With PIL it should work like this

from PIL import Image

I8 = (((I - I.min()) / (I.max() - I.min())) * 255.9).astype(np.uint8)

img = Image.fromarray(I8)
img.save("file.png")

You need a lossless image format that can save floats:

• PNG can only save up to 16-bit integer,
• JPEG is not lossless and only 8-bit integer,
• GIF is integer and 8-bit,
• NetPBM is up to 16-bit integer only.

so use TIFF with lossless compression, or no compression. Alternatively you could use PFM - Portable Float Map.

Here is a demonstration:

import numpy as np
from PIL import Image

# Generate a small float image in Numpy array
grey32 = np.random.randn(2,3).astype(np.float32)                                           

# Convert to PIL Image and save
Image.fromarray(grey32).save('test.tif')                                                   

# Read back from disk and convert to Numpy array
reloaded = np.array(Image.open('test.tif'))                                                

# Inspect
print(grey32)

array([[-0.28032717, -1.7696048 , -0.9811929 ],
       [-0.7785768 , -1.2427857 , -0.33241433]], dtype=float32)

print(reloaded)                                                                           

array([[-0.28032717, -1.7696048 , -0.9811929 ],
       [-0.7785768 , -1.2427857 , -0.33241433]], dtype=float32)

If your viewer is incapable of showing TIFF files containing floats, you can always convert them to something viewable with ImageMagick:

magick float.tif -auto-level viewable.png

I know that tifffile and pyvips are both good Python libraries for dealing with TIFF floats.

Using PIL, save a NumPy array arr by doing:

from PIL import Image
im = Image.fromarray(arr)
im.save("your_file.jpeg")

See the docs for available data formats, including JPEG, PNG, and so on.

I think the answer is wrong. The Image.fromarray( ____ , 'L') function seems to only work properly with an array of integers between 0 and 255. I use the np.uint8 function for this.

You can see this demonstrated if you try to make a gradient.

import numpy as np
from PIL import Image

# gradient between 0 and 1 for 256*256
array = np.linspace(0,1,256*256)

# reshape to 2d
mat = np.reshape(array,(256,256))

# Creates PIL image
img = Image.fromarray(np.uint8(mat * 255) , 'L')
img.show()

Makes a clean gradient

vs

import numpy as np
from PIL import Image

# gradient between 0 and 1 for 256*256
array = np.linspace(0,1,256*256)

# reshape to 2d
mat = np.reshape(array,(256,256))

# Creates PIL image
img = Image.fromarray( mat , 'L')
img.show()

Has the same kind of artifacting.

You might be better off using PIL:

from PIL import Image
import numpy as np

data = np.random.random((100,100))

#Rescale to 0-255 and convert to uint8
rescaled = (255.0 / data.max() * (data - data.min())).astype(np.uint8)

im = Image.fromarray(rescaled)
im.save('test.png')

As the assertion states, adaptiveThreshold() requires a single-channeled 8-bit image.

Assuming your floating-point image ranges from 0 to 1, which appears to be the case, you can convert the image by multiplying by 255 and casting to np.uint8:

float_img = np.random.random((4,4))
im = np.array(float_img * 255, dtype = np.uint8)
threshed = cv2.adaptiveThreshold(im, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 3, 0)

Black square is probably very dark gray, because you may have np.array with uint8 datatype, which has range of 0-255, not 0-1 like binary array. Try changing array datatype to bool, or scale values to 0-255 range.

Here is code snippet in which binary array is generated and displayed. If you scale by smaller value, circle will become slightly darker.

from PIL import Image
import numpy as np

# Generating binary array which represents circle
radius = 0.9
size = 100
x,y = np.meshgrid(np.linspace(-1,1,size),np.linspace(-1,1,size))
f = np.vectorize(lambda x,y: ( 1.0 if x*x + y*y < radius*radius else 0.0))
array = f(x,y)

# Solution 1: 
# convert np.array to bool datatype
array = (array).astype(np.bool)

# Solution 2:
# scale values to uint8 maximum 255
array = ((array) * 255).astype(np.uint8)


img = Image.fromarray(array)
display(img)

Result: White circle