20180809-stitch_benchmark-pixel_grid.py

from __future__ import division, print_function 

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

from skimage.feature import match_template
import skimage.io

def getMatchedSubImg(partialImg, fullImg, debug=False):
    result = match_template(fullImg, partialImg)
    ij = np.unravel_index(np.argmax(result), result.shape)
    x, y = ij[::-1]
    imgShape = partialImg.shape
    if debug:
        plotMatcchTemplate(partialImg, fullImg, result)
    return fullImg[y:y+imgShape[0], x:x+imgShape[1]]

def plotMatcchTemplate(partialImg, fullImg, result):
    ij = np.unravel_index(np.argmax(result), result.shape)
    x, y = ij[::-1]
    fig = plt.figure(figsize=(8, 3))
    ax1 = plt.subplot(1, 3, 1)
    ax2 = plt.subplot(1, 3, 2)
    ax3 = plt.subplot(1, 3, 3, sharey=ax2, sharex=ax2)

    ax1.imshow(partialImg, cmap=plt.cm.gray)
    ax1.set_axis_off()
    ax1.set_title('Partial image')

    ax2.imshow(fullImg, cmap=plt.cm.gray)
    ax2.set_axis_off()
    ax2.set_title('Full image')
    # highlight matched region
    hcoin, wcoin = partialImg.shape
    rect = plt.Rectangle((x, y), wcoin, hcoin, edgecolor='r', facecolor='none')
    ax2.add_patch(rect)

    r2 = np.zeros(fullImg.shape)
    r2[:result.shape[0], :result.shape[1]] += result

    ax3.imshow(r2)
    ax3.set_axis_off()
    ax3.set_title('NCC result')
    # # highlight matched region
    ax3.autoscale(False)
    ax3.plot(x, y, 'o', markeredgecolor='r', markerfacecolor='none', markersize=10)

    plt.show()
    return

def pixelDiffBinarizedWithGate (img1, img2, gate=5000):
    return (img1 > gate) != (img2 > gate)

def plotBinnedPixelDiff (img, binNumPerRow=50, maxFactor=0.3):
    rowBase = int(img.shape[0] / binNumPerRow)
    colBase = int(img.shape[1] / binNumPerRow)

    gridheat = np.array([[np.sum(img[rowBase*j:rowBase*(j+1), colBase*i:colBase*(i+1)]) for i in range(binNumPerRow)] for j in range(binNumPerRow)])
    plt.imshow(gridheat, norm=matplotlib.colors.Normalize(vmin=0, vmax=rowBase*colBase*maxFactor, clip=False))
    
import os

filesPath = '20180810_batch/'
stitchTool = 'mist'
stitchedImgsPath = '20180810_batch/si_even_mosaic_' + stitchTool + '.tif'
files = os.listdir(filesPath)

refImgs = [f for f in files if ('mosaic' not in f and 'tif' in f)]
stitchedImg = skimage.io.imread(stitchedImgsPath)

for fName in refImgs:
    refImg = skimage.io.imread(filesPath + fName)[:,:,0]
    matched = getMatchedSubImg(refImg, stitchedImg)
    skimage.io.imsave(filesPath + stitchTool + '/' + fName.split('.')[0] + '-' + stitchTool + '.tif', matched)
    diff = pixelDiffBinarizedWithGate(refImg, matched)
    plotBinnedPixelDiff(diff, 20, 0.3)

def plotBinnedPixelDiffAll(refImgs, stitchTool, binNum, factor):
    result = []
    for fName in refImgs:
        refImg = skimage.io.imread(filesPath + fName)[:,:,0]
        paredImg = skimage.io.imread(filesPath + stitchTool + '/' + fName.split('.')[0] + '-' + stitchTool + '.tif')
        print(filesPath + stitchTool + '/' + fName.split('.')[0] + '-' + stitchTool + '.tif')
        result.append(pixelDiffBinarizedWithGate(refImg, paredImg))
    plotBinnedPixelDiff(np.array(result).sum(axis=0)/12, binNum, factor)