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replacing pixels in an imagewith pixels from another image python


replacing pixels in an imagewith pixels from another image python

By : pradeep
Date : October 22 2020, 08:10 PM
around this issue I am trying to replace pixels less than value 50 in one image (src1) with pixels from another image (src2). The code i am trying is as below. The problem is it is taking lot of time. Can anyone guide me in using optimized way to do this , You can use numpy for this.
code :
src1 = cv2.imread('')
src2 = cv2.imread('')
r,c = np.where(src1<50)
src1[(r,c)] = src2[(r,c)]


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image WriteableBitmap image .Pixels The field, constructor or member 'Pixels' is not defined?

image WriteableBitmap image .Pixels The field, constructor or member 'Pixels' is not defined?


By : Jalandar R
Date : March 29 2020, 07:55 AM
like below fixes the issue Pixels is a property of the System.Windows.Media.Imaging.WriteableBitmap class for Silverlight, that is different than the System.Windows.Media.Imaging.WriteableBitmap class of the standard framework.
You should reference the Silverlight version of the framework to use the first class - and have the Pixels property - but then you will be writing a Silverlight application, not a standard Windows one.
Matlab image processing - replacing dark pixels with neighboring pixels

Matlab image processing - replacing dark pixels with neighboring pixels


By : Dean Propps
Date : March 29 2020, 07:55 AM
Hope this helps Having worked extensively with retinal images, I can tell you that what you're proposing is a complex problem in itself. Sure, if you just want a crude method, you can use imdilate. But that will affect your entire image, and other structures in the image will change appearance. Something, that is not desirable.
However, if you want to do it properly, you will first need to segment all the blood vessels and create a binary mask. Once you have a binary mask, it's up to you how to fill up the vessel regions. You can either interpolate from the boundaries or calculate a background image and replace the vessel regions with pixels from the background image, etc.
Convert image into array of pixels, recreate image using pixels - size gets reduced. Why?

Convert image into array of pixels, recreate image using pixels - size gets reduced. Why?


By : Roj Rosh
Date : March 29 2020, 07:55 AM
will be helpful for those in need You can try setting the compression level to different values and see how the file size differs depending on that. See Setting jpg compression level with ImageIO in Java for how to do that.
Doesn't explain the change in brightness and contrast though.
Removing Pimple/Soften Image/ Replacing image pixels with surrounding pixels

Removing Pimple/Soften Image/ Replacing image pixels with surrounding pixels


By : Abhijith Palanickal
Date : March 29 2020, 07:55 AM
will help you The GPUImage framework will help you. Have a look at the filters described here: JH Labs. What you need to do is (this is not easy; lots of work)
Figure out face skin-tone. Use the face-detection algorithm to locate the face and then sample flesh tone from forehead and cheek area. For each skin-tone type, you need to have a range of colors you consider shades of the skin. In your filter, if the pixel in question falls within the skin-tone range, then apply minimum or median filter (essential a noise elimination filter; not a blurring filter - that will get you no where). You may have to apply multiple passes of the filter.
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 ge

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 ge


By : Ethan V
Date : March 29 2020, 07:55 AM
I hope this helps you . In direct answer to your question, the y axis is given first in numpy arrays, followed by the x axis, so interchange your indices.
code :
#!/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')
#!/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')
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