i am trying to write a zoom function which looks something like this:
centre = ((im.width-1)/2, (im.height-1)/2)
width = int(im.width/(2.0*level))
height = int(im.height/(2.0*level))
rect = (centre[0]-width, centre[1]-height, width*2, height*2)
dst = cv.GetSubRect(im, rect)
cv.Resize(dst, im)
when I use exactly what is written above, I get an odd result where the bottom half of the resultant image is distorted and blurry. However when I replace the line cv.Resize(dst, im) with
size = cv.CloneImage(im)
cv.Resize(dst, size)
im = size
it works fine. Why is this? is there something fundamentally wrong with the way i am performing the zoom?
cv.Resize requires source and destination to be separate memory locations.
Now in the first snippet of your code, you are using cv.GetSubRect to generate an object pointing to area of image which you wish to zoom in. Here the new object is NOT pointing to a new memory location. It is pointing to a memory location which is a subset of original object.
Since cv.Resize requires both the memory locations to be different, what you are getting is a result of undefined behavior.
In the second part of your code you are fulfilling this criteria by using cv.CloneImage.
you are first creating a copy of im (i.e. size. however you could have used a blank image aswell) and then you are using cv.Resize to resize dst and write the resulting image in size.
My advice is to go through the function documentation before using them.
Related
I am trying to create a pipeline in which I first render an image using the blender python API (I am using Blender 2.90) and then perform some image processing in python. I want to fetch the image directly from blender without first writing the rendered image to disk and then loading it again. I ran the following code within the blender GUI to do so:
import bpy
import numpy as np
import PIL.Image as Image
from skimage.util import img_as_ubyte
resolution_x = 512
resolution_y = 512
# render settings
scene = bpy.context.scene
scene.render.engine = 'BLENDER_EEVEE'
scene.render.resolution_x = resolution_x
scene.render.resolution_y = resolution_y
scene.render.image_settings.file_format = 'PNG'
scene.render.filepath = "path/to/good_image.png"
# create Viewer Layer in Compositor
scene.use_nodes = True
tree = scene.node_tree
nodes = tree.nodes
links = tree.links
for node in nodes:
nodes.remove(node)
render_layer_node = nodes.new('CompositorNodeRLayers')
viewer_node = nodes.new('CompositorNodeViewer')
links.new(viewer_node.inputs[0], render_layer_node.outputs[0])
# render scene and get pixels from Viewer Node
bpy.ops.render.render(write_still=True)
pixels = bpy.data.images['Viewer Node'].pixels
# do some processing and save
img = np.flip(img_as_ubyte(np.array(pixels[:]).reshape((resolution_y, resolution_x, 4))), axis=0)
Image.fromarray(img).save("path/to/bad_image.png")
Problem: The image I get from the Viewer Node is much darker (bad image) than the image saved in the conventional way (good image). Does anyone have an idea why this happens and how to fix it? Does blender maybe treat pixel values differently than I expect?
Some additional information:
Before conversion to uint8, the values of the alpha channel within the dark image are 1.0 (as they actually should be). Background values in the dark image are not 0.0 or negative (as one might guess from appearance), but 0.05...
What I tried:
I thought that pixels might be scaled within range -1 to 1, so I rescaled the pixels to range 0 to 1 before transforming to uint8... Did not lead to the correct image either :(
It's because the image that you get from the Viewer Node is the one "straight from compositing" before color management takes place. You can have a look at the documentation here: this image is still in the linear space.
Your good_image.png on the other hand is obtained after transformation into the "Display Space" (see diagram in the doc). Hence it was transformed into a log-space, maybe gamma-corrected, etc.
Finally, you can get an image that is close to (but slightly different though) to the good image from the viewer node by calling bpy.data.images['Viewer Node'].save_render(filepath) instead, but there is no way to directly extract the color-managed version without rendering to a file first. You can probably do it yourself by adding PyOpenColorIO to your script and applying the color management from this module.
I am supposed to create several functions for my python program, and each program requires me to work with a copy of an input image. Hence, I need to write img = image.copy() for every function in my code. However, when I run the code, I am returned an AttributeError saying "'tuple' object has no attribute 'copy'. "
Given that I still have to include the statement img = image.copy() somewhere inside my function, how do I go about changing my code to remove this error? Do I need to change the image into numpy array first before I can use copy()?
Code:
def func(image):
img = image.copy() #error code appeared here
np_img = np.array(image)
rsize, csize = len(img), len(img[0]) #denoting the rows and columns of pixels of the image respectively
(the rest of the code)
Error message: AttributeError: 'tuple' object has no attribute 'copy'
Given that you have to put img=image.copy() in your functions, the easiest way should be to flip the order of np_img = np.array(image) and the former line. I'm assuming that your argument image has not been converted into a numpy array prior to what we see here.
