Matlab: Convert TIF to PNG

file.

Matlab: Convert the segmented image into a labeled file

file

1. Labeling images using LabKit for semantic segmentation

ENVI: Instructions for converting ENVI format classification maps to GEOTIFF format

[1] Open the ENVI format classification image, such as image 2003; at this point, the image's save format is ENVI Classification, which can be checked by editing the image's header file (Edit Header). The method is to right-click on 2003 in the Available Bands List, and from the pop-up menu select Edit Header, as shown in the image below;

[2] In the Header Info dialog box, select TIFF from the File Type dropdown list and click OK. Afterward, the icon for 2003 changes, as shown below;

[3] In the ENVI main menu, select File->Save File As->TIFF/GEOTIFF, choose the output location in the dialog box, click OK to finish.

Matlab: a simple approach for classifing two kinds of types on the image

It works well on small areas (here at the Plot level) and only with two types in the image that have significant color differences. File.

I added a loop to minimize the effect of the non-Rgeion of Interest on the binzrization process. 2nd. 3rd.

The conditions for applying the binary process are quite stringent. It's important to ensure that the image to be processed contains fairly typical features of the terrain colors and in appropriate quantities. Only then is it the right image to use, as this minimizes the area of invalid regions. 4th.

Matlab: Convert RGB to HSV/XYZ/LAB/Ycbcr/NTSC

File. 2nd.

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% Created by LI Xu % Version 1.0 % February 22, 2024 % If you have any question about this code, % please do not hesitate to contact me via E-mail: % jeremy456@163.com % Blog: % http://blog.sciencenet.cn/u/lixujeremy clear; clc; timebegin=tic; cur_data=date; cur_time=fix(clock); str1=sprintf('%s %.2d:%.2d:%.2d', cur_data, cur_time(4), cur_time(5), cur_time(6)); fprintf('Time Begin: '); fprintf(str1); fprintf('\n'); filename='wheat'; % Source Directory SouDir='input'; % Destination Directory DesDir='output'; files=dir(SouDir); files=files(3:end); for ii=1:numel(files) % construct the input file path filepath=fullfile(SouDir, files(ii).name); [image, geo]=readgeoraster(filepath); image=double(image); try info=geotiffinfo(filepath); catch info=georasterinfo(filepath); end Num_bands=size(image, 3); if Num_bands==3 RGB=image; else RGB=zeros(size(image, 1), size(image, 2), 3); RGB(:, :, 1)=image(:, :, 4); RGB(:, :, 2)=image(:, :, 2); RGB(:, :, 3)=image(:, :, 1); end % Normalise the RGB bands [0, 255] RGB=GenNormalise(RGB); % https://www.mathworks.com/help/images/understanding-color-spaces-and-color-space-conversion.html