From 070d0b66924a62f3f8c6698fb6afdd54ec45f8ee Mon Sep 17 00:00:00 2001 From: Moritz Marquardt Date: Mon, 16 Oct 2023 11:12:14 +0200 Subject: [PATCH 01/33] EX: new kernel based concentration analysis example. --- examples/kernel_interpolation_backup.py | 229 ++++++++++++++++++++++++ 1 file changed, 229 insertions(+) create mode 100644 examples/kernel_interpolation_backup.py diff --git a/examples/kernel_interpolation_backup.py b/examples/kernel_interpolation_backup.py new file mode 100644 index 00000000..54542b3d --- /dev/null +++ b/examples/kernel_interpolation_backup.py @@ -0,0 +1,229 @@ +import numpy as np +import darsia +import skimage +import matplotlib.pyplot as plt +from pathlib import Path +import matplotlib.patches as patches +import string + +# import matplotlib.patches as patches + +folder = Path("images") +baseline_path = folder / Path("20220914-142404.TIF") +image_path = folder / Path("20220914-151727.TIF") +# image_path = Path(".data/test/singletracer.JPG") + +# Setup curvature correction (here only cropping) +curvature_correction = darsia.CurvatureCorrection( + config={ + "crop": { + # Define the pixel values (x,y) of the corners of the ROI. + # Start at top left corner and then continue counterclockwise. + "pts_src": [[300, 600], [300, 4300], [7600, 4300], [7600, 600]], + # Specify the true dimensions of the reference points + "width": 0.92, + "height": 0.5, + } + } +) +transformations = [curvature_correction] + +# Read-in images +baseline = darsia.imread(baseline_path, transformations=transformations).subregion( + voxels=(slice(2300, 2500), slice(2200, 5200)) +) +image = darsia.imread(image_path, transformations=transformations).subregion( + voxels=(slice(2300, 2500), slice(2200, 5200)) +) + + +# baseline_full = darsia.imread(baseline_path, transformations=transformations) +# image_full = darsia.imread(image_path, transformations=transformations) + + +# LAB +diff_LAB = ( + (skimage.color.rgb2lab(image.img) - skimage.color.rgb2lab(baseline.img)) + + [0, 128, 128] +) / [100, 255, 255] + +# RGB +diff_RGB = skimage.img_as_float(image.img) - skimage.img_as_float(baseline.img) + +# HSV +# diff = skimage.color.rgb2hsv(baseline.img) - skimage.color.rgb2hsv(image.img) + +# Regularize +smooth_RGB = skimage.restoration.denoise_tv_bregman( + diff_RGB, weight=0.025, eps=1e-4, max_num_iter=100, isotropic=True +) +smooth_LAB = skimage.restoration.denoise_tv_bregman( + diff_LAB, weight=0.025, eps=1e-4, max_num_iter=100, isotropic=True +) + +samples = [ + (slice(50, 150), slice(100, 200)), + (slice(50, 150), slice(400, 500)), + (slice(50, 150), slice(600, 700)), + (slice(50, 150), slice(800, 900)), + (slice(50, 150), slice(1000, 1100)), + (slice(50, 150), slice(1200, 1300)), + (slice(50, 150), slice(1400, 1500)), + (slice(50, 150), slice(1600, 1700)), + (slice(50, 150), slice(2700, 2800)), +] + +letters = list(string.ascii_uppercase) + + +def extract_support_points(signal, samples): + # visualise patches + fig, ax = plt.subplots() + ax.imshow(np.abs(signal)) # visualise abs colours, because relative cols are neg + ax.set_xlabel("horizontal pixel") + ax.set_ylabel("vertical pixel") + + # double check number of patches + n = np.shape(samples)[0] # number of patches + print("number of support patches: " + str(n)) + + # init colour vector + colours = np.zeros((n, 3)) + # enumerate through all patches + for i, p in enumerate(samples): + # visualise patches on image + rect = patches.Rectangle( + (p[1].start, p[0].start), + p[1].stop - p[1].start, + p[0].stop - p[0].start, + linewidth=1, + edgecolor="w", + facecolor="none", + ) + # ax.text( + # p[1].start + 130, p[0].start + 100, letters[i], fontsize=15, color="white" + # ) + ax.text(p[1].start + 5, p[0].start + 95, letters[i], fontsize=12, color="white") + ax.add_patch(rect) + + # histo analysis + patch = signal[p] + flat_image = np.reshape(patch, (10000, 3)) # all pixels in one dimension + # patch visualisation + # plt.figure("patch" + letters[i]) + # plt.imshow(np.abs(patch)) + H, edges = np.histogramdd( + flat_image, bins=100, range=[(-1, 1), (-1, 1), (-1, 1)] + ) + print(H.shape) + index = np.unravel_index(H.argmax(), H.shape) + col = [ + (edges[0][index[0]] + edges[0][index[0] + 1]) / 2, + (edges[1][index[1]] + edges[1][index[1] + 1]) / 2, + (edges[2][index[2]] + edges[2][index[2] + 1]) / 2, + ] + colours[i] = col + + c = np.abs(colours) + plt.figure() + ax = plt.axes(projection="3d") + ax.set_xlabel("R*") + ax.set_ylabel("G*") + ax.set_zlabel("B*") + ax.scatter(colours[:, 0], colours[:, 1], colours[:, 2], c=c) + for i, c in enumerate(colours): + ax.text(c[0], c[1], c[2], letters[i]) + + print("characteristic colours: " + str(colours)) + return n, colours + + +n, colours_RGB = extract_support_points(signal=smooth_RGB, samples=samples) +n, colours_LAB = extract_support_points(signal=smooth_LAB, samples=samples) +concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) + + +def color_to_concentration( + k, colours, concentrations, signal: np.ndarray +) -> np.ndarray: + # signal is rgb, transofrm to lab space because it is uniform and therefore + # makes sense to interpolate in + # signal = skimage.color.rgb2lab(signal) + # colours = skimage.color.rgb2lab(colours) + + x = np.array(colours) # data points / control points / support points + y = np.array(concentrations) # goal points + X = np.ones((x.shape[0], x.shape[0])) # kernel matrix + for i in range(x.shape[0]): + for j in range(x.shape[0]): + X[i, j] = k(x[i], x[j]) + + alpha = np.linalg.solve(X, y) + + # Estimator / interpolant + def estim(signal): + sum = 0 + for n in range(alpha.shape[0]): + sum += alpha[n] * k(signal, x[n]) + return sum + + # estim = scipy.interpolate.LinearNDInterpolator(colours, concentrations, 0) + + ph_image = np.zeros(signal.shape[:2]) + for i in range(signal.shape[0]): + for j in range(signal.shape[1]): + ph_image[i, j] = estim(signal[i, j]) + return ph_image + + +# define linear kernel shifted to avoid singularities +def k_lin(x, y, a=0): + return np.inner(x, y) + a + + +# define gaussian kernel +def k_gauss(x, y, gamma=9.73): # rgb: 9.73 , lab: 24.22 + return np.exp(-gamma * np.inner(x - y, x - y)) + + +# Convert a discrete ph stripe to a numeric pH indicator. +ph_image = color_to_concentration( + k_gauss, colours_RGB, concentrations, smooth_RGB +) # gamma=10 value retrieved from ph analysis kernel calibration war bester punk für c=0.95 +# was +# physikalisch am meisten sinn ergibt + +plt.figure("cut ph val") +plt.plot(np.average(ph_image, axis=0)) +plt.xlabel("horizontal pixel") +plt.ylabel("concentration") + +ph_image[ph_image > 1] = 1 # für visualisierung von größer 1 values +ph_image[ph_image < 0] = 0 +fig = plt.figure() +fig.suptitle("evolution of signal processing in a subregion") +ax = plt.subplot(212) +ax.imshow(ph_image) +# calibration patch for the kernel calibration visualization if needed +# rect = patches.Rectangle( +# (1000, 80), +# 40, +# 40, +# linewidth=1, +# edgecolor="black", +# facecolor="none", +# ) +# ax.add_patch(rect) +ax.set_ylabel("vertical pixel") +ax.set_xlabel("horizontal pixel") + +ax = plt.subplot(211) +ax.imshow(skimage.img_as_ubyte(image.img)) +ax.set_ylabel("vertical pixel") +ax.set_xlabel("horizontal pixel") + +# plt.figure("indicator") +# indicator = np.arange(101) / 100 +# plt.axis("off") +# plt.imshow([indicator, indicator, indicator, indicator, indicator]) +plt.show() From 219da61fcd83d6df55e66030a28b428a3e638d43 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 11:21:47 +0200 Subject: [PATCH 02/33] MAINT: Rename file. --- .../{kernel_interpolation_backup.py => kernel_interpolation.