BGR8 raw image conversion to numpy python

Question

So far i have tested acquiring images from a camera in the raw BGR8 format into a numpy array, I am at the point in which i can get access to the data however the image seems to have visible image artifacts (vertical lines etc) and only displays in greyscale.

The following code is used to acquire the image in BGR8 format:

image=ctrl.GetImageWindow(100,100, 20,20) # offset 100,100, 20x20 grid of pixels

data = numpy.array(image)

Data returns the following 20-wide and 60-long numpy array - from some testing, the first "row" is Blue, the second, Green, the third Red recursively

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

The output of cv2.imwrite("test-raw.png", data) is the following image which is grey (it should have a red line)

I also tried adding in data = numpy.array(image).view(numpy.uint8) and the output was the following:

Data returns the following 80-wide and 60-long numpy array

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
53  0   0   0   52  0   0   0   50  0   0   0   53  0   0   0   51  0   0   0   48  0   0   0   52  0   0   0   48  0   0   0   46  0   0   0   51  0   0   0   43  0   0   0   45  0   0   0   50  0   0   0   48  0   0   0   55  0   0   0   51  0   0   0   61  0   0   0   49  0   0   0   48  0   0   0   42  0   0   0
54  0   0   0   69  0   0   0   50  0   0   0   51  0   0   0   43  0   0   0   60  0   0   0   51  0   0   0   54  0   0   0   45  0   0   0   57  0   0   0   50  0   0   0   60  0   0   0   62  0   0   0   38  0   0   0   50  0   0   0   48  0   0   0   48  0   0   0   44  0   0   0   56  0   0   0   59  0   0   0
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
44  0   0   0   53  0   0   0   57  0   0   0   51  0   0   0   45  0   0   0   51  0   0   0   41  0   0   0   53  0   0   0   45  0   0   0   56  0   0   0   43  0   0   0   52  0   0   0   51  0   0   0   47  0   0   0   54  0   0   0   48  0   0   0   51  0   0   0   57  0   0   0   49  0   0   0   46  0   0   0
55  0   0   0   36  0   0   0   43  0   0   0   44  0   0   0   53  0   0   0   42  0   0   0   46  0   0   0   48  0   0   0   66  0   0   0   48  0   0   0   54  0   0   0   61  0   0   0   60  0   0   0   39  0   0   0   42  0   0   0   51  0   0   0   44  0   0   0   47  0   0   0   69  0   0   0   55  0   0   0
49  0   0   0   54  0   0   0   48  0   0   0   52  0   0   0   50  0   0   0   56  0   0   0   52  0   0   0   58  0   0   0   48  0   0   0   49  0   0   0   50  0   0   0   44  0   0   0   49  0   0   0   51  0   0   0   47  0   0   0   48  0   0   0   49  0   0   0   51  0   0   0   46  0   0   0   52  0   0   0
47  0   0   0   50  0   0   0   60  0   0   0   53  0   0   0   52  0   0   0   53  0   0   0   54  0   0   0   55  0   0   0   40  0   0   0   51  0   0   0   49  0   0   0   47  0   0   0   40  0   0   0   49  0   0   0   49  0   0   0   47  0   0   0   52  0   0   0   47  0   0   0   43  0   0   0   50  0   0   0
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
37  0   0   0   52  0   0   0   51  0   0   0   52  0   0   0   54  0   0   0   53  0   0   0   51  0   0   0   51  0   0   0   62  0   0   0   62  0   0   0   57  0   0   0   51  0   0   0   57  0   0   0   55  0   0   0   43  0   0   0   43  0   0   0   37  0   0   0   49  0   0   0   52  0   0   0   52  0   0   0
54  0   0   0   61  0   0   0   58  0   0   0   38  0   0   0   50  0   0   0   48  0   0   0   57  0   0   0   58  0   0   0   50  0   0   0   48  0   0   0   51  0   0   0   48  0   0   0   33  0   0   0   46  0   0   0   52  0   0   0   38  0   0   0   37  0   0   0   52  0   0   0   46  0   0   0   50  0   0   0
50  0   0   0   48  0   0   0   39  0   0   0   49  0   0   0   54  0   0   0   47  0   0   0   40  0   0   0   44  0   0   0   52  0   0   0   54  0   0   0   41  0   0   0   48  0   0   0   26  0   0   0   33  0   0   0   51  0   0   0   59  0   0   0   55  0   0   0   48  0   0   0   49  0   0   0   52  0   0   0
44  0   0   0   48  0   0   0   63  0   0   0   59  0   0   0   52  0   0   0   50  0   0   0   45  0   0   0   49  0   0   0   58  0   0   0   63  0   0   0   57  0   0   0   47  0   0   0   59  0   0   0   57  0   0   0   55  0   0   0   44  0   0   0   46  0   0   0   46  0   0   0   62  0   0   0   58  0   0   0
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
50  0   0   0   24  0   0   0   43  0   0   0   45  0   0   0   46  0   0   0   62  0   0   0   51  0   0   0   73  0   0   0   45  0   0   0   53  0   0   0   75  0   0   0   51  0   0   0   44  0   0   0   40  0   0   0   63  0   0   0   59  0   0   0   42  0   0   0   47  0   0   0   63  0   0   0   38  0   0   0
54  0   0   0   63  0   0   0   50  0   0   0   57  0   0   0   56  0   0   0   59  0   0   0   44  0   0   0   42  0   0   0   47  0   0   0   44  0   0   0   35  0   0   0   55  0   0   0   46  0   0   0   49  0   0   0   36  0   0   0   43  0   0   0   53  0   0   0   59  0   0   0   55  0   0   0   50  0   0   0
47  0   0   0   51  0   0   0   52  0   0   0   55  0   0   0   56  0   0   0   47  0   0   0   49  0   0   0   55  0   0   0   60  0   0   0   59  0   0   0   59  0   0   0   56  0   0   0   66  0   0   0   73  0   0   0   59  0   0   0   61  0   0   0   51  0   0   0   47  0   0   0   44  0   0   0   53  0   0   0
61  0   0   0   54  0   0   0   67  0   0   0   57  0   0   0   42  0   0   0   40  0   0   0   55  0   0   0   72  0   0   0   49  0   0   0   45  0   0   0   60  0   0   0   60  0   0   0   57  0   0   0   44  0   0   0   52  0   0   0   52  0   0   0   51  0   0   0   47  0   0   0   46  0   0   0   39  0   0   0
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
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60  0   0   0   57  0   0   0   51  0   0   0   57  0   0   0   47  0   0   0   46  0   0   0   44  0   0   0   36  0   0   0   43  0   0   0   37  0   0   0   28  0   0   0   17  0   0   0   17  0   0   0   19  0   0   0   12  0   0   0   0   0   0   0   0   0   0   0   21  0   0   0   14  0   0   0   16  0   0   0
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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