xyz=rgb2xyz(RGB); lab=rgb2lab(RGB); ycbcr=rgb2ycbcr(RGB); ntsc=rgb2ntsc(RGB); HSV=rgb2hsv(RGB); % ExG = 2 * G - R - B ExG=2*RGB(:, :, 2)-RGB(:, :, 1)-RGB(:, :, 3); otpath=strsplit(files(ii).name, '_'); otpath=otpath(2:end); otpath=strjoin(otpath, '_'); otpath_rgb=[filename, '_rgb_', otpath]; otpath_hsv=[filename, '_hsv_', otpath]; otpath_xyz=[filename, '_xyz_', otpath]; otpath_lab=[filename, '_lab_', otpath]; otpath_ycbcr=[filename, '_ycbcr_', otpath]; otpath_ntsc=[filename, '_ntsc_', otpath]; otpath_exg=[filename, '_exG_', otpath]; otpath_rgb=fullfile(DesDir, otpath_rgb); otpath_hsv=fullfile(DesDir, otpath_hsv); otpath_xyz=fullfile(DesDir, otpath_xyz); otpath_lab=fullfile(DesDir, otpath_lab); otpath_ycbcr=fullfile(DesDir, otpath_ycbcr); otpath_ntsc=fullfile(DesDir, otpath_ntsc); otpath_exg=fullfile(DesDir, otpath_exg); try geotiffwrite(otpath_rgb, RGB, geo); geotiffwrite(otpath_hsv, HSV, geo); geotiffwrite(otpath_xyz, xyz, geo); geotiffwrite(otpath_lab, lab, geo); geotiffwrite(otpath_ycbcr, ycbcr, geo); geotiffwrite(otpath_ntsc, ntsc, geo); geotiffwrite(otpath_exg, ExG, geo); catch strcmd=['gdalinfo ', filepath]; [~, cmdout]=system(strcmd); epsg=extractBetween(cmdout, "EPSG:"," got from GeoTIFF keys"); epsg=epsg{1}; % geotiffwrite(otpath, uint8(class_imag), geo, 'GeoKeyDirectoryTag', info.GeoTIFFTags.GeoKeyDirectoryTag); geotiffwrite(otpath_rgb, RGB, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); geotiffwrite(otpath_hsv, HSV, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); geotiffwrite(otpath_xyz, xyz, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); geotiffwrite(otpath_lab, lab, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); geotiffwrite(otpath_ycbcr, ycbcr, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); geotiffwrite(otpath_ntsc, ntsc, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); geotiffwrite(otpath_exg, ExG, geo, 'CoordRefSysCode', ['EPSG:', epsg], 'TiffType', 'bigtiff'); end disp(['[', num2str(ii), '/', num2str(numel(files)), ']~', files(ii).name]); end fprintf('Time Begin: '); fprintf(str1); fprintf('\n'); cur_data=date; cur_time=fix(clock); str2=sprintf('%s %.2d:%.2d:%.2d', cur_data, cur_time(4), cur_time(5), cur_time(6)); fprintf('Time End: '); disp(str2); timespan=toc(timebegin); fprintf('Time Span: %.4f s\n', timespan); disp('***********************************************'); function output=GenNormalise(input) output=input*0; for ii=1:size(input, 3) image=input(:, :, ii); % red_normalized = (red - red.min()) / (red.max() - red.min()) * 255 aa=image(:); image_normalized=(image-min(aa))./(max(aa)-min(aa))*255.0; output(:, :, ii)=image_normalized; end end