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename examples/{kernel_interpolation_backup.py => kernel_interpolation.py} (100%) diff --git a/examples/kernel_interpolation_backup.py b/examples/kernel_interpolation.py similarity index 100% rename from examples/kernel_interpolation_backup.py rename to examples/kernel_interpolation.py From e2b91266a70b178d13774fd3a450339b6a225935 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 11:31:08 +0200 Subject: [PATCH 03/33] DOC/MAINT: Add comments and fix style --- examples/kernel_interpolation.py | 63 +++++++++++++++++++------------- 1 file changed, 38 insertions(+), 25 deletions(-) diff --git a/examples/kernel_interpolation.py b/examples/kernel_interpolation.py index 54542b3d..ebda1eb1 100644 --- a/examples/kernel_interpolation.py +++ b/examples/kernel_interpolation.py @@ -1,17 +1,18 @@ -import numpy as np -import darsia -import skimage -import matplotlib.pyplot as plt +import string from pathlib import Path + import matplotlib.patches as patches -import string +import matplotlib.pyplot as plt +import numpy as np +import skimage -# import matplotlib.patches as patches +import darsia + +# ! ---- DATA MANAGEMENT ---- ! folder = Path("images") baseline_path = folder / Path("20220914-142404.TIF") image_path = folder / Path("20220914-151727.TIF") -# image_path = Path(".data/test/singletracer.JPG") # Setup curvature correction (here only cropping) curvature_correction = darsia.CurvatureCorrection( @@ -36,10 +37,7 @@ voxels=(slice(2300, 2500), slice(2200, 5200)) ) - -# baseline_full = darsia.imread(baseline_path, transformations=transformations) -# image_full = darsia.imread(image_path, transformations=transformations) - +# ! ---- COLOR MANAGEMENT ---- ! # LAB diff_LAB = ( @@ -50,9 +48,6 @@ # RGB diff_RGB = skimage.img_as_float(image.img) - skimage.img_as_float(baseline.img) -# HSV -# diff = skimage.color.rgb2hsv(baseline.img) - skimage.color.rgb2hsv(image.img) - # Regularize smooth_RGB = skimage.restoration.denoise_tv_bregman( diff_RGB, weight=0.025, eps=1e-4, max_num_iter=100, isotropic=True @@ -61,6 +56,9 @@ diff_LAB, weight=0.025, eps=1e-4, max_num_iter=100, isotropic=True ) +# ! ---- CALIBRATION ---- ! + +# NOTE: Samples can also be defined using a graphical assistant. samples = [ (slice(50, 150), slice(100, 200)), (slice(50, 150), slice(400, 500)), @@ -73,10 +71,13 @@ (slice(50, 150), slice(2700, 2800)), ] -letters = list(string.ascii_uppercase) - def extract_support_points(signal, samples): + """Assistant to extract representative colors from input image for given patches.""" + + # Alphabet useful for labeling in plots + letters = list(string.ascii_uppercase) + # visualise patches fig, ax = plt.subplots() ax.imshow(np.abs(signal)) # visualise abs colours, because relative cols are neg @@ -100,9 +101,6 @@ def extract_support_points(signal, samples): edgecolor="w", facecolor="none", ) - # ax.text( - # p[1].start + 130, p[0].start + 100, letters[i], fontsize=15, color="white" - # ) ax.text(p[1].start + 5, p[0].start + 95, letters[i], fontsize=12, color="white") ax.add_patch(rect) @@ -115,7 +113,6 @@ def extract_support_points(signal, samples): H, edges = np.histogramdd( flat_image, bins=100, range=[(-1, 1), (-1, 1), (-1, 1)] ) - print(H.shape) index = np.unravel_index(H.argmax(), H.shape) col = [ (edges[0][index[0]] + edges[0][index[0] + 1]) / 2, @@ -142,10 +139,22 @@ def extract_support_points(signal, samples): n, colours_LAB = extract_support_points(signal=smooth_LAB, samples=samples) concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) +# ! ---- MAIN CONCENTRATION ANALYSIS ---- ! + def color_to_concentration( k, colours, concentrations, signal: np.ndarray ) -> np.ndarray: + """Main method. + + Two operations: + 1. Setup of kernel-based interpolation. + 2. Translation of signal (denoised difference) to concentration. + + """ + + # ! ---- Initialization of kernel based interpolation + # signal is rgb, transofrm to lab space because it is uniform and therefore # makes sense to interpolate in # signal = skimage.color.rgb2lab(signal) @@ -160,6 +169,8 @@ def color_to_concentration( alpha = np.linalg.solve(X, y) + # ! ---- Application of kernel based interpolation + # Estimator / interpolant def estim(signal): sum = 0 @@ -176,6 +187,8 @@ def estim(signal): return ph_image +# ! ---- KERNEL MANAGEMENET ----- + # define linear kernel shifted to avoid singularities def k_lin(x, y, a=0): return np.inner(x, y) + a @@ -186,12 +199,12 @@ def k_gauss(x, y, gamma=9.73): # rgb: 9.73 , lab: 24.22 return np.exp(-gamma * np.inner(x - y, x - y)) +# ! ---- MAIN ROUTINE ---- ! + # Convert a discrete ph stripe to a numeric pH indicator. -ph_image = color_to_concentration( - k_gauss, colours_RGB, concentrations, smooth_RGB -) # gamma=10 value retrieved from ph analysis kernel calibration war bester punk für c=0.95 -# was -# physikalisch am meisten sinn ergibt +ph_image = color_to_concentration(k_gauss, colours_RGB, concentrations, smooth_RGB) +# gamma=10 value retrieved from ph analysis kernel calibration was best for c = 0.95 +# which also is physically meaningful plt.figure("cut ph val") plt.plot(np.average(ph_image, axis=0)) From 18cf3fffe88f5395abe22be8861325ae7240dca0 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 11:46:54 +0200 Subject: [PATCH 04/33] MAINT: add some class structure --- examples/kernel_interpolation.py | 92 ++++++++++++++++++-------------- 1 file changed, 53 insertions(+), 39 deletions(-) diff --git a/examples/kernel_interpolation.py b/examples/kernel_interpolation.py index ebda1eb1..50adbcf6 100644 --- a/examples/kernel_interpolation.py +++ b/examples/kernel_interpolation.py @@ -1,4 +1,5 @@ import string +from abc import ABC from pathlib import Path import matplotlib.patches as patches @@ -39,6 +40,9 @@ # ! ---- COLOR MANAGEMENT ---- ! +# NOTE: The steps of taking differences, and apply denoising are part of +# darsia.ConcentrationAnalysis. + # LAB diff_LAB = ( (skimage.color.rgb2lab(image.img) - skimage.color.rgb2lab(baseline.img)) @@ -142,67 +146,77 @@ def extract_support_points(signal, samples): # ! ---- MAIN CONCENTRATION ANALYSIS ---- ! -def color_to_concentration( - k, colours, concentrations, signal: np.ndarray -) -> np.ndarray: - """Main method. +class KernelInterpolation: + def __init__(self, k, colours, concentrations): - Two operations: - 1. Setup of kernel-based interpolation. - 2. Translation of signal (denoised difference) to concentration. + self.kernel = k - """ + # signal is rgb, transofrm to lab space because it is uniform and therefore + # makes sense to interpolate in + # signal = skimage.color.rgb2lab(signal) + # colours = skimage.color.rgb2lab(colours) - # ! ---- Initialization of kernel based interpolation + x = np.array(colours) # data points / control points / support points + y = np.array(concentrations) # goal points + X = np.ones((x.shape[0], x.shape[0])) # kernel matrix + for i in range(x.shape[0]): + for j in range(x.shape[0]): + X[i, j] = self.kernel(x[i], x[j]) - # signal is rgb, transofrm to lab space because it is uniform and therefore - # makes sense to interpolate in - # signal = skimage.color.rgb2lab(signal) - # colours = skimage.color.rgb2lab(colours) + alpha = np.linalg.solve(X, y) - x = np.array(colours) # data points / control points / support points - y = np.array(concentrations) # goal points - X = np.ones((x.shape[0], x.shape[0])) # kernel matrix - for i in range(x.shape[0]): - for j in range(x.shape[0]): - X[i, j] = k(x[i], x[j]) + # Cache + self.x = x + self.alpha = alpha - alpha = np.linalg.solve(X, y) + def __call__(self, signal: np.ndarray): + # Estimator / interpolant + def estim(signal): + sum = 0 + for n in range(self.alpha.shape[0]): + sum += self.alpha[n] * self.kernel(signal, self.x[n]) + return sum - # ! ---- Application of kernel based interpolation + ph_image = np.zeros(signal.shape[:2]) + for i in range(signal.shape[0]): + for j in range(signal.shape[1]): + ph_image[i, j] = estim(signal[i, j]) + return ph_image - # Estimator / interpolant - def estim(signal): - sum = 0 - for n in range(alpha.shape[0]): - sum += alpha[n] * k(signal, x[n]) - return sum - # estim = scipy.interpolate.LinearNDInterpolator(colours, concentrations, 0) +# ! ---- KERNEL MANAGEMENET ----- - ph_image = np.zeros(signal.shape[:2]) - for i in range(signal.shape[0]): - for j in range(signal.shape[1]): - ph_image[i, j] = estim(signal[i, j]) - return ph_image +class BaseKernel(ABC): + def __call__(self, x, y): + pass -# ! ---- KERNEL MANAGEMENET ----- # define linear kernel shifted to avoid singularities -def k_lin(x, y, a=0): - return np.inner(x, y) + a +class LinearKernel(BaseKernel): + def __init__(self, a=0): + self.a = a + + def __call__(self, x, y): + return np.inner(x, y) + self.a # define gaussian kernel -def k_gauss(x, y, gamma=9.73): # rgb: 9.73 , lab: 24.22 - return np.exp(-gamma * np.inner(x - y, x - y)) +class GaussianKernel(BaseKernel): + def __init__(self, gamma=1): + self.gamma = gamma + + def __call__(self, x, y): + return np.exp(-self.gamma * np.inner(x - y, x - y)) # ! ---- MAIN ROUTINE ---- ! # Convert a discrete ph stripe to a numeric pH indicator. -ph_image = color_to_concentration(k_gauss, colours_RGB, concentrations, smooth_RGB) +color_to_concentration = KernelInterpolation( + GaussianKernel(gamma=9.73), colours_RGB, concentrations +) # rgb: 