After that, you should change img=image.copy() to the appropriate variables.
That said, I think it's best to load the image as a numpy matrix right away before doing anything else. That way, you can make a copy before any of your functions, lowering each function's costs too.
Okay, first thing first. This is a near duplicate of this question.
However, the issue I am facing is slightly different in a critical way.
In my application, I read a generic file name in, load said image, and display it. Where it gets tricky is I have overlay the appearance of being 'highlighted'. To do this, I was using the Image.blend() function, and blending it with a straight yellow image.
However, when dealing with blend, I was fighting the error that the two images are not compatible to be blended. To solve this, I opened the sample image I had in paint, and just pasted yellow over the whole thing, and saved it as a copy.
It just occurred to me that this will fail when a different type of image is read in by file name. Remember this needs to be generic.
So my question is: Instead of making a copy of the image manually, can I get python to generate one by copying the image and modifying it so it is solid yellow? Note: I do not need to save it after, so just making it happen is enough.
Unfortunately, I am not allowed to share my code, but hopefully the following will give an idea of what I need:
from PIL import Image
desiredWidth = 800
desiredHeight = 600
primaryImage = Image.open("first.jpg").resize((desiredWidth, desiredHeight), Image.ANTIALIAS)
# This is the thing I need fixed:
highlightImage = Image.open("highlight.jpg").resize((desiredWidth, desiredHeight), Image.ANTIALIAS)
toDisplay = Image.blend(primaryImage, highlightImage, 0.3)
Thanks to anyone who can help.
Sounds like you want to make a new image:
fill_color = (255,255,0) #define the colour as (R,G,B) tuple
highlightImage = Image.new(primaryImage.mode, #same mode as the primary
primaryImage.size, #same size as the primary
fill_color)#and the colour defined above
this creates a new image with the same mode and size as the already opened image, but with a solid colour. Cheers.
Also if you are using Pillow instead of original PIL you can even get the color by name:
from PIL.ImageColor import getcolor
overlay = 'yellow'
fill_color = getcolor(overlay, primaryImage.mode)
I'm writing a code that part of it is reading an image source and displaying it on the screen for the user to interact with. I also need the sharpened image data. I use the following to read the data and display it in pyGame
def image_and_sharpen_array(file_name):
#read the image data and return it, with the sharpened image
image = misc.imread(file_name)
blurred = ndimage.gaussian_filter(image,3)
edge = ndimage.gaussian_filter(blurred,1)
alpha = 20
out = blurred + alpha*(blurred - edge)
return image,out
#get image data
scan,sharpen = image_and_sharpen_array('foo.jpg')
w,h,c = scan.shape
#setting up pygame
pygame.init()
screen = pygame.display.set_mode((w,h))
pygame.surfarray.blit_array(screen,scan)
pygame.display.update()
And the image is displayed on the screen only rotated and inverted. Is this due to differences between misc.imread and pyGame? Or is this due to something wrong in my code?
Is there other way to do this? The majority of solution I read involved saving the figure and then reading it with ``pyGame''.
I often use the numpy swapaxes() method:
In this case we only need to invert x and y axis (axis number 0 and 1) before displaying our array :
return image.swapaxes(0,1),out
I thought technico provided a good solution - just a little lean on info. Assuming get_arr() is a function that returns the pixel array:
pixl_arr = get_arr()
pixl_arr = numpy.swapaxes(pixl_arr, 0, 1)
new_surf = pygame.pixelcopy.make_surface(pixl_arr)
screen.blit(new_surf, (dest_x, dest_y))
Alternatively, if you know that the image will always be of the same dimensions (as in iterating through frames of a video or gif file), it would be more efficient to reuse the same surface:
pixl_arr = get_arr()
pixl_arr = numpy.swapaxes(pixl_arr, 0, 1)
pygame.pixelcopy.array_to_surface(old_surf, pixl_arr)
screen.blit(old_surf, (dest_x, dest_y))
YMMV, but so far this is working well for me.
Every lib has its own way of interpreting image arrays. By 'rotated' I suppose you mean transposed. That's the way PyGame shows up numpy arrays. There are many ways to make it look 'correct'. Actually there are many ways even to show up the array, which gives you full control over channel representation and so on. In pygame version 1.9.2, this is the fastest array rendering that I could ever achieve. (Note for earlier version this will not work!).