Matlab: Decision Tree Tool for Image

This is a simple tool of decison tree for the image analysis. It can export the compared image and the ruleset file at once. File.

GDAL: Instructions of function 'gdal_rasterize'

Files

Canopeo: Green and Non-Green

I am using Canopeo here to calculate the percentage of green and non-green (RGB) in photos, and also to extract the green parts of the photos. Files.

[1] Canopeo.

[2] Andres Patrignani, Tyson E. Ochsner. 2015. Canopeo: A Powerful New Tool for Measuring Fractional Green Canopy Cover. Agronomy Journal, 107(6): 2312~2320.

How to Convert LiDAR LAZ to LAS files and use in ArcMap

laszip.exe

Py+eCognition: Set up and tips

1. Set up


2. Hexagon_esgmentation_python_cp.dcp
3. segment-anything.dcp
4. How to download the latest version of eCognition? Click this link.
5. How to install eCognition on Windows? Click this link for License Manager, click this link for Developer
6. If you do not have a valid license, please request a trial version. Trial software access is not limited to a specific time period, but export and save functions, and the workspace environment are restricted. Rulesets saved in trial software cannot be opened in a fully-licensed version of eCognition software.
7. From the Ground Up videos

ML: A simple SAM program from input image to output mask, saving as .tif format

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# https://github.com/facebookresearch/segment-anything/issues/221 import cv2, os import matplotlib.pyplot as plt sam_checkpoint='D:/PyTest/kkk/sam_vit_l_0b3195.pth' model_type="vit_l" from segment_anything import SamAutomaticMaskGenerator, sam_model_registry sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) inpath='D:/PyTest/kkk/dog.jpg' img_arr=cv2.imread(inpath) img_arr=cv2.cvtColor(img_arr,cv2.COLOR_BGR2RGB) mask_generator=SamAutomaticMaskGenerator(sam) # mask_generator = SamAutomaticMaskGenerator( # model=sam, # points_per_side=32, # pred_iou_thresh=0.86, # stability_score_thresh=0.92, # crop_n_layers=1, # crop_n_points_downscale_factor=2, # # # min_mask_region_area=100, # Requires open-cv to run post-processing # ) predictor=mask_generator.generate(img_arr) # Choose the first mask # mask=predictor[0]['segmentation'] # # Remove background by turn it to white # img_arr[mask==False]=[255, 255, 255] newimg = img_arr[:, :, 0] * 0 for ii in range(len(predictor)): # print(ii) mask=predictor[ii]['segmentation'] # newimg = img_arr[:, :, 0] * 0 newimg[mask == True] = ii+1 # filename = os.path.join('D:/PyTest/kkk/export',str(ii+1)+'.tif') # cv2.imwrite(filename, newimg) # plt.imshow(img_arr) # plt.axis('off') # plt.show() filename='D:/PyTest/kkk/dog_new.tif' cv2.imwrite(filename, newimg)

ML: SAM changed the background to white or other colors

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# https://github.com/facebookresearch/segment-anything/issues/221 import cv2 import matplotlib.pyplot as plt sam_checkpoint='D:/PyTest/kkk/sam_vit_l_0b3195.pth' model_type="vit_l" from segment_anything import SamAutomaticMaskGenerator, sam_model_registry sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) inpath='D:/PyTest/kkk/girl.png' img_arr=cv2.imread(inpath) img_arr=cv2.cvtColor(img_arr,cv2.COLOR_BGR2RGB) mask_generator=SamAutomaticMaskGenerator(sam) predictor=mask_generator.generate(img_arr) # Choose the first mask mask=predictor[0]['segmentation'] # Remove background by turn it to white img_arr[mask==False]=[255, 255, 255] # plt.imshow(img_arr) # plt.axis('off') # plt.show() filename='D:/PyTest/kkk/girl_new.png' img_arr=cv2.cvtColor(img_arr, cv2.COLOR_BGR2RGB) cv2.imwrite(filename, img_arr)

ML: Automatically generating object masks with SAM

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# Automatically generating object masks with SAM # https://github.com/facebookresearch/segment-anything/blob/main/notebooks/automatic_mask_generator_example.ipynb import numpy as np import torch import matplotlib.pyplot as plt import cv2 def show_anns(anns): if len(anns)==0: return sorted_anns=sorted(anns, key=(lambda x: x['area']), reverse=True) ax=plt.gca() ax.set_autoscale_on(False) img=np.ones((sorted_anns[0]['segmentation'].shape[0], sorted_anns[0]['segmentation'].shape[1], 4)) img[:, :, 3]=0 for ann in sorted_anns: m=ann['segmentation'] color_mask=np.concatenate([np.random.random(3), [0.35]]) img[m]=color_mask ax.imshow(img) image=cv2.imread('D:/PyTest/kkk/dog.jpg') image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB) # plt.figure(figsize=(20,20)) # plt.imshow(image) # plt.axis('off') # plt.show() # plt.axis('off') import sys sys.path.append("..") from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor sam_checkpoint='D:/PyTest/kkk/sam_vit_h_4b8939.pth' model_type="vit_h" # device = "cuda" sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) # sam.to(device=device) mask_generator = SamAutomaticMaskGenerator(sam) masks=mask_generator.generate(image) print(len(masks)) # print(masks[0].keys()) # plt.figure(figsize=(20, 20)) # plt.imshow(image) # show_anns(masks) # plt.axis('off') # plt.show() mask_generator_2 = SamAutomaticMaskGenerator( model=sam, points_per_side=32, pred_iou_thresh=0.86, stability_score_thresh=0.92, crop_n_layers=1, crop_n_points_downscale_factor=2, min_mask_region_area=100, ) mask2=mask_generator_2.generate(image) print(len(mask2)) # plt.figure(figsize=(20, 20)) # plt.imshow(image) # show_anns(mask2) # plt.axis('off') # plt.show()

