Develop Reference

How to change the colour of pixels in an image depending on their initial luminosity?

The aim is to take a coloured image, and change any pixels within a certain luminosity range to black. For example, if luminosity is the average of a pixel's RGB values, any pixel with a value under 50 is changed to black.
I’ve attempted to begin using PIL and converting to grayscale, but having trouble trying to find a solution that can identify luminosity value and use that info to manipulate a pixel map.

There are many ways to do this, but the simplest and probably fastest is with Numpy, which you should get accustomed to using with image processing in Python:
from PIL import Image
import numpy as np
# Load image and ensure RGB, not palette image
im = Image.open('start.png').convert('RGB')
# Make into Numpy array
na = np.array(im)
# Make all pixels of "na" where the mean of the R,G,B channels is less than 50 into black (0)
na[np.mean(na, axis=-1)<50] = 0
# Convert back to PIL Image to save or display
result = Image.fromarray(na)
result.show()
That turns this:
Into this:
Another slightly different way would be to convert the image to a more conventional greyscale, rather than averaging for the luminosity:
# Load image and ensure RGB
im = Image.open('start.png').convert('RGB')
# Calculate greyscale version
grey = im.convert('L')
# Point process over pixels to make mask of darker ones
mask = grey.point(lambda p: 255 if p<50 else 0)
# Paste black (i.e. 0) into image where mask indicates it is dark
im.paste(0, mask=mask)
Notice that the blue channel is given considerably less significance in the ITU-R 601-2 luma transform that PIL uses (see the lower 114 weighting for Blue versus 299 for Red and 587 for Green) in the formula:
L = R * 299/1000 + G * 587/1000 + B * 114/1000
so the blue shades are considered darker and become black.
Another way would be to make a greyscale and a mask as above. but then choose the darker pixel at each location when comparing the original and the mask:
from PIL import Image, ImageChops
im = Image.open('start.png').convert('RGB')
grey = im.convert('L')
mask = grey.point(lambda p: 0 if p<50 else 255)
res = ImageChops.darker(im, mask.convert('RGB'))
That gives the same result as above.
Another way, pure PIL and probably closest to what you actually asked, would be to derive a luminosity value by averaging the channels:
# Load image and ensure RGB
im = Image.open('start.png').convert('RGB')
# Calculate greyscale version by averaging R,G and B
grey = im.convert('L', matrix=(0.333, 0.333, 0.333, 0))
# Point process over pixels to make mask of darker ones
mask = grey.point(lambda p: 255 if p<50 else 0)
# Paste black (i.e. 0) into image where mask indicates it is dark
im.paste(0, mask=mask)
Another approach could be to split the image into its constituent RGB channels, evaluate a mathematical function over the channels and mask with the result:
from PIL import Image, ImageMath
# Load image and ensure RGB
im = Image.open('start.png').convert('RGB')
# Split into RGB channels
(R, G, B) = im.split()
# Evaluate mathematical function over channels
dark = ImageMath.eval('(((R+G+B)/3) <= 50) * 255', R=R, G=G, B=B)
# Paste black (i.e. 0) into image where mask indicates it is dark
im.paste(0, mask=dark)

I created a function that returns a list with True if the pixel has a luminosity of less than a parameter, and False if it doesn't. It includes an RGB or RGBA option (True or False)
def get_avg_lum(pic,avg=50,RGBA=False):
num=3
numd=4
if RGBA==False:
num=2
numd=3
li=[[[0]for y in range(0,pic.size[1])] for x in range(0,pic.size[0])]
for x in range(0,pic.size[0]):
for y in range(0,pic.size[1]):
if sum(pic.getpixel((x,y))[:num])/numd<avg:
li[x][y]=True
else:
li[x][y]=False
return(li)
a=get_avg_lum(im)
The pixels match in the list, so (0,10) on the image is [0][10] in the list.
Hopefully this helps. My module is for standard PIL objects.