9.73 , lab: 24.22 +ph_image = color_to_concentration(smooth_RGB) # gamma=10 value retrieved from ph analysis kernel calibration was best for c = 0.95 # which also is physically meaningful From f0e5277b7598a1d4edad883c28f21ee3defcb095 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 13:43:33 +0200 Subject: [PATCH 05/33] MAINT: Accelerate interpolation. --- examples/kernel_interpolation.py | 32 +++++++++++++++----------------- 1 file changed, 15 insertions(+), 17 deletions(-) diff --git a/examples/kernel_interpolation.py b/examples/kernel_interpolation.py index 50adbcf6..5878d12d 100644 --- a/examples/kernel_interpolation.py +++ b/examples/kernel_interpolation.py @@ -147,20 +147,25 @@ def extract_support_points(signal, samples): class KernelInterpolation: - def __init__(self, k, colours, concentrations): + """General kernel-based interpolation.""" - self.kernel = k + def __init__(self, kernel, colours, concentrations): + + self.kernel = kernel # signal is rgb, transofrm to lab space because it is uniform and therefore # makes sense to interpolate in # signal = skimage.color.rgb2lab(signal) # colours = skimage.color.rgb2lab(colours) + num_data = colours.shape[0] + assert len(concentrations) == num_data, "Input data not compatible." + x = np.array(colours) # data points / control points / support points y = np.array(concentrations) # goal points - X = np.ones((x.shape[0], x.shape[0])) # kernel matrix - for i in range(x.shape[0]): - for j in range(x.shape[0]): + X = np.ones((num_data, num_data)) # kernel matrix + for i in range(num_data): + for j in range(num_data): X[i, j] = self.kernel(x[i], x[j]) alpha = np.linalg.solve(X, y) @@ -170,17 +175,10 @@ def __init__(self, k, colours, concentrations): self.alpha = alpha def __call__(self, signal: np.ndarray): - # Estimator / interpolant - def estim(signal): - sum = 0 - for n in range(self.alpha.shape[0]): - sum += self.alpha[n] * self.kernel(signal, self.x[n]) - return sum - + """Apply interpolation.""" ph_image = np.zeros(signal.shape[:2]) - for i in range(signal.shape[0]): - for j in range(signal.shape[1]): - ph_image[i, j] = estim(signal[i, j]) + for n in range(self.alpha.shape[0]): + ph_image += self.alpha[n] * self.kernel(signal, self.x[n]) return ph_image @@ -198,7 +196,7 @@ def __init__(self, a=0): self.a = a def __call__(self, x, y): - return np.inner(x, y) + self.a + return np.sum(np.multiply(x, y), axis=-1) + self.a # define gaussian kernel @@ -207,7 +205,7 @@ def __init__(self, gamma=1): self.gamma = gamma def __call__(self, x, y): - return np.exp(-self.gamma * np.inner(x - y, x - y)) + return np.exp(-self.gamma * np.sum(np.multiply(x - y, x - y), axis=-1)) # ! ---- MAIN ROUTINE ---- ! From 435a8ba92206be9c2542506ad32129b2f6268f14 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 14:38:47 +0200 Subject: [PATCH 06/33] ENH: Add functionlity to change data type with correct scaling --- src/darsia/image/image.py | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/src/darsia/image/image.py b/src/darsia/image/image.py index e3b5a5cf..840e9d27 100644 --- a/src/darsia/image/image.py +++ b/src/darsia/image/image.py @@ -421,6 +421,36 @@ def astype(self, data_type) -> Any: return copy_image + def img_as(self, data_type) -> Any: + """Change data type via skimage. + + Args: + data_type: target data type + + Returns: + Image: image with transformed data type + + """ + copy_image = self.copy() + if data_type in [bool]: + copy_image.img = skimage.img_as_bool(copy_image.img) + elif data_type in [float]: + copy_image.img = skimage.img_as_float(copy_image.img) + elif data_type in [np.float32]: + copy_image.img = skimage.img_as_float32(copy_image.img) + elif data_type in [np.float64]: + copy_image.img = skimage.img_as_float64(copy_image.img) + elif data_type in [int]: + copy_image.img = skimage.img_as_int(copy_image.img) + elif data_type in [np.uint8]: + copy_image.img = skimage.img_as_ubyte(copy_image.img) + elif data_type in [np.uint16]: + copy_image.img = skimage.img_as_uint(copy_image.img) + else: + raise NotImplementedError + + return copy_image + # ! ---- Extraction routines def metadata(self) -> dict: From 5bdd4993193f08e15d4d4d23f630ac8674b7c301 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 14:39:13 +0200 Subject: [PATCH 07/33] MAINT: Increase robustness. --- src/darsia/restoration/tvd.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/darsia/restoration/tvd.py b/src/darsia/restoration/tvd.py index 3bf517f7..20eb4037 100644 --- a/src/darsia/restoration/tvd.py +++ b/src/darsia/restoration/tvd.py @@ -28,7 +28,7 @@ def __init__(self, key: str = "", **kwargs) -> None: """ # Determine method - self.method = kwargs.pop(key + "method", "chambolle") + self.method = kwargs.pop(key + "method", "chambolle").lower() # Method-specific parameters if self.method == "heterogeneous bregman": From a2c3ffce99869eb5eea1ac63eb55968fc55ded0d Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 14:40:22 +0200 Subject: [PATCH 08/33] MAINT: Use no cleaning filter if only 1 baseilne image provided. --- src/darsia/multi_image_analysis/concentrationanalysis.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/darsia/multi_image_analysis/concentrationanalysis.py b/src/darsia/multi_image_analysis/concentrationanalysis.py index 220b1596..5dd79e15 100644 --- a/src/darsia/multi_image_analysis/concentrationanalysis.py +++ b/src/darsia/multi_image_analysis/concentrationanalysis.py @@ -151,6 +151,8 @@ def find_cleaning_filter( # Use internal baseline images, if available. baseline_images = self._base_collection[1:] + if len(baseline_images) == 0: + baseline_images = None self.threshold_cleaning_filter = None """Cleaning filter.""" From 17486756e4303ef5105aa5b789cd906e584a56f6 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 14:40:47 +0200 Subject: [PATCH 09/33] MAINT: Rename method - more general --- src/darsia/multi_image_analysis/concentrationanalysis.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/src/darsia/multi_image_analysis/concentrationanalysis.py b/src/darsia/multi_image_analysis/concentrationanalysis.py index 5dd79e15..09d0751d 100644 --- a/src/darsia/multi_image_analysis/concentrationanalysis.py +++ b/src/darsia/multi_image_analysis/concentrationanalysis.py @@ -173,7 +173,7 @@ def find_cleaning_filter( diff = self._subtract_background(probe_img) # Extract scalar version - monochromatic_diff = self._extract_scalar_information(diff) + monochromatic_diff = self._reduce_signal(diff) # Consider elementwise max self.threshold_cleaning_filter = np.maximum( @@ -232,7 +232,7 @@ def __call__(self, img: darsia.Image) -> darsia.Image: self._inspect_diff(diff) # Extract monochromatic version and take difference wrt the baseline image - signal = self._extract_scalar_information(diff) + signal = self._reduce_signal(diff) # Provide possibility for tuning and inspection of intermediate results self._inspect_scalar_signal(signal) @@ -332,12 +332,14 @@ def _subtract_background(self, img: darsia.Image) -> darsia.Image: diff = skimage.util.compare_images( img.img, self.base.img, method="diff" ) + elif self._diff_option == "plain": + diff = img.img - self.base.img else: raise ValueError(f"Diff option {self._diff_option} not supported") return diff - def _extract_scalar_information(self, img: np.ndarray) -> np.ndarray: + def _reduce_signal(self, img: np.ndarray) -> np.ndarray: """ Make a scalar image from potentially multi-colored image. From c6687b0add309e38385574b7ca9a3e45b2c1bf6f Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 14:41:23 +0200 Subject: [PATCH 10/33] ENH: Add new diff type in concentration analysis. --- src/darsia/multi_image_analysis/concentrationanalysis.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/darsia/multi_image_analysis/concentrationanalysis.py b/src/darsia/multi_image_analysis/concentrationanalysis.py index 09d0751d..6a45ea0f 100644 --- a/src/darsia/multi_image_analysis/concentrationanalysis.py +++ b/src/darsia/multi_image_analysis/concentrationanalysis.py @@ -320,6 +320,8 @@ def _subtract_background(self, img: darsia.Image) -> darsia.Image: diff = np.clip(-img.img, 0, None) elif self._diff_option == "absolute": diff = np.absolute(img.img) + elif self._diff_option == "plain": + diff = img.img else: raise ValueError(f"Diff option {self._diff_option} not supported") else: From ee353be42168ab99a398dcfdd6ec544d4dc8e464 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 14:41:45 +0200 Subject: [PATCH 11/33] MAINT: Simplify style --- src/darsia/multi_image_analysis/concentrationanalysis.