ML: Deep Learning for Image Segmentation with TensorFlow

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# https://www.analyticsvidhya.com/blog/2023/04/deep-learning-for-image-segmentation-with-tensorflow/# import cv2 import os import numpy as np import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt import matplotlib as mpl from tqdm import tqdm from sklearn.model_selection import train_test_split # a list to collect paths of 1000 images image_path=[] for root, dirs, files in os.walk('D:/PyTest/kkk/png_images'): # iterate over 1000 images for file in files: path=os.path.join(root, file) image_path.append(path) print(len(image_path)) # a list to collect paths of 1000 masks mask_path=[] for root, dirs, files in os.walk('D:/PyTest/kkk/png_masks'): # iterate over 1000 masks for file in files: # obtain the path path=os.path.join(root, file) # add path to the list mask_path.append(path) print(len(mask_path)) # Create a list to store images images=[] for path in tqdm(image_path): # read file file=tf.io.read_file(path) # decode png file into a tensor image=tf.image.decode_png(file, channels=3, dtype=tf.uint8) # append to the list images.append(image) # Create a list to store masks masks=[] for path in tqdm(mask_path): file=tf.io.read_file(path) # decode png file into a tensor mask=tf.image.decode_png(file, channels=1, dtype=tf.uint8) masks.append(mask) def resize_image(image): image=tf.cast(image, tf.float32) image=image/255.0 # resize image image=tf.image.resize(image, (128, 128)) return image def resize_mask(mask): mask=tf.image.resize(mask, (128, 128)) mask=tf.cast(mask, tf.uint8) return mask X=[resize_image(i) for i in images] y=[resize_mask(m) for m in masks] print(len(X)) print(len(y)) # split data into 80/20 ratio train_X, val_X, train_y, val_y=train_test_split(X, y, test_size=0.2, random_state=0) # Develop tf Dataset objects train_X=tf.data.Dataset.from_tensor_slices(train_X) val_X=tf.data.Dataset.from_tensor_slices(val_X) train_y=tf.data.Dataset.from_tensor_slices(train_y) val_y=tf.data.Dataset.from_tensor_slices(val_y) # verify the shapes and data types train_X.element_spec, train_y.element_spec, val_X.element_spec, val_y.element_spec # adjust brightness of image # don't alter in mask def brightness(img, mask): img=tf.image.adjust_brightness(img, 0.1) return img, mask def gamma(img, mask): img=tf.image.adjust_gamma(img, 0.1) return img, mask def hue(img, mask): img=tf.image.adjust_hue(img, -0.1) return img, mask def crop(img, mask): img=tf.image.central_crop(img, 0.7) img=tf.image.resize(img, (128,128)) mask=tf.image.central_crop(mask, 0.7) mask=tf.image.resize(mask, (128,128)) # cast to integers as they are class numbers mask=tf.cast(mask, tf.uint8) return img, mask def flip_hori(img, mask): img=tf.image.flip_left_right(img) mask=tf.image.flip_up_down(mask) return img, mask def flip_vert(img, mask): img=tf.image.flip_up_down(img) mask=tf.image.flip_up_down(mask) return img, mask # rotate both image and mask identically def rotate(img, mask): img=tf.image.rot90(img) mask=tf.image.rot90(mask) return img,mask # zip images and masks train=tf.data.Dataset.zip((train_X, train_y)) val=tf.data.Dataset.zip((val_X, val_y)) # perform augmentation on train data only a=train.map(brightness) b=train.map(gamma) c=train.map(hue) d=train.map(crop) e=train.map(flip_hori) f=train.map(flip_vert) g=train.map(rotate) # concatenate every new augmented