Intensity -> Radial mapping for SWIM waveform generation from single LED SWMs

Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "invisible-industry",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image, ImageFilter, ImageOps\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "veterinary-trademark",
   "metadata": {},
   "outputs": [],
   "source": [
    "A0 = Image.open('base.png')\n",
    "A = np.array(ImageOps.grayscale(A0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "marine-scottish",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "SCALE = 1.0\n",
    "\n",
    "# W = np.where(M > 50)\n",
    "# Z0 = np.mean(W, axis=1)[:,np.newaxis]\n",
    "# Vhat = np.array(W) - Z0\n",
    "# Vhat /= np.linalg.norm(Vhat, axis=0)\n",
    "\n",
    "Aout = Image.new('RGB', A0.size, 'black')\n",
    "Aout_pxs = Aout.load()\n",
    "\n",
    "X, Y = np.meshgrid(np.arange(A.shape[1]), np.arange(A.shape[0]))\n",
    "Aflat = A.flatten()\n",
    "XY = Z0 + (np.stack((X.flatten(), Y.flatten()), axis=0) - Z0) * (1 + Aflat / 256) * SCALE\n",
    "for (x, y), a in zip(XY.T, Aflat):\n",
    "    try:\n",
    "        Aout_pxs[int(x), int(y)] = (0, Aout_pxs[int(x), int(y)][1] + int(math.sqrt(a / 256) * 256),0)\n",
    "    except IndexError:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "proved-behavior",
   "metadata": {},
   "outputs": [],
   "source": [
    "Aout.save('swimsoft-current-mask.png')"
   ]
  }
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vproc_live.py

#!/usr/bin/env python
from PIL import Image, ImageFilter, ImageOps
import numpy as np
import math
import scipy.sparse
import cv2
import pdb
import sys
import pyvirtualcam

V = cv2.VideoCapture(0)
DOWNSCALE = 2

H, W = int(V.get(cv2.CAP_PROP_FRAME_HEIGHT) / DOWNSCALE), int(V.get(cv2.CAP_PROP_FRAME_WIDTH) / DOWNSCALE)

with pyvirtualcam.Camera(width=W, height=H, fps=20, fmt=pyvirtualcam.PixelFormat.BGR) as Vout:

	codec = cv2.VideoWriter_fourcc(*'MP4V')

	SKIP = 4
	YX = np.mgrid[0:H,0:W]
	C = (0, np.zeros((2,))) # weight, coord
	ALPHA = 0.95 # exponential factor
	SCALE = 1
	frame = 0

	frame_buf = np.zeros((H, W, 3, 2))
	while V.isOpened():
		frame += 1
		valid, im_raw = V.read()
		if not valid:
			break
		#	 if frame % SKIP == 0:
		#	 continue
		
		im_raw = im_raw[(DOWNSCALE-1)::DOWNSCALE,(DOWNSCALE-1)::DOWNSCALE]
		im = im_raw[:,:,0] # blue only
		
		Etot = max(1, im.sum()) # max(1, im_thresh.sum())
		alpha_ = np.arctan(C[0] / Etot * math.tan(ALPHA * math.pi / 2)) / (math.pi / 2)
		C = (
		alpha_ * C[0] + (1 - alpha_) * Etot,
		alpha_ * C[1] + (1 - alpha_) * np.sum(im * YX, axis=(1,2)) / Etot
		)
		c_ = C[1][:,np.newaxis]
		
		BLUR_SIZE = 5
		imflat = im.flatten().astype(np.uint32) # * (BLUR_SIZE ** 2)
		xy_ = np.maximum((c_ + (YX.reshape((2,-1)) - c_) * (1 + imflat / 256) * SCALE).astype(np.int32), 0)
		
		gray_raw = scipy.sparse.coo_matrix((imflat, xy_)).toarray() # np.sqrt(imflat) * (255 / np.sqrt(255))
		frame_out = np.zeros((H, W, 3))
		frame_out[:,:,0:2] = gray_raw[:H, :W, np.newaxis] #  np.tile( # duplicates are summed together
		# Image.fromarray(frame_out).show()
		
		#	 sys.stdout.write('\r%d' % frame)
		frame_buf[:,:,:,frame%(frame_buf.shape[3])] = frame_out
		
		frame_out_b = np.minimum(255, frame_buf.sum(axis=3)).astype(np.uint8)
		frame_comp = cv2.add(cv2.boxFilter(frame_out_b, -1, (3,3), normalize=False), im_raw)
		cv2.circle(frame_comp, C[1][::-1].astype(np.uint32), 3, (0, 0, 255), -1)
		
		Vout.send(frame_comp)
		
		sys.stdout.write('\r%d' % frame)
		
		
	V.release()
	cv2.destroyAllWindows()