Python 3: I am trying to find find all green pixels in an image by traversing all pixels using an np.array, but can't get around index error

My code currently consists of loading the image, which is successful and I don't believe has any connection to the problem.
Then I go on to transform the color image into a np.array named rgb
# convert image into array
rgb = np.array(img)
red = rgb[:,:,0]
green = rgb[:,:,1]
blue = rgb[:,:,2]
To double check my understanding of this array, in case that may be the root of the issue, it is an array such that rgb[x-coordinate, y-coordinate, color band] which holds the value between 0-255 of either red, green or blue.
Then, my idea was to make a nested for loop to traverse all pixels of my image (620px,400px) and sort them based on the ratio of green to blue and red in an attempt to single out the greener pixels and set all others to black or 0.
for i in range(xsize):
for j in range(ysize):
color = rgb[i,j] <-- Index error occurs here
if(color[0] > 128):
if(color[1] < 128):
if(color[2] > 128):
rgb[i,j] = [0,0,0]
The error I am receiving when trying to run this is as follows:
IndexError: index 400 is out of bounds for axis 0 with size 400
I thought it may have something to do with the bounds I was giving i and j so I tried only sorting through a small inner portion of the image but still got the same error. At this point I am lost as to what is even the root of the error let alone even the solution.

In direct answer to your question, the y axis is given first in numpy arrays, followed by the x axis, so interchange your indices.
Less directly, you will find that for loops are very slow in Python and you are generally better off using numpy vectorised operations instead. Also, you will often find it easier to find shades of green in HSV colourspace.
Let's start with an HSL colour wheel:
and assume you want to make all the greens into black. So, from that Wikipedia page, the Hue corresponding to Green is 120 degrees, which means you could do this:
#!/usr/local/bin/python3
import numpy as np
from PIL import Image
# Open image and make RGB and HSV versions
RGBim = Image.open("image.png").convert('RGB')
HSVim = RGBim.convert('HSV')
# Make numpy versions
RGBna = np.array(RGBim)
HSVna = np.array(HSVim)
# Extract Hue
H = HSVna[:,:,0]
# Find all green pixels, i.e. where 100 < Hue < 140
lo,hi = 100,140
# Rescale to 0-255, rather than 0-360 because we are using uint8
lo = int((lo * 255) / 360)
hi = int((hi * 255) / 360)
green = np.where((H>lo) & (H<hi))
# Make all green pixels black in original image
RGBna[green] = [0,0,0]
count = green[0].size
print("Pixels matched: {}".format(count))
Image.fromarray(RGBna).save('result.png')
Which gives:
Here is a slightly improved version that retains the alpha/transparency, and matches red pixels for extra fun:
#!/usr/local/bin/python3
import numpy as np
from PIL import Image
# Open image and make RGB and HSV versions
im = Image.open("image.png")
# Save Alpha if present, then remove
if 'A' in im.getbands():
savedAlpha = im.getchannel('A')
im = im.convert('RGB')
# Make HSV version
HSVim = im.convert('HSV')
# Make numpy versions
RGBna = np.array(im)
HSVna = np.array(HSVim)
# Extract Hue
H = HSVna[:,:,0]
# Find all red pixels, i.e. where 340 < Hue < 20
lo,hi = 340,20
# Rescale to 0-255, rather than 0-360 because we are using uint8
lo = int((lo * 255) / 360)
hi = int((hi * 255) / 360)
red = np.where((H>lo) | (H<hi))
# Make all red pixels black in original image
RGBna[red] = [0,0,0]
count = red[0].size
print("Pixels matched: {}".format(count))
result=Image.fromarray(RGBna)
# Replace Alpha if originally present
if savedAlpha is not None:
result.putalpha(savedAlpha)
result.save('result.png')
Keywords: Image processing, PIL, Pillow, Hue Saturation Value, HSV, HSL, color ranges, colour ranges, range, prime.

python+opencv - How to plot hsv range?