sets train=train.concatenate(a) train=train.concatenate(b) train=train.concatenate(c) train=train.concatenate(d) train=train.concatenate(e) train=train.concatenate(f) # Setting the batch size BATCH=64 AT=tf.data.AUTOTUNE # Buffer size BUFFER=1000 STEPS_PER_EPOCH=800//BATCH VALIDATION_STEPS=200//BATCH train=train.cache().shuffle(BUFFER).batch(BATCH).repeat() train=train.prefetch(buffer_size=AT) val=val.batch(BATCH) # Use pre-trained DenseNet21 without head base=keras.applications.DenseNet121(input_shape=[128, 128, 3], include_top=False, weights='imagenet') skip_names=[ 'conv1_relu', 'pool2_relu', 'pool3_relu', 'pool4_relu', 'relu' ] skip_outputs=[base.get_layer(name).output for name in skip_names] # Building the downstack with the above layers. # We use the pre-trained model as much, without any fine-tuning downstack=keras.Model(inputs=base.input, outputs=skip_outputs) # freeze the downstack layers downstack.trainable=False from tensorflow_examples.models.pix2pix import pix2pix upstack=[ pix2pix.upsample(512, 3), pix2pix.upsample(256, 3), pix2pix.upsample(128, 3), pix2pix.upsample(64, 3) ] # define the input layer inputs=keras.layers.Input(shape=[128, 128, 3]) # downsample down=downstack(inputs) out=down[-1] # prepare skip connection skips=reversed(down[:-1]) # upsample with skip-connections for up,skip in zip(upstack, skips): out=up(out) out=keras.layers.Concatenate()([out,skip]) # define the final transpose conv layer out=keras.layers.Conv2DTranspose( 59, 3, strides=2, padding='same', )(out) unet=keras.Model(inputs=inputs, outputs=out) def Compile_Model(): unet.compile(loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), optimizer=keras.optimizers.RMSprop(learning_rate=0.001), metrics=['accuracy']) Compile_Model() # training and fine-tuning hist_1=unet.fit( train, validation_data=val, steps_per_epoch=STEPS_PER_EPOCH, validation_steps=VALIDATION_STEPS, epochs=20, verbose=2 ) # select a validation data batch img, mask=next(iter(val)) # make prediction pred=unet.predict(img) plt.figure(figsize=(20,28)) NORM = mpl.colors.Normalize(vmin=0, vmax=58) k=0 for i in pred: plt.subplot(4, 3, 1+k*3) i=tf.argmax(i, axis=-1) plt.imshow(i, cmap='jet', norm=NORM) plt.axis('off') plt.title('Prediction') # plot the ground truth mask plt.subplot(4, 3, 2+k*3) plt.imshow(mask[k], cmap='jet', norm=NORM) plt.axis('off') plt.title('Ground Truth') # plot the actual image plt.subplot(4, 3, 3+k*3) plt.imshow(img[k]) plt.axis('off') plt.title('Actual Image') k=k+1 if k==4: break plt.suptitle('Prediction After 20 Epochs (No Fine-tuning)', color='red', size=20) # plt.show() downstack.trainable=True # compile again Compile_Model() # train from epoch 20 to 40 hist_2=unet.fit( train, validation_data=val, steps_per_epoch=STEPS_PER_EPOCH, epochs=40, initial_epoch=20, verbose=2 ) # select a validation data batch img, mask=next(iter(val)) # make prediction pred=unet.predict(img) plt.figure(figsize=(20, 30)) k=0 for i in pred: plt.subplot(4, 3, 1+k*3) i=tf.argmax(i, axis=-1) plt.imshow(i, cmap='jet', norm=NORM) plt.axis('off') plt.title('Prediction') plt.subplot(4, 3, 2+k*3) plt.imshow(mask[k], cmap='jet', norm=NORM) plt.axis('off') plt.title('Ground Truth') plt.subplot(4, 3, 3+k*3) plt.imshow(img[k]) plt.axis('off') plt.title('Actual Image') k=k+1 if k==4: break plt.suptitle('Predition After 40 Epochs (By Fine-tuning from 21th Epoch)', color='red', size=20) plt.show()