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/darsia/multi_image_analysis/concentrationanalysis.py b/src/darsia/multi_image_analysis/concentrationanalysis.py index 6a45ea0f..0d718794 100644 --- a/src/darsia/multi_image_analysis/concentrationanalysis.py +++ b/src/darsia/multi_image_analysis/concentrationanalysis.py @@ -388,10 +388,7 @@ def _balance_signal(self, img: np.ndarray) -> np.ndarray: np.ndarray: balanced image """ - if self.balancing is not None: - return self.balancing(img) - else: - return img + return img if self.balancing is None else self.balancing(img) def _prepare_signal(self, signal: np.ndarray) -> np.ndarray: """ From 2d10dee390832c0cc57b2d11a29677ddb8941bfa Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 14:42:31 +0200 Subject: [PATCH 12/33] EX: Integrate concentration analysis into example --- examples/kernel_interpolation.py | 89 ++++++++++++++++++++++---------- 1 file changed, 63 insertions(+), 26 deletions(-) diff --git a/examples/kernel_interpolation.py b/examples/kernel_interpolation.py index 5878d12d..d66f6ab0 100644 --- a/examples/kernel_interpolation.py +++ b/examples/kernel_interpolation.py @@ -76,8 +76,19 @@ ] -def extract_support_points(signal, samples): - """Assistant to extract representative colors from input image for given patches.""" +def extract_characteristic_data(signal, samples, verbosity=False, show_plot=True): + """Assistant to extract representative colors from input image for given patches. + + Args: + signal (np.ndarray): input signal, assumed to have the structure of a 2d, + colored image. + samples (list of slices): list of 2d regions of interest + show_plot (boolean): flag controlling whether plots are displayed. + + Returns: + np.ndarray: characteristic colors. + + """ # Alphabet useful for labeling in plots letters = list(string.ascii_uppercase) @@ -90,7 +101,8 @@ def extract_support_points(signal, samples): # double check number of patches n = np.shape(samples)[0] # number of patches - print("number of support patches: " + str(n)) + if verbosity: + print("Number of support patches: " + str(n)) # init colour vector colours = np.zeros((n, 3)) @@ -125,22 +137,30 @@ def extract_support_points(signal, samples): ] colours[i] = col - c = np.abs(colours) - plt.figure() - ax = plt.axes(projection="3d") - ax.set_xlabel("R*") - ax.set_ylabel("G*") - ax.set_zlabel("B*") - ax.scatter(colours[:, 0], colours[:, 1], colours[:, 2], c=c) - for i, c in enumerate(colours): - ax.text(c[0], c[1], c[2], letters[i]) - - print("characteristic colours: " + str(colours)) - return n, colours - - -n, colours_RGB = extract_support_points(signal=smooth_RGB, samples=samples) -n, colours_LAB = extract_support_points(signal=smooth_LAB, samples=samples) + if verbosity: + c = np.abs(colours) + plt.figure() + ax = plt.axes(projection="3d") + ax.set_xlabel("R*") + ax.set_ylabel("G*") + ax.set_zlabel("B*") + ax.scatter(colours[:, 0], colours[:, 1], colours[:, 2], c=c) + for i, c in enumerate(colours): + ax.text(c[0], c[1], c[2], letters[i]) + if show_plot: + plt.show() + + print("Characteristic colours: " + str(colours)) + return colours + + +n = len(samples) +colours_RGB = extract_characteristic_data( + signal=smooth_RGB, samples=samples, verbosity=True, show_plot=False +) +colours_LAB = extract_characteristic_data( + signal=smooth_LAB, samples=samples, verbosity=True, show_plot=False +) concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) # ! ---- MAIN CONCENTRATION ANALYSIS ---- ! @@ -210,13 +230,30 @@ def __call__(self, x, y): # ! ---- MAIN ROUTINE ---- ! -# Convert a discrete ph stripe to a numeric pH indicator. -color_to_concentration = KernelInterpolation( - GaussianKernel(gamma=9.73), colours_RGB, concentrations -) # rgb: 9.73 , lab: 24.22 -ph_image = color_to_concentration(smooth_RGB) -# gamma=10 value retrieved from ph analysis kernel calibration was best for c = 0.95 -# which also is physically meaningful +if False: + # Reference solution + + # Convert a discrete ph stripe to a numeric pH indicator. + color_to_concentration = KernelInterpolation( + GaussianKernel(gamma=9.73), colours_RGB, concentrations + ) # rgb: 9.73 , lab: 24.22 + ph_image = color_to_concentration(smooth_RGB) + # gamma=10 value retrieved from ph analysis kernel calibration was best for c = 0.95 + # which also is physically meaningful + +elif True: + # Same but under the use of DarSIA + analysis = darsia.ConcentrationAnalysis( + base=baseline.img_as(float), + restoration=darsia.TVD( + weight=0.025, eps=1e-4, max_num_iter=100, method="isotropic Bregman" + ), + model=KernelInterpolation( + GaussianKernel(gamma=9.73), colours_RGB, concentrations + ), + **{"diff option": "plain"} + ) + ph_image = analysis(image.img_as(float)).img plt.figure("cut ph val") plt.plot(np.average(ph_image, axis=0)) From 3b9608369d774663c663317e49d0e6b383136613 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 15:15:32 +0200 Subject: [PATCH 13/33] ENH: Provide box selection assistant. --- src/darsia/__init__.py | 1 + .../assistants/box_selection_assistant.py | 61 +++++++++++++++++++ 2 files changed, 62 insertions(+) create mode 100644 src/darsia/assistants/box_selection_assistant.py diff --git a/src/darsia/__init__.py b/src/darsia/__init__.py index b7e4cdd2..a13b53fd 100644 --- a/src/darsia/__init__.py +++ b/src/darsia/__init__.py @@ -68,6 +68,7 @@ from darsia.manager.co2analysis import * from darsia.assistants.base_assistant import * from darsia.assistants.point_selection_assistant import * +from darsia.assistants.box_selection_assistant import * from darsia.assistants.rotation_correction_assistant import * from darsia.assistants.subregion_assistant import * from darsia.assistants.crop_assistant import * diff --git a/src/darsia/assistants/box_selection_assistant.py b/src/darsia/assistants/box_selection_assistant.py new file mode 100644 index 00000000..4ac0c3c1 --- /dev/null +++ b/src/darsia/assistants/box_selection_assistant.py @@ -0,0 +1,61 @@ +"""Module for defining subregions interactively.""" + +from typing import Optional + +import numpy as np + +import darsia + + +class BoxSelectionAssistant(darsia.PointSelectionAssistant): + def __init__(self, img: darsia.Image, **kwargs) -> None: + super().__init__(img) + + self.name = "Box selection assistant" + """Name of assistant.""" + + self.width = kwargs.get("width", 100) + """Width and height of selected box(es).""" + + self.boxes = None + """Output of assistant.""" + + self.shape = img.shape + """Image shape.""" + + self.dim = img.space_dim + """Dimension of image.""" + + if self.dim != 2: + raise NotImplementedError + + def _convert_pts(self) -> None: + """Convert list of points to list of boxes in terms of slices.""" + + self.boxes = [] + half_width = self.width / 2 + # Point selection assistant produces points with cartesian ordering + pts_matrix_indexing = [np.flip(pt) for pt in self.pts] + for pt in pts_matrix_indexing: + self.boxes.append( + tuple( + [ + slice( + max(int(pt[d] - half_width), 0), + min(int(pt[d] + half_width), self.shape[d]), + ) + for d in range(self.dim) + ] + ) + ) + + def __call__(self) -> Optional[np.ndarray]: + """Call the assistant.""" + + super().