Alignment for a whole directory

[sgtkellox]

I used your code to make some alignments and I like the result, yet I found it a bit inconvenient that you have to process the images one by one. I made some changes to process directorys of images, it works fine. Shall I submitt the code to you? Regards, Felix

[poynting]

Have you seen the Batch Processing notebook?

On Thu, Aug 12, 2021, 10:56 AM sgtkellox @.***> wrote:

I used your code to make some alignments and I like the result, yet I found it a bit inconvenient that you have to process the images one by one. I made some changes to process directorys of images, it works fine. Shall I submitt the code to you? Regards, Felix

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[XingWei19]

I used your code to make some alignments and I like the result, yet I found it a bit inconvenient that you have to process the images one by one. I made some changes to process directorys of images, it works fine. Shall I submitt the code to you? Regards, Felix

Hello Felix, I am working on batch processing RedEdge 5-band images. I would like to test your codes if you are willing to share them. Thanks, Xing

[sgtkellox]

I will have a look at that notebook. @XingWei19 ill paste my code here so you can use it. Ill assume that is ok. Here you go, hope it helps:

import os, glob
import micasense.capture as capture
import cv2
import numpy as np
import matplotlib.pyplot as plt
import micasense.imageutils as imageutils
import micasense.plotutils as plotutils
import imageio

path = path goes here 

os.mkdir(os.path.join(path,"cir_aligned"))
os.mkdir(os.path.join(path,"rgb_aligned"))

files = os.listdir(path)
names = []
for fileName in files:
    if fileName.endswith(".tif"):
        name = fileName.split("_",2)
        if not name[0]+"_"+name[1] in names:
            names.append(name[0]+"_"+name[1])

for name in names:
    panelNames = None

    imagePath = os.path.join(path)
    imageNames = glob.glob(os.path.join(imagePath,name+'_*.tif'))

    if panelNames is not None:
        panelCap = capture.Capture.from_filelist(panelNames)
    else:
        panelCap = None

    cap = capture.Capture.from_filelist(imageNames)

    if panelCap is not None:
        if panelCap.panel_albedo() is not None:
            panel_reflectance_by_band = panelCap.panel_albedo()
        else:
            panel_reflectance_by_band = [0.67, 0.69, 0.68, 0.61, 0.67] #RedEdge band_index order
        panel_irradiance = panelCap.panel_irradiance(panel_reflectance_by_band)    
        img_type = "reflectance"
        cap.plot_undistorted_reflectance(panel_irradiance)
    else:
        if cap.dls_present():
            img_type='reflectance'
            cap.plot_undistorted_reflectance(cap.dls_irradiance())
        else:
            img_type = "radiance"
            cap.plot_undistorted_radiance()
    match_index = 1 # Index of the band 
    max_alignment_iterations = 10
    warp_mode = cv2.MOTION_HOMOGRAPHY
    pyramid_levels = 0 

    print("Alinging images. Depending on settings this can take from a few seconds to many minutes")

    warp_matrices, alignment_pairs = imageutils.align_capture(cap,ref_index = match_index,max_iterations = max_alignment_iterations,warp_mode = warp_mode,pyramid_levels = pyramid_levels)

    print("Finished Aligning, warp matrices={}".format(warp_matrices))

    cropped_dimensions, edges = imageutils.find_crop_bounds(cap, warp_matrices, warp_mode=warp_mode)
    im_aligned = imageutils.aligned_capture(cap, warp_matrices, warp_mode, cropped_dimensions, match_index, img_type=img_type)

    figsize=(16,13)   # use this size for export-sized display

    rgb_band_indices = [cap.band_names_lower().index('red'),
                    cap.band_names_lower().index('green'),
                    cap.band_names_lower().index('blue')]
    cir_band_indices = [cap.band_names_lower().index('nir'),
                    cap.band_names_lower().index('red'),
                    cap.band_names_lower().index('green')]

    im_display = np.zeros((im_aligned.shape[0],im_aligned.shape[1],im_aligned.shape[2]), dtype=np.float32 )

    im_min = np.percentile(im_aligned[:,:,rgb_band_indices].flatten(), 0.5)  # modify these percentiles to adjust contrast
    im_max = np.percentile(im_aligned[:,:,rgb_band_indices].flatten(), 99.5)  # for many images, 0.5 and 99.5 are good values

    for i in rgb_band_indices:
        im_display[:,:,i] =  imageutils.normalize(im_aligned[:,:,i], im_min, im_max)

    rgb = im_display[:,:,rgb_band_indices]

    for i in cir_band_indices:
        im_display[:,:,i] =  imageutils.normalize(im_aligned[:,:,i])

    cir = im_display[:,:,cir_band_indices]
    fig, axes = plt.subplots(1, 2, figsize=figsize)

    gaussian_rgb = cv2.GaussianBlur(rgb, (9,9), 10.0)
    gaussian_rgb[gaussian_rgb<0] = 0
    gaussian_rgb[gaussian_rgb>1] = 1
    unsharp_rgb = cv2.addWeighted(rgb, 1.5, gaussian_rgb, -0.5, 0)
    unsharp_rgb[unsharp_rgb<0] = 0
    unsharp_rgb[unsharp_rgb>1] = 1

    gamma = 1.4
    gamma_corr_rgb = unsharp_rgb**(1.0/gamma)
    fig = plt.figure(figsize=figsize)
    imtype = 'jpg' # or 'jpg'

    imageio.imwrite(os.path.join(path,"rgb_aligned",name+'rgb.'+imtype), (255*gamma_corr_rgb).astype('uint8'))
    imageio.imwrite(os.path.join(path,"cir_aligned",name+'cir.'+imtype), (255*cir).astype('uint8'))

[poynting]

It's not necessary to align each capture each time. It's much faster to use one image with good features to find an alignment matrix, then use that to align all of the images from the same capture session.

The batch processing notebook does this, using the output of the alignment notebook.

The 10-band Batch Processing notebook further improves this by using the imgset.save_stacks function which is internally multithreaded. The 10-band approach can also be used with 5-band or 6-band data.

[XingWei19]

@sgtkellox Thank you for sharing your codes. I appreciate it.

[XingWei19]

@poynting When I testing the 10-band Batch Processing notebook, I got an error saying, "ImageSet' object has no attribute 'save_stacks". Do I need to update the library/package to use this function? Thanks

[karimelgazar]

use imgset.process_imageset instead

[zekrimohamed]

@poynting When I tested the 10-band Batch Processing notebook, I got an error saying, "ImageSet' object has no attribute 'save_stacks". Do I need to update the library/package to use this function? Thanks

[YY-99999]

Hi @poynting

I am having same issue "ImageSet' object has no attribute 'save_stacks". I have tried imgset.process_imageset and it seemed not working as well. Any ideas ? Many Thanks

[alegent]

I am having same errors on using both imgset.save_stacks() and imgset.process_imageset() . Any progress on this issue?