463 lines
16 KiB
Python
463 lines
16 KiB
Python
# %%
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#
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# offline_encoding.py
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# ========================================================
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# encode visual scenes into sparse representations using
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# different kinds of dictionaries
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#
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# -> derived from OnlineEncoding.py
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#
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# Version 1.0, 16.04.2024:
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#
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# Import Python modules
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# ========================================================
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# import csv
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# import time
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# import os
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# import glob
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import matplotlib.pyplot as plt
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import torch
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import torchvision as tv # type:ignore
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# from PIL import Image
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import cv2
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import numpy as np
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import json
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from jsmin import jsmin # type:ignore
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# Import our modules
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# ========================================================
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from processing_chain.ContourExtract import ContourExtract
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from processing_chain.PatchGenerator import PatchGenerator
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from processing_chain.Sparsifier import Sparsifier
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# from processing_chain.DiscardElements import discard_elements_simple
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from processing_chain.BuildImage import BuildImage
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# from processing_chain.WebCam import WebCam
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# from processing_chain.Yolo5Segmentation import Yolo5Segmentation
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class OfflineEncoding:
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# INPUT PARAMETERS
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config: dict
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# DERIVED PARAMETERS
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default_dtype: torch.dtype
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torch_device: str
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display_size_max_x_pix: float
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display_size_max_y_pix: float
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# padding_fill: float
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# DEFINED PREVIOUSLY IN "apply_parameter_changes":
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padding_pix: int
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sigma_kernel_pix: float
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lambda_kernel_pix: float
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out_x: int
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out_y: int
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clocks: torch.Tensor
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phosphene: torch.Tensor
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clocks_filter: torch.Tensor
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# DELIVERED BY ENCODING
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position_found: None | torch.Tensor
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canvas_size: None | torch.Tensor
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def __init__(self, config="config.json"):
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# Define parameters
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# ========================================================
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print("OffE-Init: Loading configuration parameters...")
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with open(config, "r") as file:
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config = json.loads(jsmin(file.read()))
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# store in class
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self.config = config
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self.position_found = None
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self.canvas_size = None
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# get sub-dicts for easier access
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display = self.config["display"]
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dictionary = self.config["dictionary"]
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gabor = self.config["gabor"]
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# print(
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# "OE-Init: Defining paths, creating dirs, setting default device and datatype"
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# )
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# self.path = {"output": "test/output/level1/", "input": "test/images_test/"}
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# Make output directories, if necessary: the place were we dump the new images to...
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# os.makedirs(self.path["output"], mode=0o777, exist_ok=True)
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# Check if GPU is available and use it, if possible
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# =================================================
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self.default_dtype = torch.float32
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torch.set_default_dtype(self.default_dtype)
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if self.config["control"]["force_torch_use_cpu"]:
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torch_device = "cpu"
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else:
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torch_device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"Using {torch_device} as TORCH device...")
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self.torch_device = torch_device
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print("OffE-Init: Compute display scaling factors and padding RGB values")
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# global scaling factors for all pixel-related length scales
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self.display_size_max_x_pix = (
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display["size_max_x_dva"] * display["pix_per_dva"]
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)
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self.display_size_max_y_pix = (
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display["size_max_y_dva"] * display["pix_per_dva"]
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)
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# determine padding fill value
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tmp = tv.transforms.Grayscale(num_output_channels=1)
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tmp_value = torch.full((3, 1, 1), 254.0/255)
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self.padding_fill = int(tmp(tmp_value).squeeze())
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# PREVIOUSLY, A SEPARATE ROUTINE APPLIED PARAMETER CHANGES
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# WE DISCARD THIS HERE BUT KEEP THE CODE AS EXAMPLE
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#
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# self.apply_parameter_changes()
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# return
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#
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# def apply_parameter_changes(self):
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#
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# GET NEW PARAMETERS
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print("OffE-Init: Computing image/patch sizes from parameters")
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# BLOCK: dictionary ----------------
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# set patch size for both dictionaries, make sure it is odd number
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dictionary_size_pix = (
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1
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+ (int(dictionary["size_dva"] *
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display["pix_per_dva"]) // 2) * 2
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)
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# BLOCK: gabor ---------------------
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# convert contour-related parameters to pixel units
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self.sigma_kernel_pix = (
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gabor["sigma_kernel_dva"] *
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display["pix_per_dva"]
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)
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self.lambda_kernel_pix = (
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gabor["lambda_kernel_dva"] *
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display["pix_per_dva"]
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)
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# BLOCK: gabor & dictionary ------------------
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# Padding
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# -------
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self.padding_pix = int(
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max(3.0 * self.sigma_kernel_pix, 1.1 * dictionary_size_pix)
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)
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# define target video/representation width/height
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multiple_of = 4
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out_x = self.display_size_max_x_pix + 2 * self.padding_pix
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out_y = self.display_size_max_y_pix + 2 * self.padding_pix
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out_x += (multiple_of - (out_x % multiple_of)) % multiple_of
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out_y += (multiple_of - (out_y % multiple_of)) % multiple_of
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self.out_x = int(out_x)
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self.out_y = int(out_y)
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# generate dictionaries
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# ---------------------
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# BLOCK: dictionary --------------------------
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print("OffE-Init: Generating dictionaries...")
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patch_generator = PatchGenerator(torch_device=self.torch_device)
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self.phosphene = patch_generator.alphabet_phosphene(
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patch_size=dictionary_size_pix,
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sigma_width=dictionary["phosphene"]["sigma_width"]
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* dictionary_size_pix,
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)
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# BLOCK: dictionary & gabor --------------------------
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self.clocks_filter, self.clocks, segments = patch_generator.alphabet_clocks(
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patch_size=dictionary_size_pix,
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n_dir=dictionary["clocks"]["n_dir"],
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n_filter=gabor["n_orientations"],
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segment_width=dictionary["clocks"]["pointer_width"]
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* dictionary_size_pix,
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segment_length=dictionary["clocks"]["pointer_length"]
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* dictionary_size_pix,
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)
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return
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# TODO image supposed to be torch.Tensor(3, Y, X) within 0...1
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def encode(self, image: torch.Tensor, number_of_patches: int = 42, border_pixel_value: float = 254.0 / 255) -> dict:
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assert len(image.shape) == 3, "Input image must be RGB (3 dimensions)!"
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assert image.shape[0] == 3, "Input image format must be (3, HEIGHT, WIDTH)!"
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control = self.config["control"]
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# determine padding fill value
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tmp = tv.transforms.Grayscale(num_output_channels=1)
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tmp_value = torch.full((3, 1, 1), border_pixel_value)
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padding_fill = float(tmp(tmp_value).squeeze())
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# show input image, if desired...
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if control["show_image"]:
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self.__show_torch_frame(
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image,
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title="Encode: Input Image",
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target=control["show_mode"]
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)
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# some constants for addressing specific components of output arrays
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image_id_const: int = 0
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overlap_index_const: int = 1
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# Determine target size of image
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# image: [RGB, Height, Width], dtype= tensor.torch.uint8
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print("OffE-Encode: Computing downsampling factor image -> display")
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f_x: float = self.display_size_max_x_pix / image.shape[-1]
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f_y: float = self.display_size_max_y_pix / image.shape[-2]
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f_xy_min: float = min(f_x, f_y)
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downsampling_x: int = int(f_xy_min * image.shape[-1])
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downsampling_y: int = int(f_xy_min * image.shape[-2])
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# CURRENTLY we do not crop in the end...
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# Image size for removing the fft crop later
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# center_crop_x: int = downsampling_x
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# center_crop_y: int = downsampling_y
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# define contour extraction processing chain
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# ------------------------------------------
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print("OffE-Encode: Extracting contours")
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train_processing_chain = tv.transforms.Compose(
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transforms=[
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tv.transforms.Grayscale(num_output_channels=1), # RGB to grayscale
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tv.transforms.Resize(
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size=(downsampling_y, downsampling_x)
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), # downsampling
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tv.transforms.Pad( # extra white padding around the picture
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padding=(self.padding_pix, self.padding_pix),
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fill=padding_fill,
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),
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ContourExtract( # contour extraction
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n_orientations=self.config["gabor"]["n_orientations"],
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sigma_kernel=self.sigma_kernel_pix,
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lambda_kernel=self.lambda_kernel_pix,
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torch_device=self.torch_device,
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),
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# CURRENTLY we do not crop in the end!
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# tv.transforms.CenterCrop( # Remove the padding
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# size=(center_crop_x, center_crop_y)
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# ),
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],
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)
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# ...with and without orientation channels
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contour = train_processing_chain(image.unsqueeze(0))
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contour_collapse = train_processing_chain.transforms[-1].create_collapse(
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contour
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)
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if control["show_contours"]:
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self.__show_torch_frame(
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contour_collapse,
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title="Encode: Contours Extracted",
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cmap="gray",
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target=control["show_mode"],
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)
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# generate a prior for mapping the contour to the dictionary
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# CURRENTLY we use an uniform prior...
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# ----------------------------------------------------------
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dictionary_prior = torch.ones(
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(self.clocks_filter.shape[0]),
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dtype=self.default_dtype,
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device=torch.device(self.torch_device),
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)
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# instantiate and execute sparsifier
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# ----------------------------------
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print("OffE-Encode: Performing sparsification")
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encoding = self.config["encoding"]
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display = self.config["display"]
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sparsifier = Sparsifier(
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dictionary_filter=self.clocks_filter,
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dictionary=self.clocks,
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dictionary_prior=dictionary_prior,
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number_of_patches=encoding["n_patches_compute"],
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size_exp_deadzone=encoding["size_exp_deadzone_dva"]
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* display["pix_per_dva"],
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plot_use_map=False, # self.control["plot_deadzone"],
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deadzone_exp=encoding["use_exp_deadzone"],
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deadzone_hard_cutout=encoding["use_cutout_deadzone"],
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deadzone_hard_cutout_size=encoding["size_cutout_deadzone_dva"]
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* display["pix_per_dva"],
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padding_deadzone_size_x=self.padding_pix,
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padding_deadzone_size_y=self.padding_pix,
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torch_device=self.torch_device,
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)
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sparsifier(contour)
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assert sparsifier.position_found is not None
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# extract and normalize the overlap found
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overlap_found = sparsifier.overlap_found[
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image_id_const, :, overlap_index_const
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]
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overlap_found = overlap_found / overlap_found.max()
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# get overlap above certain threshold, extract corresponding elements
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overlap_idcs_valid = torch.where(
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overlap_found >= encoding["overlap_threshold"]
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)[0]
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position_selection = sparsifier.position_found[
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image_id_const : image_id_const + 1, overlap_idcs_valid, :
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]
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n_elements = len(overlap_idcs_valid)
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print(f"OffE-Encode: {n_elements} elements positioned!")
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contour_shape = contour.shape
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n_cut = min(position_selection.shape[-2], number_of_patches)
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data_out = {
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"position_found": position_selection[:, :n_cut, :],
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"canvas_size": contour_shape,
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}
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self.position_found = data_out["position_found"]
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self.canvas_size = data_out["canvas_size"]
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return data_out
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def render(self):
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assert self.position_found is not None, "Use ""encode"" before rendering!"
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assert self.canvas_size is not None, "Use ""encode"" before rendering!"
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control = self.config["control"]
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# build the full image!
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image_clocks = BuildImage(
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canvas_size=self.canvas_size,
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dictionary=self.clocks,
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position_found=self.position_found,
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default_dtype=self.default_dtype,
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torch_device=self.torch_device,
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)
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# normalize to range [0...1]
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m = image_clocks[0].max()
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if m == 0:
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m = 1
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image_clocks_normalized = image_clocks[0] / m
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# embed into frame of desired output size
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out_torch = self.__embed_image(
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image_clocks_normalized, out_height=self.out_y, out_width=self.out_x
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)
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# show, if desired...
