How to check is pixel transparent in OpenCV? I have a png image with transparent portions and I want to convert rgb image to hsv and then change hue of pixels. I need that transparent pixels remain transparent after the conversion.
Any help please.
You may try GDAL. It is compatible with CV2
These links may be useful.
Reading Raster Data with GDAL
GDAL API Tutorial
import gdal
from gdalconst import *
import numpy as np
ds = gdal.Open('lena.jpg', GA_ReadOnly)
B = ds.GetRasterBand(1)
G = ds.GetRasterBand(2)
R = ds.GetRasterBand(3)
A = ds.GetRasterBand(4) // Alpha
height, width = B.shape
img = np.zeros(height, width, 3)
img[:, :, 0] = B
img[:, :, 1] = G
img[:, :, 2] = R
// Do something you want
ds.GetRasterBand(1).WriteArray(new_B)
ds.GetRasterBand(2).WriteArray(new_G)
ds.GetRasterBand(3).WriteArray(new_R)
// The forth band dose not need to be changed
// Close image, the changes is writen in the source file
ds = None
// Note that I did not test this code
OpenCV does not support transperancy in images. (before v2.4, I'm not sure about the latest version)
You can try the solution at http://blog.developer.stylight.de/2010/05/how-to-load-alpha-channel-pngs-with.html and rebuild OpenCV, or you can use something like ImageMagick to extract the alpha layer (forum link) as a separate image and load it.
Related
I have a set of images similar to this one:
And for each image, I have a text file with bounding box regions expressed in normalized pixel values, YOLOv5 format (a text document with rows of type: class, x_center, y_center, width, height). Here's an example:
3 0.1661542727623449 0.6696164480452673 0.2951388888888889 0.300925925925926
3 0.41214353459362196 0.851908114711934 0.2719907407407405 0.2961837705761321
I'd like to obtain a new dataset of masked images, where the bounding box area from the original image gets converted into white pixels, and the rest gets converted into black pixels. This would be and example of the output image:
I'm sure there is a way to do this in PIL (Pillow) in Python, but I just can't seem to find a way.
Would somebody be able to help?
Kindest thanks!
so here's the answer:
import os
import numpy as np
from PIL import Image
label=open(os.path.join(labPath, filename), 'r')
lines=label.read().split('\n')
square=np.zeros((1152,1152))
for line in lines:
if line!='':
line=line.split() #line: class, x, y, w, h
left=int((float(line[1])-0.5*float(line[3]))*1152 )
bot=int((float(line[2])+0.5*float(line[4]))*1152)
top=int(bot-float(line[4])*1152)
right=int(left+float(line[3])*1152)
square[top:bot, left:right]=255
square_img = Image.fromarray(square)
square_img=square_img.convert("L")
Let me know if you have any questions!
Hi, just posting this on behalf of my 10yo son. He's working on a Python/OpenCV/GUI application and having some issues. Hoping someone might be able to point him in the right direction. Information as per below (maybe he needs to take a different approach?)
At the moment in my project I am having a problem with no errors. The only problem is the code isn't doing exactly what I want it to be. I can not tell if the blur is too strong, the blur is making the circle detection more sensitive or something else. My code is below.
I am trying to make the circle detection less sensitive by using a blur, however I can not tell what it's doing because there is no error.
What I want it to do is:
blur the image
ensure the circle detection is not to sensitive (not too many circles)
show the image unblurred and on the unblurred image show the circles from the blurred image
For an example, I should be able to detect moon craters.
import tkinter as tk
from tkinter import filedialog
from PIL import ImageTk, Image
import numpy as np
import cv2
root = tk.Tk()
root.title("Circle detecter")
root.geometry("1100x600")
root.iconbitmap('C:/Users/brett/')
def open():
global my_image
filename = filedialog.askopenfilename(initialdir="images", title="Select A File", filetypes=(("jpg files", "*.jpg"),("all files", "*.*")))
my_label.config(text=filename)
my_image = Image.open(filename)
tkimg = ImageTk.PhotoImage(my_image)
my_image_label.config(image=tkimg)
my_image_label.image = tkimg # save a reference of the image
def find_circles():
# convert PIL image to OpenCV image
circles_image = np.array(my_image.convert('RGB'))
gray_img = cv2.cvtColor(circles_image, cv2.COLOR_BGR2GRAY)
img = cv2.medianBlur(gray_img, 5)
circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 20,
param1=20, param2=60, minRadius=20, maxRadius=200)
if circles is not None:
circles = np.uint16(np.around(circles))
for i in circles[0]:
# draw the outer circle
cv2.circle(circles_image, (i[0],i[1]), i[2], (0,255,0), 2)
# draw the center of the circle
cv2.circle(circles_image, (i[0],i[1]), 2, (0,0,255), 3)
# convert OpenCV image back to PIL image
image = Image.fromarray(circles_image)
# update shown image
my_image_label.image.paste(image)
tk.Button(root, text="Load Image", command=open).pack()
tk.Button(root, text="Find circles", command=find_circles).pack()
# for the filename of selected image
my_label = tk.Label(root)
my_label.pack()
# for showing the selected image
my_image_label = tk.Label(root)
my_image_label.pack()
root.mainloop()
As show in the above image, when I read it with pillow:
from PIL import Image
label = Image.open('example.png')
print(np.unique(array(label)))
The number are within range of [0, 34], which is correct. However, when I read with cv2:
import cv2
label = cv2.imread('example.png')
print(np.unique(label))
The number are with [0, 255] which is not correct in my application. How could I align the behavior of cv2 and pil please ?