To extract the color, we have this function
# define range of blue color in HSV
lower_blue = np.array([110,50,50])
upper_blue = np.array([130,255,255])
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, lower_blue, upper_blue)
How do we actually visualize the range(lower_blue,upper_blue) I define on hsv space?
Also How do I actually plot a hsv color,but it is not working...?
I have this code:
upper = np.array([60, 255, 255])
upper = cv2.cvtColor(upper, cv2.COLOR_HSV2BGR)
upper = totuple(upper/-255)
print(upper)
plt.imshow([[upper]])

What are HSV colors
HSV, like HSL (or in OpenCV, HLS), is one of the cylindrical colorspaces.
The name is somewhat descriptive of how their values are referenced.
The hue is represented as degrees from 0 to 360 (in OpenCV, to fit into the an 8-bit unsigned integer format, they degrees are divided by two to get a number from 0 to 179; so 110 in OpenCV is 220 degrees). If you were to take a "range" of hue values, it's like cutting a slice from a cake. You're just taking some angle chunk of the cake.
The saturation channel is how far from the center you are---the radius you're at. The center has absolutely no saturation---only gray colors from black to white. If you took a range of these values, it is akin to shaving off the outside of the cylinder, or cutting out a circle from the center. For example, if the range is 0 to 255, then the range 0 to 127 would be a cylinder only extending to half the radius; the range 127 to 255 would be cutting an inner cylinder with half the radius out.
The value channel is a slightly confusing name; it's not exactly darkness-to-brightness because the highest value represents the direct color, while the lowest value is black. This is the height of the cylinder. Not too hard to imagine cutting a slice of the cylinder vertically.
Ranges of HSV values
The function cv2.inRange(image, lower_bound, upper_bound) finds all values of the image between lower_bound and upper_bound. For instance, if your image was a 3x3 image (just for simple demonstration purposes) with 3-channels, it might look something like this:
# h channel # s channel # v channel
100 150 250 150 150 100 50 75 225
50 100 125 75 25 50 255 100 50
0 255 125 100 200 250 50 75 100
If we wanted to select hues between 100 and 200, then our lower_b should be [100, 0, 0] and upper_b should be [200, 255, 255]. That way our mask would only take into account values in the hue channel, and not be affected by the saturation and value. That's why HSV is so popular---you can select colors by hue regardless of their brightness or darkness, so a dark red and bright red can be selected just by specifying the min and max of the hue channel.
But say we only wanted to select bright white colors. Take a look back at the cylinder model---we see that white is given at the top-center of the cylinder, so where s values are low, and v values are high, and the color angle doesn't matter. So the lower_b would look something like [0, 0, 200] and upper_b would look something like [255, 50, 255]. That means all H values will be included and won't affect our mask. But then only S values between 0 and 50 would be included (towards the center of the cylinder) and only V values from 200 to 255 will be included (towards the top of the cylinder).
Visualizing a range of colors from HSV
One way to visualize all the colors in a range is to create gradients going the length of both directions for each of two channels, and then animate over the changing third channel.
For instance, you could create a gradient of values from left to right for the range of S values, from top to bottom for the range of V values, and then loop over each H value. This whole program could look something like this:
import numpy as np
import cv2
lower_b = np.array([110,50,50])
upper_b = np.array([130,255,255])
s_gradient = np.ones((500,1), dtype=np.uint8)*np.linspace(lower_b[1], upper_b[1], 500, dtype=np.uint8)
v_gradient = np.rot90(np.ones((500,1), dtype=np.uint8)*np.linspace(lower_b[1], upper_b[1], 500, dtype=np.uint8))
h_array = np.arange(lower_b[0], upper_b[0]+1)
for hue in h_array:
h = hue*np.ones((500,500), dtype=np.uint8)
hsv_color = cv2.merge((h, s_gradient, v_gradient))
rgb_color = cv2.cvtColor(hsv_color, cv2.COLOR_HSV2BGR)
cv2.imshow('', rgb_color)
cv2.waitKey(250)
cv2.destroyAllWindows()
Now this gif shows a new H value every frame. And from left to right we have the min to max S values, and from top to bottom we have the min to max V values. Every single one of the colors showing up in this animation will be selected from your image to be part of your mask.
Make your own inRange() function
To fully understand the OpenCV function, the easiest way is just to make your own function to complete the task. It's not difficult at all, and not very much code.
The idea behind the function is simple: find where the values of each channel fall between min and max, and then & all the channels together.
def inRange(img, lower_b, upper_b):
ch1, ch2, ch3 = cv2.split(img)
ch1m = (lower_b[0] <= ch1) & (ch1 <= upper_b[0])
ch2m = (lower_b[1] <= ch2) & (ch2 <= upper_b[1])
ch3m = (lower_b[2] <= ch3) & (ch3 <= upper_b[2])
mask = ch1m & ch2m & ch3m
return mask.astype(np.uint8)*255
You can read the OpenCV docs to see that this is indeed the formula used. And we can verify it too.
lower_b = np.array([200,200,200])
upper_b = np.array([255,255,255])
mask = cv2.inRange(img, lower_b, upper_b) # OpenCV function
mask2 = inRange(img, lower_b, upper_b) # above defined function
print((mask==mask2).all()) # checks that the masks agree on all values
# True
How to find the right colors
It can be a little tricky to find the correct values to use for a particular image. There is an easy way to experiment, though. You can create trackbars in OpenCV and use them to control the min and max for each channel and have the Python program update your mask every time you change the values. I made a program for this which you can grab on GitHub here. Here's an animated .gif of it being used, to demonstrate:

PIL: What is happening in Image.Blend() when I use an alpha greater than 1.0?

From the docs:
Creates a new image by interpolating between the given images, using a constant alpha. Both images must have the same size and mode. out = image1 * (1.0 - alpha) + image2 * alpha
If the alpha is 0.0, a copy of the first image is returned. If the alpha is 1.0, a copy of the second image is returned. There are no restrictions on the alpha value. If necessary, the result is clipped to fit into the allowed output range.
So there is no restriction on alpha, but what actually happens when you use values greater than 1.0?
Image 1:
Image 2:
The image blended with an alpha of 100.0:

The results are fully explained by the formula you quoted. If alpha is 100, you get image1 * -99 + image2 * 100 and then the results for each pixel are clamped to be valid. Lets look at what that means for some example point.
A pixel I sampled from the joker's forehead has RGB value (255, 194, 106). An approximately corresponding pixel from the other image (part of the seascape's blue sky) has RGB value (1, 109, 217). Combining them according to the equation above, gives (25401, 8609, -10883), which is obviously way out of bounds for all three bands. Clamping each color to be between 0 and 255 gives (255, 255, 0), which is the pure yellow you see in the output image for that area.
Almost all of the pixels in the output image will have values of either 0 or 255 for each color band (resulting in very saturated colors). Only a very few pixels (maybe none for these images, I've not checked), where the difference between the two images was very small will there be any intermediate values.

Sum of colorvalues of an image

I am looking for a way to sum the color values of all pixels of an image. I require this to estimate the total flux of a bright source (say a distant galaxy) from its surface brightness image.
Would anyone please help me how can I sum the colour values of all pixels of an image.
For example:
Each pixel of the following image has a colour value in between 0 to 1.
But when I read the image with imread the colour values of each pixel I get is an array of 3 elements. I am very new in matplotlib and I do not know how can I convert that array to single values in the scale of 0 to 1 and add them.

If you have a PIL image, then you can convert to greyscale ("luminosity") like this:
from PIL import Image
col = Image.open('sample.jpg')
gry = col.convert('L') # returns grayscale version.
If you want ot have more control over how the colors are added, convert to a numpy array first:
arr = np.asarray(col)
tot = arr.sum(-1) # sum over color (last) axis
mn = arr.mean(-1) # or a mean, to keep the same normalization (0-1)
Or you can weight the colors differently:
wts = [.25, .25, .5] # in order: R, G, B
tot = (arr*wts).sum(-1) # now blue has twice the weight of red and green
For large arrays, this is equivalent to the last line and faster, but possibly harder to read:
tot = np.einsum('ijk, k -> ij', arr, wts)
All of the above adds up the colors of each pixel, to turn a color image into a grayscale (luminosity) image. The following will add up all the pixels together to see the integral of the entire image:
tot = arr.sum(0).sum(0) # first sums all the rows, second sums all the columns
If you have a color image, tot will still have three values. If your image is grayscale, it will be a single value. If you want the mean value, just replace sum with mean:
mn = arr.mean(0).mean(0)