ML: SVM vs. Random Forest for image segmentation

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# https://github.com/bnsreenu/python_for_microscopists/blob/master/068b-ML_06_04_TRAIN_ML_segmentation_All_filters_RForest_SVM.py import numpy as np import cv2 import pandas as pd img=cv2.imread("D://Mask//IMG_0686.JPG") img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) img2=img.reshape(-1) df=pd.DataFrame() df['Original Image']=img2 # Generate Gabor features # To count numbers up in order to give Gabor features a label in the data frame num=1 kernels=[] for theta in range(2): # Define number of thetas theta=theta/4.*np.pi for sigma in (1, 3): for lamda in np.arange(0, np.pi, np.pi/4): for gamma in (0.05, 0.5): gabor_label='Gabor'+str(num) ksize=9 kernel=cv2.getGaborKernel((ksize, ksize),sigma,theta,lamda,gamma, 0, ktype=cv2.CV_32F) kernels.append(kernel) fimg=cv2.filter2D(img2, cv2.CV_8UC3, kernel) filtered_img=fimg.reshape(-1) df[gabor_label]=filtered_img print(gabor_label, ': theta=', theta, ': sigma, ', sigma, ': lamda=', lamda, ':gamma=', gamma) num=num+1 # Canny Edge edges=cv2.Canny(img, 100, 200) # Image, min and max values edges1=edges.reshape(-1) df['Canny Edge']=edges1 from skimage.filters import roberts, sobel, scharr, prewitt # Robert Edge edge_roberts=roberts(img) edge_roberts1=edge_roberts.reshape(-1) df['Roberts']=edge_roberts1 # Sobel edge_sobel=sobel(img) edge_sobel1=edge_sobel.reshape(-1) df['Sobel']=edge_sobel1 # Scharr edge_scharr=scharr(img) edge_scharr1=edge_scharr.reshape(-1) df['Scharr']=edge_scharr1 # Prewitt edge_prewitt=prewitt(img) edge_prewitt1=edge_prewitt.reshape(-1) df['Prewitt']=edge_prewitt1 # Gaussian with sigma=3 from scipy import ndimage as nd gaussian_img=nd.gaussian_filter(img, sigma=3) gaussian_img1=gaussian_img.reshape(-1) df['Gaussian s3']=gaussian_img1 # Gaussian with sigma=7 gaussian_img2=nd.gaussian_filter(img, sigma=7) gaussian_img3=gaussian_img2.reshape(-1) df['Gaussian s7']=gaussian_img3 # Median with sigma=3 median_img=nd.median_filter(img, size=3) median_img1=median_img.reshape(-1) df['Median s3']=median_img1 # Variance with size=3 variance_img=nd.generic_filter(img, np.var, size=3) variance_img1=variance_img.reshape(-1) df['Variance s3']=variance_img1 # Now, add a column in the data frame for the labels # For this, we need to import the labeled image labeled_img=cv2.imread("D://Mask//mask.tif") # Remember that you can load an image with partial labels # But, drop the rows with unlabeled data labeled_img=cv2.cvtColor(labeled_img, cv2.COLOR_BGR2GRAY) labeled_img1=labeled_img.reshape(-1) df['Labels']=labeled_img1 print(df.head()) # Define the dependent variable that needs to be predicted (labels) Y=df["Labels"].values # Define the independent variables X=df.drop(labels=["Labels"], axis=1) # Split data into train and test to verify accuracy after fitting the model from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.4, random_state=20) # Import the model we are using # RandomForestRegressor is for regression type of problems # For classification we use RandomForestClassifier # Both yield similar results except for regressor the result is float # and for classifier it is an integer from sklearn.ensemble import RandomForestClassifier model=RandomForestClassifier(n_estimators=100, random_state=42) # Train the model on training data model.fit(X_train, y_train) # Testing the model by predicting on test data # and Calculate the accuracy score # First test predication on the training data itself. Should be good prediction_test_train=model.predict(X_train) # Test prediction on testing data prediction_test=model.predict(X_test) # Let us check the accuracy on test data from sklearn import metrics # First check the accuracy on training data. This will be higher than test data predication accuracy print("Accuracy on training data= ", metrics.accuracy_score(y_train, prediction_test_train)) print("Accuracy= ", metrics.accuracy_score(y_test, prediction_test))