Also when I checked the matlab example code parsing this image, it is written like this:
[labels, color_mappings] = imread('example.png')
It seems that the png file has two data fields, one is the fields with values ranges from 0 to 34, and the other is the color pixels, how could I parse it with cv2?
I think Dan has the right answer, but if you want to do some "quick and dirty" testing, you could use the following code to:
convert your palette image into a single channel greyscale PGM image of the palette indices that OpenCV can read without any extra libraries, and a separate palette file that you can apply back afterwards
load back a PGM file of the indices that OpenCV may have altered, and reapply the saved palette
#!/usr/bin/env python3
import numpy as np
from PIL import Image
# Open palette image and remove pointless alpha channel
im = Image.open('image.png').convert('P')
# Extract palette and save as CSV
np.array(im.getpalette()).tofile('palette.csv',sep=',')
# Save palette indices as single channel PGM image that OpenCV can read
na = np.array(im)
im = Image.fromarray(na).save('indices.pgm')
So that will have split image.png into indices.pgm that OpenCV can read as a single channel image and palette.csv that we can reload later.
And here is the second part, where we rebuild the image from indices.pgm and palette.csv
# First load indices
im = Image.open('indices.pgm')
# Now load palette
palette = np.fromfile('palette.csv',sep=',').astype(np.uint8)
# Put palette back into image
im.putpalette(palette)
# Save
im.save('result.png')
Remember not to use any interpolation other than NEAREST_NEIGHBOUR in OpenCV else you will introduce new colours not present in the original image.
Keywords: Python, PNG, image processing, palette, palette indices, palette index
I am using Kinect v1 and I want to get the depth image in greyscale mode from the channel "/camera/depth_registered/image" in ROS. As I found here, I can do it by using the function imgmsg_to_cv2. The default desired_encoding for my depth messages is "32FC1", which I keep. The problem is that when I use the cv2.imshow() function to show it, I get the image in binary... When I do the same for the RGB image everything is being shown just fine...
Any help appreciated!
So after all, I found a solution, which you can see here:
def Depthcallback(self,msg_depth): # TODO still too noisy!
try:
# The depth image is a single-channel float32 image
# the values is the distance in mm in z axis
cv_image = self.bridge.imgmsg_to_cv2(msg_depth, "32FC1")
# Convert the depth image to a Numpy array since most cv2 functions
# require Numpy arrays.
cv_image_array = np.array(cv_image, dtype = np.dtype('f8'))
# Normalize the depth image to fall between 0 (black) and 1 (white)
# http://docs.ros.org/electric/api/rosbag_video/html/bag__to__video_8cpp_source.html lines 95-125
cv_image_norm = cv2.normalize(cv_image_array, cv_image_array, 0, 1, cv2.NORM_MINMAX)
# Resize to the desired size
cv_image_resized = cv2.resize(cv_image_norm, self.desired_shape, interpolation = cv2.INTER_CUBIC)
self.depthimg = cv_image_resized
cv2.imshow("Image from my node", self.depthimg)
cv2.waitKey(1)
except CvBridgeError as e:
print(e)
However, the result is not that perfect as the one I get from the image_view node of ROS, but it is still pretty acceptable!
I want to display original image left side and grayscale image on right side. Below is my code, I create grayscale image and create window, but I couldn't put grayscale image to right side. How can I do this?
import cv
import time
from PIL import Image
import sys
filePath = raw_input("file path: ")
filename = filePath
img = cv.LoadImage(filename)
imgGrayScale = cv.LoadImage(filename, cv.CV_LOAD_IMAGE_GRAYSCALE) # create grayscale image
imgW = img.width
imgH = img.height
cv.NamedWindow("title", cv.CV_WINDOW_AUTOSIZE)
cv.ShowImage("title", img )
cv.ResizeWindow("title", imgW * 2, imgH)
cv.WaitKey()
First concatenate the images either horizontally (across columns) or vertically (across rows) and then display it as a single image.
import numpy as np
import cv2
from skimage.data import astronaut
import scipy.misc as misc
img=cv2.cvtColor(astronaut(),cv2.COLOR_BGR2RGB)
numpy_horizontal_concat = np.concatenate((img, img), axis=1)
cv2.imshow('Numpy Horizontal Concat', numpy_horizontal_concat)
As far as I know, one window, one image. So create a new image with imgW*2 and copy the contents of the grayscale image at the region starting from (originalimage.width,0). The ROI capabilities may be helpful to you.