__call__() + self._convert_pts() + return self.boxes + + def _print_info(self) -> None: + self._convert_pts() + print(self.boxes) From bd33f1c4a066229543f79dc93f606ab1c0deeb7a Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 15:16:49 +0200 Subject: [PATCH 14/33] EX: Allow to use assistant to choose samples. --- examples/kernel_interpolation.py | 37 ++++++++++++++++++++------------ 1 file changed, 23 insertions(+), 14 deletions(-) diff --git a/examples/kernel_interpolation.py b/examples/kernel_interpolation.py index d66f6ab0..2c7cb903 100644 --- a/examples/kernel_interpolation.py +++ b/examples/kernel_interpolation.py @@ -63,17 +63,28 @@ # ! ---- CALIBRATION ---- ! # NOTE: Samples can also be defined using a graphical assistant. -samples = [ - (slice(50, 150), slice(100, 200)), - (slice(50, 150), slice(400, 500)), - (slice(50, 150), slice(600, 700)), - (slice(50, 150), slice(800, 900)), - (slice(50, 150), slice(1000, 1100)), - (slice(50, 150), slice(1200, 1300)), - (slice(50, 150), slice(1400, 1500)), - (slice(50, 150), slice(1600, 1700)), - (slice(50, 150), slice(2700, 2800)), -] +if False: + # Chosen as in thesis + samples = [ + (slice(50, 150), slice(100, 200)), + (slice(50, 150), slice(400, 500)), + (slice(50, 150), slice(600, 700)), + (slice(50, 150), slice(800, 900)), + (slice(50, 150), slice(1000, 1100)), + (slice(50, 150), slice(1200, 1300)), + (slice(50, 150), slice(1400, 1500)), + (slice(50, 150), slice(1600, 1700)), + (slice(50, 150), slice(2700, 2800)), + ] + n = len(samples) + concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) + +else: + # Ask user to provide characteristic regions with expected concentration values + assistant = darsia.BoxSelectionAssistant(image) + samples = assistant() + n = len(samples) + concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) def extract_characteristic_data(signal, samples, verbosity=False, show_plot=True): @@ -122,7 +133,7 @@ def extract_characteristic_data(signal, samples, verbosity=False, show_plot=True # histo analysis patch = signal[p] - flat_image = np.reshape(patch, (10000, 3)) # all pixels in one dimension + flat_image = np.reshape(patch, (-1, 3)) # all pixels in one dimension # patch visualisation # plt.figure("patch" + letters[i]) # plt.imshow(np.abs(patch)) @@ -154,14 +165,12 @@ def extract_characteristic_data(signal, samples, verbosity=False, show_plot=True return colours -n = len(samples) colours_RGB = extract_characteristic_data( signal=smooth_RGB, samples=samples, verbosity=True, show_plot=False ) colours_LAB = extract_characteristic_data( signal=smooth_LAB, samples=samples, verbosity=True, show_plot=False ) -concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) # ! ---- MAIN CONCENTRATION ANALYSIS ---- ! From 62ac7c219394526563bd39a23a58550516880890 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 15:30:18 +0200 Subject: [PATCH 15/33] EX: Use pre analysis for calibration. --- examples/kernel_interpolation.py | 35 +++++++++----------------------- 1 file changed, 10 insertions(+), 25 deletions(-) diff --git a/examples/kernel_interpolation.py b/examples/kernel_interpolation.py index 2c7cb903..a9a10a65 100644 --- a/examples/kernel_interpolation.py +++ b/examples/kernel_interpolation.py @@ -38,28 +38,6 @@ voxels=(slice(2300, 2500), slice(2200, 5200)) ) -# ! ---- COLOR MANAGEMENT ---- ! - -# NOTE: The steps of taking differences, and apply denoising are part of -# darsia.ConcentrationAnalysis. - -# LAB -diff_LAB = ( - (skimage.color.rgb2lab(image.img) - skimage.color.rgb2lab(baseline.img)) - + [0, 128, 128] -) / [100, 255, 255] - -# RGB -diff_RGB = skimage.img_as_float(image.img) - skimage.img_as_float(baseline.img) - -# Regularize -smooth_RGB = skimage.restoration.denoise_tv_bregman( - diff_RGB, weight=0.025, eps=1e-4, max_num_iter=100, isotropic=True -) -smooth_LAB = skimage.restoration.denoise_tv_bregman( - diff_LAB, weight=0.025, eps=1e-4, max_num_iter=100, isotropic=True -) - # ! ---- CALIBRATION ---- ! # NOTE: Samples can also be defined using a graphical assistant. @@ -80,6 +58,7 @@ concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) else: + # Same but under the use of DarSIA # Ask user to provide characteristic regions with expected concentration values assistant = darsia.BoxSelectionAssistant(image) samples = assistant() @@ -165,12 +144,18 @@ def extract_characteristic_data(signal, samples, verbosity=False, show_plot=True return colours +# Extract characteristic colors from calibration image in relative colors. +pre_analysis = darsia.ConcentrationAnalysis( + base=baseline.img_as(float), + restoration=darsia.TVD( + weight=0.025, eps=1e-4, max_num_iter=100, method="isotropic Bregman" + ), + **{"diff option": "plain"} +) +smooth_RGB = pre_analysis(image.img_as(float)).img colours_RGB = extract_characteristic_data( signal=smooth_RGB, samples=samples, verbosity=True, show_plot=False ) -colours_LAB = extract_characteristic_data( - signal=smooth_LAB, samples=samples, verbosity=True, show_plot=False -) # ! ---- MAIN CONCENTRATION ANALYSIS ---- ! From cf29cf5661c392e425a8a1c5294892f6ce4e3e3b Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 15:34:54 +0200 Subject: [PATCH 16/33] MAINT: Allow non-scalar output (when model is None). --- src/darsia/multi_image_analysis/concentrationanalysis.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/darsia/multi_image_analysis/concentrationanalysis.py b/src/darsia/multi_image_analysis/concentrationanalysis.py index 0d718794..0cb73a52 100644 --- a/src/darsia/multi_image_analysis/concentrationanalysis.py +++ b/src/darsia/multi_image_analysis/concentrationanalysis.py @@ -257,7 +257,11 @@ def __call__(self, img: darsia.Image) -> darsia.Image: plt.show() metadata = img.metadata() - return darsia.ScalarImage(concentration, **metadata) + is_scalar = len(concentration.shape) == len(img.shape) - 1 + if is_scalar: + return darsia.ScalarImage(concentration, **metadata) + else: + return type(img)(concentration, **metadata) # ! ---- Inspection routines def _inspect_diff(self, img: np.ndarray) -> None: From 3a797fe96ca06cfa9eb6562cc92b838636d6c68a Mon Sep 17 00:00:00 2001 From: Moritz Marquardt Date: Mon, 16 Oct 2023 20:18:46 +0200 Subject: [PATCH 17/33] new kernel classes initial comit --- src/darsia/utils/kernels.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 src/darsia/utils/kernels.py diff --git a/src/darsia/utils/kernels.py b/src/darsia/utils/kernels.py new file mode 100644 index 00000000..4db22cad --- /dev/null +++ b/src/darsia/utils/kernels.py @@ -0,0 +1,25 @@ +from abc import ABC +import numpy as np + + +class BaseKernel(ABC): + def __call__(self, x, y): + pass + + +# define linear kernel shifted to avoid singularities +class LinearKernel(BaseKernel): + def __init__(self, a=0): + self.a = a + + def __call__(self, x, y): + return np.sum(np.multiply(x, y), axis=-1) + self.a + + +# define gaussian kernel +class GaussianKernel(BaseKernel): + def __init__(self, gamma=1): + self.gamma = gamma + + def __call__(self, x, y): + return np.exp(-self.gamma * np.sum(np.multiply(x - y, x - y), axis=-1)) From 92a2049fb0a54f1fff2b22228e91f4b183aa2c6b Mon Sep 17 00:00:00 2001 From: Moritz Marquardt Date: Mon, 16 Oct 2023 20:19:20 +0200 Subject: [PATCH 18/33] extract characteristic data function initial --- src/darsia/utils/extractcharacteristicdata.py | 82 +++++++++++++++++++ 1 file changed, 82 insertions(+) create mode 100644 src/darsia/utils/extractcharacteristicdata.py diff --git a/src/darsia/utils/extractcharacteristicdata.py b/src/darsia/utils/extractcharacteristicdata.py new file mode 100644 index 00000000..5530cb1f --- /dev/null +++ b/src/darsia/utils/extractcharacteristicdata.py @@ -0,0 +1,82 @@ +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.patches as patches +import string + + +def extract_characteristic_data(signal, samples, verbosity=False, show_plot=True): + """Assistant to extract representative colors from input image for given patches. + + Args: + signal (np.ndarray): input signal, assumed to have the structure of a 2d, + colored image. + samples (list of slices): list of 2d regions of interest + show_plot (boolean): flag controlling whether plots are displayed. + + Returns: + np.ndarray: characteristic colors. + + """ + + # Alphabet useful for labeling in plots + letters = list(string.ascii_uppercase) + + # visualise patches + fig, ax = plt.subplots() + ax.imshow(np.abs(signal)) # visualise abs colours, because relative cols are neg + ax.set_xlabel("horizontal pixel") + ax.set_ylabel("vertical pixel") + + # double check number of patches + n = np.shape(samples)[0] # number of patches + if verbosity: + print("Number of support patches: " + str(n)) + + # init colour vector + colours = np.zeros((n, 3)) + # enumerate through all patches + for i, p in enumerate(samples): + # visualise patches on image + rect = patches.Rectangle( + (p[1].start, p[0].start), + p[1].stop - p[1].start, + p[0].stop - p[0].start, + linewidth=1, + edgecolor="w", + facecolor="none", + ) + ax.text(p[1].start + 5, p[0].start + 95, letters[i], fontsize=12, color="white") + ax.add_patch(rect) + + # histo analysis + patch = signal[p] + flat_image = np.reshape(patch, (-1, 3)) # all pixels in one dimension + # patch visualisation + # plt.figure("patch" + letters[i]) + # plt.imshow(np.abs(patch)) + H, edges = np.histogramdd( + flat_image, bins=100, range=[(-1, 1), (-1, 1), (-1, 1)] + ) + index = np.unravel_index(H.argmax(), H.shape) + col = [ + (edges[0][index[0]] + edges[0][index[0] + 1]) / 2, + (edges[1][index[1]] + edges[1][index[1] + 1]) / 2, + (edges[2][index[2]] + edges[2][index[2] + 1]) / 2, + ] + colours[i] = col + + if verbosity: + c = np.abs(colours) + plt.figure() + ax = plt.axes(projection="3d") + ax.set_xlabel("R*") + ax.set_ylabel("G*") + ax.set_zlabel("B*") + ax.scatter(colours[:, 0], colours[:, 1], colours[:, 2], c=c) + for i, c in enumerate(colours): + ax.text(c[0], c[1], c[2], letters[i]) + if show_plot: + plt.show() + + print("Characteristic colours: " + str(colours)) + return colours From afc5a350c09f5b361991779ad0a702de7f4b8080 Mon Sep 17 00:00:00 2001 From: Moritz Marquardt Date: Mon, 16 Oct 2023 20:19:46 +0200 Subject: [PATCH 19/33] kernel interpolation class initial commit --- .../signals/models/kernelinterpolation.py | 32 +++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 src/darsia/signals/models/kernelinterpolation.py diff --git a/src/darsia/signals/models/kernelinterpolation.py b/src/darsia/signals/models/kernelinterpolation.py new file mode 100644 index 00000000..fdaa6d5e --- /dev/null +++ b/src/darsia/signals/models/kernelinterpolation.py @@ -0,0 +1,32 @@ +import numpy as np + + +class KernelInterpolation: + """General kernel-based interpolation.""" + + def __init__(self, kernel, colours, concentrations): + + self.kernel = kernel + + num_data = colours.shape[0] + assert len(concentrations) == num_data, "Input data not compatible." + + x = np.array(colours) # data points / control points / support points + y = np.array(concentrations) # goal points + X = np.ones((num_data, num_data)) # kernel matrix + for i in range(num_data): + for j in range(num_data): + X[i, j] = self.kernel(x[i], x[j]) + + alpha = np.linalg.solve(X, y) + + # Cache + self.x = x + self.alpha = alpha + + def __call__(self, signal: np.ndarray): + """Apply interpolation.""" + ph_image = np.zeros(signal.shape[:2]) + for n in range(self.alpha.shape[0]): + ph_image += self.alpha[n] * self.kernel(signal, self.x[n]) + return ph_image From 13eefd41900d782beb5714c589335eff967ff9fd Mon Sep 17 00:00:00 2001 From: Moritz Marquardt Date: Mon, 16 Oct 2023 20:20:09 +0200 Subject: [PATCH 20/33] add new files necessary for kernell interpolation --- src/darsia/__init__.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/darsia/__init__.py b/src/darsia/__init__.py index a13b53fd..3a16d3c9 100644 --- a/src/darsia/__init__.py +++ b/src/darsia/__init__.py @@ -27,6 +27,8 @@ from darsia.utils.dtype import * from darsia.utils.grid import * from darsia.utils.fv import * +from darsia.utils.kernels import * +from darsia.utils.extractcharacteristicdata import * from darsia.corrections.basecorrection import * from darsia.corrections.shape.curvature import * from darsia.corrections.shape.affine import * @@ -46,6 +48,7 @@ from darsia.signals.models.staticthresholdmodel import * from darsia.signals.models.dynamicthresholdmodel import * from darsia.signals.models.binarydataselector import * +from darsia.signals.models.kernelinterpolation import * from darsia.signals.reduction.signalreduction import * from darsia.signals.reduction.monochromatic import * from darsia.signals.reduction.dimensionreduction import * From 69d5cad590806fa906774a93738c4b9344d640ce Mon Sep 17 00:00:00 2001 From: Moritz Marquardt Date: Mon, 16 Oct 2023 20:20:50 +0200 Subject: [PATCH 21/33] use darsia new kernel interpol functionality --- examples/kernel_interpolation.py | 277 +++++++------------------------ 1 file changed, 62 insertions(+), 215 deletions(-) diff --git a/examples/kernel_interpolation.py b/examples/kernel_interpolation.py index a9a10a65..c309e40b 100644 --- a/examples/kernel_interpolation.py +++ b/examples/kernel_interpolation.py @@ -1,8 +1,4 @@ -import string -from abc import ABC from pathlib import Path - -import matplotlib.patches as patches import matplotlib.pyplot as plt import numpy as np import skimage @@ -15,7 +11,8 @@ baseline_path = folder / Path("20220914-142404.TIF") image_path = folder / Path("20220914-151727.TIF") -# Setup curvature correction (here only cropping) + +# Setup DarSIA curvature correction (here only cropping) curvature_correction = darsia.CurvatureCorrection( config={ "crop": { @@ -30,7 +27,7 @@ ) transformations = [curvature_correction] -# Read-in images +# Read-in images and restrict to a small sub region for demonstration purpose baseline = darsia.imread(baseline_path, transformations=transformations).subregion( voxels=(slice(2300, 2500), slice(2200, 5200)) ) @@ -39,109 +36,39 @@ ) # ! ---- CALIBRATION ---- ! - -# NOTE: Samples can also be defined using a graphical assistant. -if False: - # Chosen as in thesis - samples = [ - (slice(50, 150), slice(100, 200)), - (slice(50, 150), slice(400, 500)), - (slice(50, 150), slice(600, 700)), - (slice(50, 150), slice(800, 900)), - (slice(50, 150), slice(1000, 1100)), - (slice(50, 150), slice(1200, 1300)), - (slice(50, 150), slice(1400, 1500)), - (slice(50, 150), slice(1600, 1700)), - (slice(50, 150), slice(2700, 2800)), - ] - n = len(samples) - concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) - -else: - # Same but under the use of DarSIA - # Ask user to provide characteristic regions with expected concentration values - assistant = darsia.BoxSelectionAssistant(image) - samples = assistant() - n = len(samples) - concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) - - -def extract_characteristic_data(signal, samples, verbosity=False, show_plot=True): - """Assistant to extract representative colors from input image for given patches. - - Args: - signal (np.ndarray): input signal, assumed to have the structure of a 2d, - colored image. - samples (list of slices): list of 2d regions of interest - show_plot (boolean): flag controlling whether plots are displayed. - - Returns: - np.ndarray: characteristic colors. - - """ - - # Alphabet useful for labeling in plots - letters = list(string.ascii_uppercase) - - # visualise patches - fig, ax = plt.subplots() - ax.imshow(np.abs(signal)) # visualise abs colours, because relative cols are neg - ax.set_xlabel("horizontal pixel") - ax.set_ylabel("vertical pixel") - - # double check number of patches - n = np.shape(samples)[0] # number of patches - if verbosity: - print("Number of support patches: " + str(n)) - - # init colour vector - colours = np.zeros((n, 3)) - # enumerate through all patches - for i, p in enumerate(samples): - # visualise patches on image - rect = patches.Rectangle( - (p[1].start, p[0].start), - p[1].stop - p[1].start, - p[0].stop - p[0].start, - linewidth=1, - edgecolor="w", - facecolor="none", - ) - ax.text(p[1].start + 5, p[0].start + 95, letters[i], fontsize=12, color="white") - ax.add_patch(rect) - - # histo analysis - patch = signal[p] - flat_image = np.reshape(patch, (-1, 3)) # all pixels in one dimension - # patch visualisation - # plt.figure("patch" + letters[i]) - # plt.imshow(np.abs(patch)) - H, edges = np.histogramdd( - flat_image, bins=100, range=[(-1, 1), (-1, 1), (-1, 1)] - ) - index = np.unravel_index(H.argmax(), H.shape) - col = [ - (edges[0][index[0]] + edges[0][index[0] + 1]) / 2, - (edges[1][index[1]] + edges[1][index[1] + 1]) / 2, - (edges[2][index[2]] + edges[2][index[2] + 1]) / 2, - ] - colours[i] = col - - if verbosity: - c = np.abs(colours) - plt.figure() - ax = plt.axes(projection="3d") - ax.set_xlabel("R*") - ax.set_ylabel("G*") - ax.set_zlabel("B*") - ax.scatter(colours[:, 0], colours[:, 1], colours[:, 2], c=c) - for i, c in enumerate(colours): - ax.text(c[0], c[1], c[2], letters[i]) - if show_plot: - plt.show() - - print("Characteristic colours: " + str(colours)) - return colours +# NOTE: +''' +Before using kernel interpolation to transform the given image to a concentration image, +the support data on which the interpolation depends has to be defined. +In the thesis these were predefined samples of the smoothed and preprocessed image and +corresponding concentrations. +Now it is possible to select these samples in a GUI using darsia.BoxSelectionAssistant(). +In these samples of the image, the most common colour was identified using a histogram +analysis, which is now available through darsia.extract_characteristic_data(). +''' + +# Chosen as in thesis: +'''samples = [ + (slice(50, 150), slice(100, 200)), + (slice(50, 150), slice(400, 500)), + (slice(50, 150), slice(600, 700)), + (slice(50, 150), slice(800, 900)), + (slice(50, 150), slice(1000, 1100)), + (slice(50, 150), slice(1200, 1300)), + (slice(50, 150), slice(1400, 1500)), + (slice(50, 150), slice(1600, 1700)), + (slice(50, 150), slice(2700, 2800)), +] +n = len(samples) +concentrations = np.append(np.linspace(1, 0.99, n - 1), 0)''' + + +# Same but under the use of DarSIA +# Ask user to provide characteristic regions with expected concentration