87 lines
3.1 KiB
Python
87 lines
3.1 KiB
Python
"""
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Capture remote URL
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"""
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import time
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from typing import Dict
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import torch
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from comfy.utils import ProgressBar
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from cozy_comfyui import \
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EnumConvertType, \
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logger, \
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deep_merge, parse_param, zip_longest_fill
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from cozy_comfyui import RGBAMaskType
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from cozy_comfyui.image.convert import cv_to_tensor_full
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from . import VideoStreamNodeHeader
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from .stream import MediaStreamBase
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# ==============================================================================
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# === NODE ===
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# ==============================================================================
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class RemoteSteamReader(VideoStreamNodeHeader):
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NAME = "REMOTE"
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DESCRIPTION = """
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Capture frames from a URL. Supports batch processing, allowing multiple frames to be captured simultaneously. The node provides options for configuring the source, resolution, frame rate, zoom, orientation, and interpolation method. Additionally, it supports capturing frames from multiple monitors or windows simultaneously.
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"""
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@classmethod
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def INPUT_TYPES(cls) -> Dict[str, str]:
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d = super().INPUT_TYPES()
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return deep_merge({
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"optional": {
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"URL": ("STRING", {"default": "", "dynamicPrompts": False,
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"tooltip":"A remote URL to stream"})
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}
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}, d)
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def run(self, **kw) -> RGBAMaskType:
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# need to see if we have a device...
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# http://63.142.190.238:6106/mjpg/video.mjpg
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if self.device is None:
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self.device = MediaStreamBase()
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images = []
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self.device.url = parse_param(kw, "URL", EnumConvertType.STRING, "")[0]
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self.device.fps = parse_param(kw, "FPS", EnumConvertType.INT, 30)[0]
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batch_size = parse_param(kw, "BATCH", EnumConvertType.INT, 1, 1)[0]
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if parse_param(kw, "PAUSE", EnumConvertType.BOOLEAN, False)[0]:
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self.device.pause()
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else:
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self.device.play()
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self.device.timeout = parse_param(kw, "TIMEOUT", EnumConvertType.INT, 5, 1, 30)[0]
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flip = parse_param(kw, "FLIP", EnumConvertType.BOOLEAN, False)
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reverse = parse_param(kw, "REVERSE", EnumConvertType.BOOLEAN, False)
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rate = 1. / self.device.fps
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pbar = ProgressBar(batch_size)
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batch_size = [batch_size] * batch_size
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params = list(zip_longest_fill(batch_size, flip, reverse))
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for idx, (batch_size, flip, reverse) in enumerate(params):
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start_time = time.perf_counter()
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self.device.flip = flip
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self.device.reverse = reverse
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while True:
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if not (img := self.device.frame) is None and img.sum() > 0:
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break
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if time.perf_counter() - start_time > self.device.timeout:
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logger.error("could not capture device")
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img = self.empty
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break
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images.append(cv_to_tensor_full(img))
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if batch_size > 1:
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time.sleep(rate)
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pbar.update_absolute(idx)
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if len(images) == 0:
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images.append(self.__empty)
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self.__last = [torch.stack(i) for i in zip(*images)]
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return self.__last
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