在没有慢循环的情况下，在 Python 中使用条件进行逐像素操作

Question

I'm trying to apply a treshold to an image, but not a regular simple treshold.

I need to set to black pixels if they fit the conditonal, and if not, set them to white.

I could just loop over pixels, but on a 1080p image, it's far too long.

I'm using HSV for the comparisons I need to make.

Here is the conditional (this example is how I would use it if it was in a loop):

if abs(input_pixel_color.hue - reference.hue) < 2 and input_pixel_color.saturation >= 0.25 and input_pixel_color.brightness >= 0.42:
    set_to_black
else:
    set_to_white

input_pixel is the HSV value of the pixel in the loop.

reference is a variable to be compared to.

I thought about using numpy, but I really don't know how to write this :/

Thanks in advance

Answer 1

Updated

Now that your actual intended processing has become clearer, you would probably be better served by OpenCV inRange() function. Like this:

#!/usr/local/bin/python3 

import cv2 as cv 
import numpy as np 

# Load the image and convert to HLS 
image = cv.imread("image.jpg") 
hls   = cv.cvtColor(image,cv.COLOR_BGR2HLS) 

# Define lower and uppper limits for each component 
lo = np.array([50,0,0]) 
hi = np.array([70,255,255]) 

# Mask image to only select filtered pixels 
mask = cv.inRange(hls,lo,hi) 

# Change image to white where we found our colour 
image[mask>0]=(255,255,255) 

cv.imwrite("result.png",image) 

So, if we use this image:

We are selecting Hues in the range 50-70, and making them white:

If you go here to a colour converter, you can see that "Green" is Hue=120, but OpenCV divides Hues by 2 so that 360 degrees becomes 180 and still fits in a uint8. So, our 60 in the code means 120 in online colour converters.

The ranges OpenCV uses for uint8 images are:

• Hue 0..180
• Lightness 0..255
• Saturation 0..255

As I said before, you should get in the habit of looking at your data types, shapes and ranges in your debugger. To see the shape , dtype , and maximum Hue, Lightness and Saturation, use:

print(hls.dtype, hls.shape) 
print(hls[...,0].max())
print(hls[...,1].max())
print(hls[...,2].max())

Original Answer

There are several ways to do that. The most performant is probably with the OpenCV function cv2.inRange() and there are plenty of answers on StackOverflow about that.

Here is a Numpy way. If you read the comments and look at the printed values, you can see how to combine logical AND with logical OR and so on, as well as how to address specific channels.

#!/usr/bin/env python3

from random import randint, seed 
import numpy as np

# Generate a repeatable random HSV image
np.random.seed(42)
h, w = 4, 5
HSV = np.random.randint(1,100,(h,w,3),dtype=np.uint8)
print('Initial HSV\n',HSV)

# Create mask of all pixels with acceptable Hue, i.e. H > 50
HueOK = HSV[...,0] > 50
print('HueOK\n',HueOK)

# Create mask of all pixels with acceptable Saturation, i.e. S > 20 AND S < 80
SatOK = np.logical_and(HSV[...,1]>20, HSV[...,1]<80)
print('SatOK\n',SatOK)

# Create mask of all pixels with acceptable value, i.e. V < 20 OR V > 60
ValOK = np.logical_or(HSV[...,2]<20, HSV[...,2]>60)
print('ValOK\n',ValOK)

# Combine masks
combinedMask = HueOK & SatOK & ValOK
print('Combined\n',combinedMask)

# Now, if you just want to set the masked pixels to 255
HSV[combinedMask] = 255
print('Result1\n',HSV)

# Or, if you want to set the masked pixels to one value and the others to another value
HSV = np.where(combinedMask,255,0)
print('Result2\n',HSV)

Sample Output

Initial HSV
 [[[93 98 96]
  [52 62 76]
  [93  4 99]
  [15 22 47]
  [60 72 85]]

 [[26 72 61]
  [47 66 26]
  [21 45 76]
  [25 87 40]
  [25 35 83]]

 [[66 40 87]
  [24 26 75]
  [18 95 15]
  [75 86 18]
  [88 57 62]]

 [[94 86 45]
  [99 26 19]
  [37 24 63]
  [69 54  3]
  [33 33 39]]]
HueOK
 [[ True  True  True False  True]
 [False False False False False]
 [ True False False  True  True]
 [ True  True False  True False]]
SatOK
 [[False  True False  True  True]
 [ True  True  True False  True]
 [ True  True False False  True]
 [False  True  True  True  True]]
ValOK
 [[ True  True  True False  True]
 [ True False  True False  True]
 [ True  True  True  True  True]
 [False  True  True  True False]]
Combined
 [[False  True False False  True]
 [False False False False False]
 [ True False False False  True]
 [False  True False  True False]]
Result1
 [[[ 93  98  96]
  [255 255 255]
  [ 93   4  99]
  [ 15  22  47]
  [255 255 255]]

 [[ 26  72  61]
  [ 47  66  26]
  [ 21  45  76]
  [ 25  87  40]
  [ 25  35  83]]

 [[255 255 255]
  [ 24  26  75]
  [ 18  95  15]
  [ 75  86  18]
  [255 255 255]]

 [[ 94  86  45]
  [255 255 255]
  [ 37  24  63]
  [255 255 255]
  [ 33  33  39]]]
Result2
 [[  0 255   0   0 255]
 [  0   0   0   0   0]
 [255   0   0   0 255]
 [  0 255   0 255   0]]

Notes :

1) You can also access pixels not selected by the mask, using negation:

# All unmasked pixels become 3
HSV[~combinedMask] = 3

2) The ellipsis ( ... ) is just a shortcut meaning "all other dimensions I didn't bother listing" , so HSV[...,1] is the same as HSV[:,:,1]

3) If you don't like writing HSV[...,0] for Hue, and HSV[...,1] for Saturation, you can split the channels

H, S, V = cv2.split(HSV)

Then you can just use H instead of HSV[...,0] . When you are finished, if you want to re-assemble the channels back into a 3-channel image, you can do:

HSV = cv2.merge((H,S,V))

or

HSV = np.dstack((H,S,V))