values +assistant = darsia.BoxSelectionAssistant(image) +samples = assistant() +n = len(samples) +concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) # Extract characteristic colors from calibration image in relative colors. @@ -153,126 +80,46 @@ def extract_characteristic_data(signal, samples, verbosity=False, show_plot=True **{"diff option": "plain"} ) smooth_RGB = pre_analysis(image.img_as(float)).img -colours_RGB = extract_characteristic_data( +colours_RGB = darsia.extract_characteristic_data( signal=smooth_RGB, samples=samples, verbosity=True, show_plot=False ) # ! ---- MAIN CONCENTRATION ANALYSIS ---- ! +''' +comments: +- here, the support points as defined above are used to build a Kernel Interpolation +- This Kernel interpolation is then used as the model in the darsia.ConcentrationAnalysis +- use plain difference to keep all information (no cut off or norm) + this is the advantage of the kernel interpolation - it can handle negative colours +''' +analysis = darsia.ConcentrationAnalysis( + base=baseline.img_as(float), + restoration=darsia.TVD( + weight=0.025, eps=1e-4, max_num_iter=100, method="isotropic Bregman" + ), + model=darsia.KernelInterpolation( + darsia.GaussianKernel(gamma=9.73), colours_RGB, concentrations + ), + **{"diff option": "plain"} +) +concentration_image = analysis(image.img_as(float)).img -class KernelInterpolation: - """General kernel-based interpolation.""" - - def __init__(self, kernel, colours, concentrations): - - self.kernel = kernel - - # signal is rgb, transofrm to lab space because it is uniform and therefore - # makes sense to interpolate in - # signal = skimage.color.rgb2lab(signal) - # colours = skimage.color.rgb2lab(colours) - - num_data = colours.shape[0] - assert len(concentrations) == num_data, "Input data not compatible." - - x = np.array(colours) # data points / control points / support points - y = np.array(concentrations) # goal points - X = np.ones((num_data, num_data)) # kernel matrix - for i in range(num_data): - for j in range(num_data): - X[i, j] = self.kernel(x[i], x[j]) - - alpha = np.linalg.solve(X, y) - - # Cache - self.x = x - self.alpha = alpha - - def __call__(self, signal: np.ndarray): - """Apply interpolation.""" - ph_image = np.zeros(signal.shape[:2]) - for n in range(self.alpha.shape[0]): - ph_image += self.alpha[n] * self.kernel(signal, self.x[n]) - return ph_image - - -# ! ---- KERNEL MANAGEMENET ----- - - -class BaseKernel(ABC): - def __call__(self, x, y): - pass - - -# define linear kernel shifted to avoid singularities -class LinearKernel(BaseKernel): - def __init__(self, a=0): - self.a = a - - def __call__(self, x, y): - return np.sum(np.multiply(x, y), axis=-1) + self.a - - -# define gaussian kernel -class GaussianKernel(BaseKernel): - def __init__(self, gamma=1): - self.gamma = gamma - - def __call__(self, x, y): - return np.exp(-self.gamma * np.sum(np.multiply(x - y, x - y), axis=-1)) - - -# ! ---- MAIN ROUTINE ---- ! - -if False: - # Reference solution - - # Convert a discrete ph stripe to a numeric pH indicator. - color_to_concentration = KernelInterpolation( - GaussianKernel(gamma=9.73), colours_RGB, concentrations - ) # rgb: 9.73 , lab: 24.22 - ph_image = color_to_concentration(smooth_RGB) - # gamma=10 value retrieved from ph analysis kernel calibration was best for c = 0.95 - # which also is physically meaningful - -elif True: - # Same but under the use of DarSIA - analysis = darsia.ConcentrationAnalysis( - base=baseline.img_as(float), - restoration=darsia.TVD( - weight=0.025, eps=1e-4, max_num_iter=100, method="isotropic Bregman" - ), - model=KernelInterpolation( - GaussianKernel(gamma=9.73), colours_RGB, concentrations - ), - **{"diff option": "plain"} - ) - ph_image = analysis(image.img_as(float)).img +# ! ----- VISUALISATION ---- ! -plt.figure("cut ph val") -plt.plot(np.average(ph_image, axis=0)) +plt.figure("concentration average along one dimension") +plt.plot(np.average(concentration_image, axis=0)) plt.xlabel("horizontal pixel") plt.ylabel("concentration") -ph_image[ph_image > 1] = 1 # für visualisierung von größer 1 values -ph_image[ph_image < 0] = 0 +concentration_image[concentration_image > 1] = 1 # für visualisierung von größer 1 values +concentration_image[concentration_image < 0] = 0 fig = plt.figure() -fig.suptitle("evolution of signal processing in a subregion") +fig.suptitle("original image and resulting concentration") ax = plt.subplot(212) -ax.imshow(ph_image) -# calibration patch for the kernel calibration visualization if needed -# rect = patches.Rectangle( -# (1000, 80), -# 40, -# 40, -# linewidth=1, -# edgecolor="black", -# facecolor="none", -# ) -# ax.add_patch(rect) +ax.imshow(concentration_image) ax.set_ylabel("vertical pixel") ax.set_xlabel("horizontal pixel") - ax = plt.subplot(211) ax.imshow(skimage.img_as_ubyte(image.img)) ax.set_ylabel("vertical pixel") From ad3ea42a0c3794b50be06adca422333746f862e7 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 21:04:07 +0200 Subject: [PATCH 22/33] DOC: Add simple documentation. --- src/darsia/utils/kernels.py | 62 ++++++++++++++++++++++++++++++++----- 1 file changed, 55 insertions(+), 7 deletions(-) diff --git a/src/darsia/utils/kernels.py b/src/darsia/utils/kernels.py index 4db22cad..8e4c94ef 100644 --- a/src/darsia/utils/kernels.py +++ b/src/darsia/utils/kernels.py @@ -1,25 +1,73 @@ +"""Standard kernels accompanied by abstract base class. + +Provided: + * Linear kernel + * Gaussian kernel + +""" + from abc import ABC + import numpy as np class BaseKernel(ABC): - def __call__(self, x, y): + """Abstract base class for kernel.""" + + def __call__(self, x: np.ndarray, y: np.ndarray) -> np.ndarray: + """Compute kernel between two arrays. + + Args: + x (np.ndarray): first array + y (np.ndarray): second array + + Returns: + np.ndarray: kernel between x and y + + """ pass -# define linear kernel shifted to avoid singularities class LinearKernel(BaseKernel): - def __init__(self, a=0): + """Linear kernel. + + NOTE: Allows to to avoid singularities by shifting the kernel by a constant. + + """ + + def __init__(self, a: float = 0): self.a = a + """Shift of the kernel.""" + + def __call__(self, x: np.ndarray, y: np.ndarray) -> np.ndarray: + """Compute kernel between two arrays. + + Args: + x (np.ndarray): first array + y (np.ndarray): second array - def __call__(self, x, y): + Returns: + np.ndarray: kernel between x and y + + """ return np.sum(np.multiply(x, y), axis=-1) + self.a -# define gaussian kernel class GaussianKernel(BaseKernel): - def __init__(self, gamma=1): + """Gaussian kernel.""" + + def __init__(self, gamma: float = 1.0): self.gamma = gamma + """Gamma parameter of the kernel.""" + + def __call__(self, x: np.ndarray, y: np.ndarray) -> np.ndarray: + """Compute kernel between two arrays. + + Args: + x (np.ndarray): first array + y (np.ndarray): second array - def __call__(self, x, y): + Returns: + np.ndarray: kernel between x and y + """ return np.exp(-self.gamma * np.sum(np.multiply(x - y, x - y), axis=-1)) From 947a67ed83cf004a44f2555aa6bb72a8dd2db008 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 21:06:41 +0200 Subject: [PATCH 23/33] DOC: Add typing --- src/darsia/utils/extractcharacteristicdata.py | 21 +++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/src/darsia/utils/extractcharacteristicdata.py b/src/darsia/utils/extractcharacteristicdata.py index 5530cb1f..bc91f605 100644 --- a/src/darsia/utils/extractcharacteristicdata.py +++ b/src/darsia/utils/extractcharacteristicdata.py @@ -1,10 +1,18 @@ -import numpy as np -import matplotlib.pyplot as plt -import matplotlib.patches as patches +"""Module to extract characteristic data from input image for given patches.""" + import string +import matplotlib.patches as patches +import matplotlib.pyplot as plt +import numpy as np -def extract_characteristic_data(signal, samples, verbosity=False, show_plot=True): + +def extract_characteristic_data( + signal: np.ndarray, + samples: list[tuple[slice]], + verbosity: bool = False, + surpress_plot: bool = False, +) -> np.ndarray: """Assistant to extract representative colors from input image for given patches. Args: @@ -48,7 +56,7 @@ def extract_characteristic_data(signal, samples, verbosity=False, show_plot=True ax.text(p[1].start + 5, p[0].start + 95, letters[i], fontsize=12, color="white") ax.add_patch(rect) - # histo analysis + # histogramm analysis patch = signal[p] flat_image = np.reshape(patch, (-1, 3)) # all pixels in one dimension # patch visualisation @@ -75,8 +83,9 @@ def extract_characteristic_data(signal, samples, verbosity=False, show_plot=True ax.scatter(colours[:, 0], colours[:, 1], colours[:, 2], c=c) for i, c in enumerate(colours): ax.text(c[0], c[1], c[2], letters[i]) - if show_plot: + if not surpress_plot: plt.show() print("Characteristic colours: " + str(colours)) + return colours From 5487a65b9c2456077867b32e81639eddae3439ca Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 21:09:20 +0200 Subject: [PATCH 24/33] DOC: Add description of method --- src/darsia/assistants/box_selection_assistant.