ImageGrabPIL

Documentation

• Class name: ImageGrabPIL
• Category: KJNodes/experimental
• Output node: False

The ImageGrabPIL node is designed to capture and process images from the screen using the PIL library. It focuses on grabbing screen content, possibly for further manipulation or analysis within a larger image processing or computer vision workflow.

Input types

Required

• x
  • Specifies the starting x-coordinate for the screen capture area, affecting the horizontal position of the capture region.
  • Comfy dtype: INT
  • Python dtype: int
• y
  • Specifies the starting y-coordinate for the screen capture area, influencing the vertical position of the capture region.
  • Comfy dtype: INT
  • Python dtype: int
• width
  • Defines the width of the screen capture area, determining how wide the captured image will be.
  • Comfy dtype: INT
  • Python dtype: int
• height
  • Defines the height of the screen capture area, determining the height of the captured image.
  • Comfy dtype: INT
  • Python dtype: int
• num_frames
  • Indicates the number of frames to capture, useful for creating a sequence of images over time.
  • Comfy dtype: INT
  • Python dtype: int
• delay
  • Sets the delay between captures when multiple frames are being captured, controlling the timing of captures.
  • Comfy dtype: FLOAT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The captured image or sequence of images from the specified screen area.
  • Python dtype: PIL.Image.Image

Usage tips

• Infra type: CPU
• Common nodes:
  • VAEEncode
  • CannyEdgePreprocessor
  • PixelPerfectResolution

Source code

class ImageGrabPIL:

    @classmethod
    def IS_CHANGED(cls):

        return

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = "screencap"
    CATEGORY = "KJNodes/experimental"
    DESCRIPTION = """
Captures an area specified by screen coordinates.  
Can be used for realtime diffusion with autoqueue.
"""

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                 "x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
                 "y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
                 "width": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
                 "height": ("INT", {"default": 512,"min": 0, "max": 4096, "step": 1}),
                 "num_frames": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
                 "delay": ("FLOAT", {"default": 0.1,"min": 0.0, "max": 10.0, "step": 0.01}),
        },
    } 

    def screencap(self, x, y, width, height, num_frames, delay):
        captures = []
        bbox = (x, y, x + width, y + height)

        for _ in range(num_frames):
            # Capture screen
            screen_capture = ImageGrab.grab(bbox=bbox)
            screen_capture_torch = torch.tensor(np.array(screen_capture), dtype=torch.float32) / 255.0
            screen_capture_torch = screen_capture_torch.unsqueeze(0)
            captures.append(screen_capture_torch)

            # Wait for a short delay if more than one frame is to be captured
            if num_frames > 1:
                time.sleep(delay)

        return (torch.cat(captures, dim=0),)