Return cv2.addWeighted(np.array(img),0.4,np.array(mask_colored),0.3,0)įunction to change color of each pixel in a picture : def change_color(picture, width, height, ex_color, new_color):Ĭurrent_color = picture. That input image is stored in the image variable. Here's an example input image I am using: That image is loaded with a standard cv2.IMREADUNCHANGED flag so that alpha channel is perfectly preserved. # Convert the black (for background) to white -> important to make a good overlapping I would like to add a semi-transparent rectangle filled with a solid colour to an already loaded semi-transparent PNG. # Convert the white color (for blobs) to magenta Mask = Image.open(mask_folder+"/"+filename+".png").convert('RGB') # Import and convert the mask from binary to RGB Img = cv2.imread(img_folder+"/"+filename+".jpg") I can give you my function two overlap a picture and a mask of dataset : def get_overlapped_img(filename, img_folder, mask_folder): What I am doing wrong? Is there any straight way? I am coming from a Matlab environment and I am quite new to Python. Plt.pcolormesh(x, y, Image2_mask,cmap='jet') Plt.axes().set_aspect('equal', 'datalim') I found a example on github I modified a bit, works as expected: import numpy as np import cv2 img cv2.imread ('1calib.jpeg') overlayt cv2.imread ('ol.png',-1) -1 loads with transparency def overlaytransparent (bgimg, imgtooverlayt): Extract the alpha mask of the RGBA image, convert to RGB b,g,r,a cv2. Image2_mask = ma.masked_array(Image2,mask)
To do that I am trying to create a masked array. The second one is also a 512x512 NumPy array but I am just interested in the pixels where the value is larger than 0 (a functional image). The first one is a 512x512 NumPy array (from a CT image). Any help would be greatly appreciated.I am trying to overlay two images.
I would like the solution to be limited to OpenCV and/or Numpy. As a side note, I did try the cv2.addWeighted method but that yielded even worse results. Perhaps my method of blending transparent PNG with semi-transparent shapes is far from ideal.
#Overlay images with transparency python how to#
It seems that alpha is not blended at all which results in original image pixels being only fully opaque or fully transparent. Well organized and easy to understand Web building tutorials with lots of examples of how to use HTML, CSS, JavaScript, SQL, Python, PHP, Bootstrap, Java. However, upon closer inspection, we can observe strange behaviour when mixing alpha channels (marked with arrows): It accepts an RGBA image, so the simplest solution is to use your overlay image again: im.paste (overlay, (0, 0), overlay). Alternatively, if you just want to display an image in 3d space. The 3rd option to paste is a mask (see the docs ). We have our input image with a semi-transparent rectangle in the middle. overlay (Im, slicexy, sliceNo10) python matplotlib matplotlib-3d Share.
Output = cv2.convertScaleAbs(overlay * mask + image * (1 - mask))Īt first glance it looks acceptable. Overlay = np.zeros((imgHeight, imgWidth, 4), dtype = "uint8") # create empty overlay layer with 4 channels
#Overlay images with transparency python code#
Here's my code that I have so far: # get image dimensions That image is loaded with a standard cv2.IMREAD_UNCHANGED flag so that alpha channel is perfectly preserved. Here's an example input image I am using: I would like to add a semi-transparent rectangle filled with a solid colour to an already loaded semi-transparent PNG.
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