pyimage.interpretation and pyimage.bands give different values about pyvips HOT 5 CLOSED

import pyvips
import numpy as np
format_to_dtype = {
    'uchar': np.uint8,
    'char': np.int8,
    'ushort': np.uint16,
    'short': np.int16,
    'uint': np.uint32,
    'int': np.int32,
    'float': np.float32,
    'double': np.float64,
    'complex': np.complex64,
    'dpcomplex': np.complex128,
}

**#map np dtypes to vips**
dtype_to_format = {
    'uint8': 'uchar',
    'int8': 'char',
    'uint16': 'ushort',
    'int16': 'short',
    'uint32': 'uint',
    'int32': 'int',
    'float32': 'float',
    'float64': 'double',
    'complex64': 'complex',
    'complex128': 'dpcomplex',
}
def numpy2vips(a):
    height, width, bands = a.shape
    linear = a.reshape(width * height * bands)
    vi = pyvips.Image.new_from_memory(linear.data, width, height, bands,
                                      dtype_to_format[str(a.dtype)])
    return vi
def vips2numpy(vi):
    return np.ndarray(buffer=vi.write_to_memory(),
                      dtype=format_to_dtype[vi.format],
                      shape=[vi.height, vi.width, vi.bands])
filename = '/home/Drive2/deepak/nikhil/her2_negative_svs/TCGA-AR-A24R-01Z-00-DX1.47D79205-63E7-43E6-A513-2CD10DFA53C9.svs'
Ip = pyvips.Image.new_from_file(filename)
Ip
**Out[8]:** <pyvips.Image 106623x65505 uchar, 4 bands, rgb>
**#simple vips2numpy**
def vips2numpy_vect(vi):
    return np.ndarray(buffer=vi.write_to_memory(),
                      dtype=format_to_dtype[vi.format],
                      shape=[vi.height*vi.width, vi.bands])
img = vips2numpy_vect(Ip)
img.shape
**Out[11]:** (6984339615, 4)
del img
**#vips2numpy for just taking first three channels (r,g,b)**
def vips2numpy_vect(vi):
    return np.ndarray(buffer=vi.write_to_memory(),
                      dtype=format_to_dtype[vi.format],
                      shape=[vi.height*vi.width, 3])
img = vips2numpy_vect(Ip)
img.shape
**Out[15]:** (6984339615, 3)

#!/usr/bin/python3

import sys
import pyvips
import numpy as np

format_to_dtype = {
    'uchar': np.uint8,
    'char': np.int8,
    'ushort': np.uint16,
    'short': np.int16,
    'uint': np.uint32,
    'int': np.int32,
    'float': np.float32,
    'double': np.float64,
    'complex': np.complex64,
    'dpcomplex': np.complex128,
}

#map np dtypes to vips
dtype_to_format = {
    'uint8': 'uchar',
    'int8': 'char',
    'uint16': 'ushort',
    'int16': 'short',
    'uint32': 'uint',
    'int32': 'int',
    'float32': 'float',
    'float64': 'double',
    'complex64': 'complex',
    'complex128': 'dpcomplex',
}
def numpy2vips(a):
    height, width, bands = a.shape
    linear = a.reshape(width * height * bands)
    vi = pyvips.Image.new_from_memory(linear.data, width, height, bands,
                                      dtype_to_format[str(a.dtype)])
    return vi
def vips2numpy(vi):
    return np.ndarray(buffer=vi.write_to_memory(),
                      dtype=format_to_dtype[vi.format],
                      shape=[vi.height, vi.width, vi.bands])

image = pyvips.Image.new_from_file(sys.argv[1])

print("loaded", image)

print("as a 4 band numpy image:")
nump = vips2numpy(image)
print(nump.shape)

# drop the alpha channel
image = image[0:3]
print("as a 3 band numpy image:")
nump = vips2numpy(image)
print(nump.shape)

# finally, as a (W * H, 3) image
print("as a 3 band linear image:")
nump = nump.reshape(image.width * image.height, 3)
print(nump.shape)

$ python3 try283.py ~/pics/openslide/CMU-1-Small-Region.svs
loaded <pyvips.Image 2220x2967 uchar, 4 bands, rgb>
as a 4 band numpy image:
(2967, 2220, 4)
as a 3 band numpy image:
(2967, 2220, 3)
as a 3 band linear image:
(6586740, 3)