EDIT for more context

After some reading into opencv and the following answer on SO i believe that it's a case of rotating the numpy array to match the default format

Adding a larger image and transposing based on the above assumption ends up with the same format as opencv is expecting rows-to-cols, but the image is still greyscale, is squished & has artifacts as below (the text should be red and it should be taller as an image)

data = data.transpose()

EDIT: Resultant code looks like:

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()

OUTPUT

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

It's still grayscale (and 3x wider than it ought to be), since you still have it as a single channel image. You need 3 channels for BGR. https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.reshape.html — Dan Mašek, May 03 '18 at 15:42
I think [this](https://pastebin.com/8NxjGDr2) does it, but the output image is rather weird... is [this](https://i.imgur.com/sPbyxN9.png) what you expect? — Dan Mašek, May 03 '18 at 16:04
For the "TEST" image, simply `image.reshape((768,-1,3))` -- https://i.imgur.com/2u5wzMy.png — Dan Mašek, May 03 '18 at 16:10
I get an error: (-206) Unrecognized or unsupported array type in function cvGetMat — sman0307, May 03 '18 at 16:31
[Edit] the question and append the new stuff at the end, please. Define "doesn't seem to work" -- some errors? Or does it not produce a meaningful result? Are at least the individual colour planes (b,g,r) looking right? — Dan Mašek, May 03 '18 at 16:33
Hmm. I'd really love to reproduce this here, what's `ctrl` (which object from which library). Can you perhaps provide some [mcve] that includes it as well? Or just dump the raw data you get from `GetImageWindow` into a file and upload it somewhere. — Dan Mašek, May 03 '18 at 17:09
@Dan, managed to get this working, will post further details for future reference on this. Thanks for the help! — sman0307, May 03 '18 at 19:42

score 0 · Accepted Answer · answered Aug 01 '18 at 12:08

0

Working example is:

image=ctrl.GetImageWindow(0,0, w,h)
data = numpy.array(image, dtype=numpy.uint8).reshape(768,-1,3)

answered Aug 01 '18 at 12:08

sman0307

198
1
9

BGR8 raw image conversion to numpy python

1 Answers1