0
51 0 0 0 49 0 0 0 51 0 0 0 56 0 0 0 56 0 0 0 41 0 0 0 37 0 0 0 40 0 0 0 50 0 0 0 61 0 0 0 50 0 0 0 41 0 0 0 52 0 0 0 56 0 0 0 50 0 0 0 54 0 0 0 60 0 0 0 51 0 0 0 56 0 0 0 45 0 0 0
50 0 0 0 46 0 0 0 53 0 0 0 47 0 0 0 37 0 0 0 41 0 0 0 51 0 0 0 60 0 0 0 53 0 0 0 50 0 0 0 70 0 0 0 78 0 0 0 61 0 0 0 65 0 0 0 49 0 0 0 32 0 0 0 40 0 0 0 62 0 0 0 31 0 0 0 8 0 0 0
55 0 0 0 60 0 0 0 53 0 0 0 54 0 0 0 55 0 0 0 57 0 0 0 51 0 0 0 49 0 0 0 52 0 0 0 45 0 0 0 38 0 0 0 41 0 0 0 45 0 0 0 45 0 0 0 66 0 0 0 54 0 0 0 54 0 0 0 38 0 0 0 26 0 0 0 4 0 0 0
42 0 0 0 50 0 0 0 52 0 0 0 54 0 0 0 56 0 0 0 45 0 0 0 39 0 0 0 49 0 0 0 47 0 0 0 58 0 0 0 53 0 0 0 60 0 0 0 48 0 0 0 45 0 0 0 48 0 0 0 48 0 0 0 53 0 0 0 52 0 0 0 54 0 0 0 48 0 0 0
51 0 0 0 52 0 0 0 43 0 0 0 45 0 0 0 50 0 0 0 38 0 0 0 45 0 0 0 45 0 0 0 43 0 0 0 55 0 0 0 68 0 0 0 69 0 0 0 57 0 0 0 66 0 0 0 59 0 0 0 48 0 0 0 42 0 0 0 35 0 0 0 30 0 0 0 18 0 0 0
48 0 0 0 46 0 0 0 55 0 0 0 58 0 0 0 54 0 0 0 58 0 0 0 53 0 0 0 50 0 0 0 53 0 0 0 38 0 0 0 47 0 0 0 46 0 0 0 40 0 0 0 49 0 0 0 49 0 0 0 38 0 0 0 30 0 0 0 24 0 0 0 18 0 0 0 1 0 0 0
38 0 0 0 51 0 0 0 48 0 0 0 59 0 0 0 46 0 0 0 50 0 0 0 44 0 0 0 49 0 0 0 62 0 0 0 56 0 0 0 46 0 0 0 52 0 0 0 46 0 0 0 47 0 0 0 42 0 0 0 51 0 0 0 52 0 0 0 48 0 0 0 49 0 0 0 49 0 0 0
48 0 0 0 47 0 0 0 52 0 0 0 63 0 0 0 68 0 0 0 51 0 0 0 42 0 0 0 35 0 0 0 59 0 0 0 64 0 0 0 49 0 0 0 45 0 0 0 39 0 0 0 57 0 0 0 53 0 0 0 33 0 0 0 33 0 0 0 28 0 0 0 15 0 0 0 1 0 0 0
44 0 0 0 41 0 0 0 50 0 0 0 55 0 0 0 47 0 0 0 56 0 0 0 56 0 0 0 48 0 0 0 43 0 0 0 52 0 0 0 47 0 0 0 48 0 0 0 50 0 0 0 45 0 0 0 39 0 0 0 37 0 0 0 20 0 0 0 13 0 0 0 18 0 0 0 8 0 0 0
47 0 0 0 63 0 0 0 51 0 0 0 45 0 0 0 51 0 0 0 48 0 0 0 42 0 0 0 55 0 0 0 54 0 0 0 50 0 0 0 53 0 0 0 50 0 0 0 45 0 0 0 53 0 0 0 47 0 0 0 56 0 0 0 52 0 0 0 49 0 0 0 48 0 0 0 55 0 0 0
43 0 0 0 31 0 0 0 45 0 0 0 56 0 0 0 55 0 0 0 63 0 0 0 49 0 0 0 20 0 0 0 39 0 0 0 40 0 0 0 49 0 0 0 43 0 0 0 23 0 0 0 30 0 0 0 30 0 0 0 21 0 0 0 8 0 0 0 18 0 0 0 22 0 0 0 9 0 0 0
56 0 0 0 51 0 0 0 56 0 0 0 38 0 0 0 47 0 0 0 51 0 0 0 55 0 0 0 55 0 0 0 45 0 0 0 57 0 0 0 47 0 0 0 51 0 0 0 47 0 0 0 37 0 0 0 22 0 0 0 17 0 0 0 22 0 0 0 13 0 0 0 14 0 0 0 5 0 0 0
51 0 0 0 58 0 0 0 52 0 0 0 60 0 0 0 51 0 0 0 40 0 0 0 35 0 0 0 65 0 0 0 59 0 0 0 56 0 0 0 49 0 0 0 45 0 0 0 53 0 0 0 52 0 0 0 57 0 0 0 54 0 0 0 49 0 0 0 54 0 0 0 61 0 0 0 63 0 0 0
23 0 0 0 50 0 0 0 51 0 0 0 63 0 0 0 49 0 0 0 51 0 0 0 51 0 0 0 18 0 0 0 59 0 0 0 57 0 0 0 40 0 0 0 38 0 0 0 23 0 0 0 7 0 0 0 8 0 0 0 15 0 0 0 2 0 0 0 10 0 0 0 11 0 0 0 24 0 0 0
