I have a short program that generates images of 1, 2, 3, and 4 random coloured circles to train a neural network on. My issue is, just doing 4000 images takes about 20-30 mins, and I need about 50000 images. My current method is to create the image, screenshot it, and then delete the tkinter object and restart. The issue is, windows 11 has a little fade in/slide animation when a new window is created, only about 200 ms, but it adds up quite a bit, because I need to wait for the animation to finish to take the screenshot.
So my question is, Is there another way to save a tkinter canvas other than screenshot?
I want to point out that I am putting these images into a numpy array, so putting it directly into an array is an option, but I need some way to save that as a file, so I dont need to generate the images every time.
My current code (only showing how I make 4 circles)
from PIL import ImageGrab
from tkinter import *
from random import choice, randint
colors = ["red", "blue", "green", "yellow", "orange", "purple", "black"]
s = 1 #scale 1 = 50x50 px canvas, 20x20 px circles
def four(i):
def ss():
x, y = screen.winfo_rootx(), screen.winfo_rooty()
w, h = screen.winfo_width(), screen.winfo_height()
img = ImageGrab.grab((x, y, x w, y h))
img.save(f"4MC{i}.jpg")
def des():
root.destroy()
root = Tk()
screen = Canvas(root, width = 50 * s, height = 50 * s, bg="white")
screen.pack()
colors = ["red", "blue", "green", "yellow", "orange", "purple", "black"]
x = randint(1 * s, 19 * s)
y = randint(1 * s, 19 * s)
screen.create_oval(x, y, x 10 * s, y 10 * s, fill=choice(colors), outline="")
screen.create_oval(x, y 20 * s, x 10 * s, y 30 * s, fill=choice(colors), outline="")
screen.create_oval(x 20 * s, y, x 30 * s, y 10 * s, fill=choice(colors), outline="")
screen.create_oval(x 20 * s, y 20 * s, x 30 * s, y 30 * s, fill=choice(colors), outline="")
root.after(200, ss)
root.after(300, des)
root.mainloop()
for i in range(1000):
four(i)
CodePudding user response:
I think your approach of destroying and creating a new window over each iteration is tedious way of doing it. Instead, you can clear the canvas each time and keep creating this random circles and then click a picture of it.
from PIL import ImageGrab
from tkinter import *
from random import choice, randint
root = Tk()
colors = ["red", "blue", "green", "yellow", "orange", "purple", "black"]
s = 1 #scale 1 = 50x50 px canvas, 20x20 px circles
i = 1 # Variable for creating numbers in file-name: 1,2,3,...
LIMIT = 10 # Variable to keep limit of how many iterations/image
def create(i):
if i <= LIMIT:
screen.delete('all')
x = randint(1 * s, 19 * s)
y = randint(1 * s, 19 * s)
screen.create_oval(x , y , x 10 * s, y 10 * s, fill=choice(colors), outline="")
screen.create_oval(x , y 20 * s, x 10 * s, y 30 * s, fill=choice(colors), outline="")
screen.create_oval(x 20 * s, y , x 30 * s, y 10 * s, fill=choice(colors), outline="")
screen.create_oval(x 20 * s, y 20 * s, x 30 * s, y 30 * s, fill=choice(colors), outline="")
if i == 1: # If it is first iteration, then the event loop hasnt been entered, so give a delay
root.after(200, capture, screen, f'4MC{i}')
else:
# Give a general delay of 100ms before capturing the image
root.after(100, capture, screen, f'4MC{i}')
i = 1
root.after(300, create, i) # Give a delay of 300ms before creating the circle
def capture(wid, file_name='img',file_format='png'):
"""Take screenshot of the passed widget"""
x0 = wid.winfo_rootx()
y0 = wid.winfo_rooty()
x1 = x0 wid.winfo_width()
y1 = y0 wid.winfo_height()
im = ImageGrab.grab(bbox=(x0, y0, x1, y1)) # bbox means boundingbox, which is shown in the image below
im.save(f'{file_name}.{file_format}') # Can also say im.show() to display it
screen = Canvas(root, width = 50 * s, height = 50 * s, bg="white")
screen.pack()
create(i)
root.mainloop()
I replaced your ss
with my capture
which is taken from another answer of mine, the logic is same but adds more flexibility, you can use your function with necessary changes. I suggest you first run this with a LIMIT = 10
and check if the delay are okay, if not you can adjust it and then move on to produce your 50000 image-set.
If you furthermore want to convert/load your image as a numpy
array, then check the first reference link below.
Extra references: