vmarkovtsev

{
  "embeddings": [
    {
      "tensorName": "GitLab developer experience clusters",
      "tensorShape": [
        173,
        258
      ],
      "tensorPath": "https://gist.githubusercontent.com/vmarkovtsev/4bd05fdd761e49a7bbbca87381e65c9c/raw/38801e9740529ac2c9ac7884fc3e8623bb37caad/data.tsv",
      "metadataPath": "https://gist.githubusercontent.com/vmarkovtsev/4bd05fdd761e49a7bbbca87381e65c9c/raw/38801e9740529ac2c9ac7884fc3e8623bb37caad/meta.tsv"

vmarkovtsev

from typing import Tuple, Union

import numpy as np
from skimage.draw import line_aa
import tensorflow as tf


def create_motion_blur_kernel(dim: int, angle: float) -> tf.Tensor:
    # Define a disk which contains the dim x dim square
    radius = np.sqrt(0.5 * dim**2)

vmarkovtsev

import matplotlib.pyplot as plt
plt.rcParams["image.cmap"] = "hot"

# 25x25 kernel pointing straight to the east
dim = 25
kernel = np.squeeze(create_motion_blur_kernel(dim, (90/180) * np.pi).numpy()[:, :, 0, 0])
plt.contourf(np.linspace(-dim//2, dim//2, dim), np.linspace(-dim//2, dim//2, dim), kernel)
plt.colorbar()
plt.show()

vmarkovtsev

from PIL import Image

def create_motion_blur_func(dim: int, angle: float):
    kernel = create_motion_blur_kernel(dim, angle)

    def motion_blur_func(images):
        return tf.nn.depthwise_conv2d(images, kernel, strides=[1] * 4, padding="SAME")
    
    return motion_blur_func

vmarkovtsev

import logging
from pathlib import Path


def create_motion_blur_func(images_shape: Tuple[int], dim: int, angle: float):
    kernel = create_motion_blur_kernel(dim, angle)

    # This is new: input signature definition
    @tf.function(input_signature=[tf.TensorSpec(images_shape, tf.float32)])
    def motion_blur_func(images):

vmarkovtsev

# Remember to remove the old model!
# rm *.tflite

import subprocess
from tensorflow.lite.python.interpreter import load_delegate


def create_motion_blur_func_edgetpu(images_shape, dim, angle, edgetpu=True):
    name = "motion_blur_%s_%d_%.2f" % ("_".join(map(str, images_shape)), dim, angle)
    ctor = lambda: create_motion_blur_func(images_shape, dim, angle)

vmarkovtsev

# Remember to remove the old model!
# rm *.tflite

import json
import urllib.request


def generate_edgetpu_model(log: logging.Logger, images_shape: Tuple[int], func: callable, name: str):
    """Convert tf.function to Edge TPU model."""

vmarkovtsev

def create_lanczos_resize_func_edgetpu(images_shape: Tuple[int], dim: int, factor: int, edgetpu=True):
    name = "lanczos_resize_%s_%d_%d" % ("_".join(map(str, images_shape)), dim, factor)
    ctor = lambda: create_lanczos_resize_func(images_shape, dim, factor)
    
    return create_func_edgetpu(images_shape, ctor, name, edgetpu)


def create_lanczos_resize_func(images_shape: Tuple[int], dim: int, factor: int):
    kernel = create_lanczos_resize_kernel(dim)
    

vmarkovtsev

# The kernel size is 15 instead of 5 for greater detail.
dim = 15
kernel = np.squeeze(create_lanczos_resize_kernel(dim).numpy()[:, :, 0, 0])
plt.contourf(np.linspace(-dim, dim, dim * 2 + 1), np.linspace(-dim, dim, dim * 2 + 1), kernel)
plt.colorbar()
plt.show()

vmarkovtsev

# Remember to remove the old model!
# rm *.tflite

from typing import Optional


def create_lanczos_resize_func_edgetpu(images_shape: Tuple[int], dim: int, factor: int, edgetpu=True):
    name = "lanczos_resize_%s_%d_%d" % ("_".join(map(str, images_shape)), dim, factor)
    ctor = lambda: create_lanczos_resize_func(images_shape, dim, factor)