55 0 0 0 58 0 0 0 59 0 0 0 51 0 0 0 46 0 0 0 48 0 0 0 53 0 0 0 52 0 0 0 48 0 0 0 46 0 0 0 35 0 0 0 32 0 0 0 15 0 0 0 11 0 0 0 16 0 0 0 11 0 0 0 13 0 0 0 15 0 0 0 8 0 0 0 10 0 0 0
52 0 0 0 45 0 0 0 50 0 0 0 57 0 0 0 48 0 0 0 50 0 0 0 45 0 0 0 67 0 0 0 56 0 0 0 56 0 0 0 56 0 0 0 41 0 0 0 55 0 0 0 52 0 0 0 52 0 0 0 49 0 0 0 57 0 0 0 63 0 0 0 58 0 0 0 50 0 0 0
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51 0 0 0 53 0 0 0 51 0 0 0 41 0 0 0 39 0 0 0 51 0 0 0 38 0 0 0 31 0 0 0 26 0 0 0 25 0 0 0 15 0 0 0 8 0 0 0 0 0 0 0 11 0 0 0 9 0 0 0 0 0 0 0 2 0 0 0 20 0 0 0 21 0 0 0 22 0 0 0
43 0 0 0 48 0 0 0 50 0 0 0 48 0 0 0 54 0 0 0 51 0 0 0 63 0 0 0 58 0 0 0 48 0 0 0 40 0 0 0 55 0 0 0 58 0 0 0 58 0 0 0 59 0 0 0 53 0 0 0 60 0 0 0 64 0 0 0 53 0 0 0 52 0 0 0 52 0 0 0
编辑以获取更多上下文
在阅读了 opencv 和以下answer on SO 之后，我相信这是旋转 numpy 数组以匹配默认格式的情况
添加更大的图像并根据上述假设进行转置最终得到与 opencv 期望行到列相同的格式，但图像仍然是灰度的，被压扁并且具有如下伪影（文本应该是红色的并且它应该比图像更高）
data = data.transpose()
编辑：结果代码如下：
image=ctrl.GetImageWindow(0,0, w,h)
data = numpy.array(image, dtype=numpy.uint8).reshape(768,-1,3)
data=data.transpose()
b,g,r = data[::3,], data[1::3,],data[2::3]
result = cv2.merge([b,g,r])
cv2.imwrite("test-raw.png", result)
cv2.imshow("test-raw", result)
cv2.waitKey()
Image Details: {'width': 2048, 'dateTime': '2018-05-03 17:35:34.564646', 'bytesPerPixel': 3, 'height': 1536, 'pixelFormat': 'BGR8'}
libpng warning: Invalid image width in IHDR
libpng warning: Image width exceeds user limit in IHDR
libpng warning: Invalid image height in IHDR
libpng warning: Image height exceeds user limit in IHDR
libpng error: Invalid IHDR data
OpenCV Error: Bad flag (parameter or structure field) (Unrecognized or unsupported array type) in cvGetMat, file C:\projects\opencv-python\opencv\modules\core\src\array.cpp, line 2493
Traceback (most recent call last):
File "ActiveGigeComTypes3.py", line 69, in <module>
cv2.imshow("test-raw", result)
cv2.error: C:\projects\opencv-python\opencv\modules\core\src\array.cpp:2493: error: (-206) Unrecognized or unsupported array type in function cvGetMat
【问题讨论】：
-
它仍然是灰度的（并且比应有的宽 3 倍），因为您仍然将它作为单通道图像。 BGR 需要 3 个通道。 docs.scipy.org/doc/numpy-1.14.0/reference/generated/…
-
对于“TEST”图像，只需
image.reshape((768,-1,3))-- i.imgur.com/2u5wzMy.png -
Edit 问题并在末尾附加新内容。定义“似乎不起作用”——一些错误？或者它不会产生有意义的结果？至少各个颜色平面（b、g、r）看起来正确吗？
-
嗯。我真的很想在这里重现这个，
ctrl是什么（哪个对象来自哪个库）。您能否提供一些包含它的minimal reproducible example？或者只是将您从GetImageWindow获得的原始数据转储到一个文件中并上传到某个地方。
标签： python numpy opencv python-imaging-library