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if control["show_percept"]:
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self.__show_torch_frame(
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out_torch, title="Percept",
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cmap="gray", target=control["show_mode"]
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)
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return
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def __show_torch_frame(self,
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frame_torch: torch.Tensor,
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title: str = "default",
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cmap: str = "viridis",
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target: str = "pyplot",
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):
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frame_numpy = (
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(frame_torch.movedim(0, -1) * 255).type(dtype=torch.uint8).cpu().numpy()
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)
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if target == "pyplot":
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plt.imshow(frame_numpy, cmap=cmap)
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plt.title(title)
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plt.show()
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if target == "cv2":
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if frame_numpy.ndim == 3:
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if frame_numpy.shape[-1] == 1:
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frame_numpy = np.tile(frame_numpy, [1, 1, 3])
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frame_numpy = (frame_numpy - frame_numpy.min()) / (
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frame_numpy.max() - frame_numpy.min()
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)
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# print(frame_numpy.shape, frame_numpy.max(), frame_numpy.min())
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cv2.namedWindow(title, cv2.WINDOW_NORMAL)
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cv2.imshow(title, frame_numpy[:, :, (2, 1, 0)])
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cv2.waitKey(1)
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return
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def __embed_image(self, frame_torch, out_height, out_width, init_value=0):
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out_shape = torch.tensor(frame_torch.shape)
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frame_width = frame_torch.shape[-1]
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frame_height = frame_torch.shape[-2]
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frame_width_idx0 = max([0, (frame_width - out_width) // 2])
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frame_height_idx0 = max([0, (frame_height - out_height) // 2])
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select_width = min([frame_width, out_width])
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select_height = min([frame_height, out_height])
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out_shape[-1] = out_width
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out_shape[-2] = out_height
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out_torch = init_value * torch.ones(tuple(out_shape))
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out_width_idx0 = max([0, (out_width - frame_width) // 2])
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out_height_idx0 = max([0, (out_height - frame_height) // 2])
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out_torch[
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...,
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out_height_idx0: (out_height_idx0 + select_height),
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out_width_idx0: (out_width_idx0 + select_width),
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] = frame_torch[
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...,
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frame_height_idx0: (frame_height_idx0 + select_height),
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frame_width_idx0: (frame_width_idx0 + select_width),
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]
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return out_torch
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def __del__(self):
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print("OffE-Delete: exiting gracefully!")
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# TODO ...only do it when necessary
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cv2.destroyAllWindows()
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return
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if __name__ == "__main__":
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source = 'bernd.jpg'
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img_cv2 = cv2.imread(source)
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img_torch = torch.Tensor(img_cv2[:, :, (2, 1, 0)]).movedim(-1, 0) / 255
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# show_torch_frame(img_torch, target="cv2", title=source)
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print(f"CV2 Shape: {img_cv2.shape}")
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print(f"Torch Shape: {img_torch.shape}")
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img = img_torch
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frame_width = img.shape[-1]
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frame_height = img.shape[-2]
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print(
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f"OffE-Test: Processing image {source} of {frame_width} x {frame_height}."
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)
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# TEST tfg = tv.transforms.Grayscale(num_output_channels=1)
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# TEST pixel_fill = torch.full((3, 1, 1), 254.0 / 255)
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# TEST value_fill = float(tfg(pixel_fill).squeeze())
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# TEST tfp = tv.transforms.Pad(padding=(1, 1), fill=value_fill)
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# TEST img_gray = tfg(img[:, :3, :3])
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# TEST img_pad = tfp(img_gray)
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oe = OfflineEncoding()
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encoding = oe.encode(img)
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stimulus = oe.render()
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if oe.config["control"]["show_mode"] == "cv2":
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cv2.waitKey(5000)
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del oe
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# %%
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