This function will fill the surface with array:
def put_array(surface, myarr): # put array into surface
bv = surface.get_view("0")
bv.write(myarr.tostring())
If that is not working, use this, should work everywhere:
# put array data into a pygame surface
def put_arr(surface, myarr):
bv = surface.get_buffer()
bv.write(myarr.tostring(), 0)
You probably still get not what you want, so it is transposed or have swapped color channels. The idea is, manage your arrays in that form, which suites this surface buffer. To find out what is correct channel order and axes order, use openCV library (cv2.imread(filename)). With openCV you open images in BGR order as standard, and it has a lot of conversion functions. If I remember correctly, when writing directly to surface buffer, BGR is the correct order for 24 bit and BGRA for a 32 bit surface. So you can try to put the image array which you get out of file with this function and blit to the screen.
There are other ways to draw arrays e.g. here is whole set of helper functions http://www.pygame.org/docs/ref/surfarray.html
But I would not recommend using it, since surfaces are not for direct pixel manipulating, you will probably get lost in references.
Small tip: To do 'signalling test' use a picture, like this. So you will immediately see if something is wrong, just load as array and try to render.
My suggestion is to use the pygame.transform module. There are the flip and rotate methods, which you can use to however your transformation is. Look up the docs on this.
My recommendation is to save the output image to a new Surface, and then apply the transformations, and blit to the display.
temp_surf = pygame.Surface((w,h))
pygame.surfarray.blit(temp_surf, scan)
'''transform temp_surf'''
screen.blit(temp_surf, (0,0))
I have no idea why this is. It is probably something to do with the order in which the axes are transferred from a 2d array to a pygame Surface.
I'm trying to convert images taken from a capture (webcam) and do some processing on them with OpenCV, but I'm having a difficult time..
When trying to convert the image to grayscale, the program crashes. (Python.exe has stopped working)
Here is the main snippet of my code:
newFrameImageGS = cv.CreateImage ((320, 240), cv.IPL_DEPTH_8U, 1)
for i in range(0,5):
newFrameImage = cv.QueryFrame(ps3eye)
cv.CvtColor(newFrameImage,newFrameImageGS,cv.CV_BGR2GRAY)
golfSwing.append(newFrameImageGS)
When I try using cvConvertScale I get the assertion error:
src.size() == dst.size() && src.channels() == dst.channels()
which makes sense, but I'm pretty confused on how to go about converting the input images of my web cam into images that can be used by functions like cvUpdateMotionHistory() and cvCalcOpticalFlowLK()
Any ideas? Thanks.
UPDATE:
I converted the image to grayscale manually with this:
for row in range(0,newFrameImage.height):
for col in range(0,newFrameImage.width):
newFrameImageGS[row,col] = (newFrameImage8U[row,col][0] * 0.114 + # B
newFrameImage8U[row,col][1] * 0.587 + # G
newFrameImage8U[row,col][2] * 0.299) # R
But this takes quite a while.. and i still can't figure out why cvCvtColor is causing the program to crash.
For some reason, CvtColor caused the program to crash when the image depths where 8 bit. When I converted them to 32 bit, the program no longer crashed and everything seemed to work OK. I have no idea why this is, but at least it works now.
newFrameImage = cv.QueryFrame(ps3eye)
newFrameImage32F = cv.CreateImage((320, 240), cv.IPL_DEPTH_32F, 3)
cv.ConvertScale(newFrameImage,newFrameImage32F)
newFrameImageGS_32F = cv.CreateImage ((320,240), cv.IPL_DEPTH_32F, 1)
cv.CvtColor(newFrameImage32F,newFrameImageGS_32F,cv.CV_RGB2GRAY)
newFrameImageGS = cv.CreateImage ((320,240), cv.IPL_DEPTH_8U, 1)
cv.ConvertScale(newFrameImageGS_32F,newFrameImageGS)
There is a common mistake here:
You're creating a single image in the newFrameImageGS variable before the loop, then overwrite its contents in the loop, which is then appended to a list. The result will not be what you would expect. The list will contain five references to the same image instance at the end, since only the object reference is appended to the list, no copy of the object made this way. This image will contain the very last frame, so you get five of that frame as a result, which is not what you want, I guess. Please review the Python tutorial if it is not clear for you. You can solve this by moving the first line of the above code into the body of the for loop.
Another possibilities if fixing the above would not help you:
The CvtColor function seems to be the correct one for conversion to grayscale, since it can convert to a different number of channels.
According to this manual the CvtColor function requires a destination image of the same data type as the source. Please double check that newFrameImage is a IPL_DEPTH_8U image.