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/darsia/assistants/box_selection_assistant.py b/src/darsia/assistants/box_selection_assistant.py index 4ac0c3c1..3c39195e 100644 --- a/src/darsia/assistants/box_selection_assistant.py +++ b/src/darsia/assistants/box_selection_assistant.py @@ -57,5 +57,6 @@ def __call__(self) -> Optional[np.ndarray]: return self.boxes def _print_info(self) -> None: + """Print info about boxes so far assigned by the assistant.""" self._convert_pts() print(self.boxes) From eb5cca2ca40b827404f655fddb355b0624eefe45 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 21:14:05 +0200 Subject: [PATCH 25/33] DOC: Add simple documentation. --- .../signals/models/kernelinterpolation.py | 48 +++++++++++++++++-- 1 file changed, 45 insertions(+), 3 deletions(-) diff --git a/src/darsia/signals/models/kernelinterpolation.py b/src/darsia/signals/models/kernelinterpolation.py index fdaa6d5e..735bfbe0 100644 --- a/src/darsia/signals/models/kernelinterpolation.py +++ b/src/darsia/signals/models/kernelinterpolation.py @@ -1,12 +1,43 @@ +"""Kernel-based interpolation. + +The kernel-based interpolation is a general interpolation method that can be used +for different interpolation tasks. The interpolation is based on a kernel matrix +that is computed from the input data. The input data consists of a set of data +points (colours) and a set of goal points (concentrations). The kernel matrix is +then used to compute the interpolation weights. The interpolation weights are +computed by solving a linear system of equations. The interpolation weights are +then used to compute the interpolated image. + +One example use case is the concentration analysis. + +""" + import numpy as np +import darsia + class KernelInterpolation: """General kernel-based interpolation.""" - def __init__(self, kernel, colours, concentrations): + def __init__( + self, kernel: darsia.BaseKernel, colours: np.ndarray, concentrations: np.ndarray + ): + """Setup of the kernel-based interpolation. + + Args: + kernel (darsia.BaseKernel): kernel + colours (np.ndarray): colours (support points) + concentrations (np.ndarray): concentrations (goal points) + + Raises: + AssertionError: if the input data is not compatible, i.e., the number of + colours and concentrations does not match. + + """ self.kernel = kernel + """Kernel.""" num_data = colours.shape[0] assert len(concentrations) == num_data, "Input data not compatible." @@ -22,10 +53,21 @@ def __init__(self, kernel, colours, concentrations): # Cache self.x = x + """Support points.""" + self.alpha = alpha + """Interpolation weights.""" + + def __call__(self, signal: np.ndarray) -> np.ndarray: + """Apply interpolation. + + Args: + signal (np.ndarray): signal to be interpolated + + Returns: + np.ndarray: interpolated signal - def __call__(self, signal: np.ndarray): - """Apply interpolation.""" + """ ph_image = np.zeros(signal.shape[:2]) for n in range(self.alpha.shape[0]): ph_image += self.alpha[n] * self.kernel(signal, self.x[n]) From 81040b96fac00c0a56e03b205f31d7bff80894c3 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 21:23:08 +0200 Subject: [PATCH 26/33] EX: Reuse analysis for the better narrative. --- examples/kernel_interpolation.py | 45 +++++++++++++++----------------- 1 file changed, 21 insertions(+), 24 deletions(-) diff --git a/examples/kernel_interpolation.py b/examples/kernel_interpolation.py index c309e40b..49b25c23 100644 --- a/examples/kernel_interpolation.py +++ b/examples/kernel_interpolation.py @@ -1,4 +1,5 @@ from pathlib import Path + import matplotlib.pyplot as plt import numpy as np import skimage @@ -35,9 +36,8 @@ voxels=(slice(2300, 2500), slice(2200, 5200)) ) -# ! ---- CALIBRATION ---- ! -# NOTE: -''' +# ! ---- MAIN CONCENTRATION ANALYSIS AND CALIBRATION ---- ! +""" Before using kernel interpolation to transform the given image to a concentration image, the support data on which the interpolation depends has to be defined. In the thesis these were predefined samples of the smoothed and preprocessed image and @@ -45,10 +45,10 @@ Now it is possible to select these samples in a GUI using darsia.BoxSelectionAssistant(). In these samples of the image, the most common colour was identified using a histogram analysis, which is now available through darsia.extract_characteristic_data(). -''' +""" # Chosen as in thesis: -'''samples = [ +"""samples = [ (slice(50, 150), slice(100, 200)), (slice(50, 150), slice(400, 500)), (slice(50, 150), slice(600, 700)), @@ -60,7 +60,7 @@ (slice(50, 150), slice(2700, 2800)), ] n = len(samples) -concentrations = np.append(np.linspace(1, 0.99, n - 1), 0)''' +concentrations = np.append(np.linspace(1, 0.99, n - 1), 0)""" # Same but under the use of DarSIA @@ -70,40 +70,35 @@ n = len(samples) concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) - +# Predefine concentration analysis for now without any model (to be defined later). # Extract characteristic colors from calibration image in relative colors. -pre_analysis = darsia.ConcentrationAnalysis( +analysis = darsia.ConcentrationAnalysis( base=baseline.img_as(float), restoration=darsia.TVD( weight=0.025, eps=1e-4, max_num_iter=100, method="isotropic Bregman" ), **{"diff option": "plain"} ) -smooth_RGB = pre_analysis(image.img_as(float)).img +smooth_RGB = analysis(image.img_as(float)).img colours_RGB = darsia.extract_characteristic_data( - signal=smooth_RGB, samples=samples, verbosity=True, show_plot=False + signal=smooth_RGB, samples=samples, verbosity=True, surpress_plot=True ) -# ! ---- MAIN CONCENTRATION ANALYSIS ---- ! -''' +# Now add kernel interpolation as model trained by the extracted information. +""" comments: - here, the support points as defined above are used to build a Kernel Interpolation - This Kernel interpolation is then used as the model in the darsia.ConcentrationAnalysis - use plain difference to keep all information (no cut off or norm) this is the advantage of the kernel interpolation - it can handle negative colours -''' -analysis = darsia.ConcentrationAnalysis( - base=baseline.img_as(float), - restoration=darsia.TVD( - weight=0.025, eps=1e-4, max_num_iter=100, method="isotropic Bregman" - ), - model=darsia.KernelInterpolation( - darsia.GaussianKernel(gamma=9.73), colours_RGB, concentrations - ), - **{"diff option": "plain"} +- finding the right kernel parameters is part of the modeling +""" +analysis.model = darsia.KernelInterpolation( + darsia.GaussianKernel(gamma=9.73), colours_RGB, concentrations ) -concentration_image = analysis(image.img_as(float)).img +# Finally, apply the (full) concentration analysis to analyze the test image +concentration_image = analysis(image.img_as(float)).img # ! ----- VISUALISATION ---- ! @@ -112,7 +107,9 @@ plt.xlabel("horizontal pixel") plt.ylabel("concentration") -concentration_image[concentration_image > 1] = 1 # für visualisierung von größer 1 values +concentration_image[ + concentration_image > 1 +] = 1 # für visualisierung von größer 1 values concentration_image[concentration_image < 0] = 0 fig = plt.figure() fig.suptitle("original image and resulting concentration") From 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Date: Mon, 16 Oct 2023 21:49:25 +0200 Subject: [PATCH 28/33] MAINT: Make letter placement in plot relative to image size. --- src/darsia/utils/extractcharacteristicdata.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/src/darsia/utils/extractcharacteristicdata.py b/src/darsia/utils/extractcharacteristicdata.py index bc91f605..57183b07 100644 --- a/src/darsia/utils/extractcharacteristicdata.py +++ b/src/darsia/utils/extractcharacteristicdata.py @@ -53,7 +53,13 @@ def extract_characteristic_data( edgecolor="w", facecolor="none", ) - ax.text(p[1].start + 5, p[0].start + 95, letters[i], fontsize=12, color="white") + ax.text( + int(p[1].start + 0.05 * (p[1].stop - p[1].start)), + int(p[0].start + 0.95 * (p[0].stop - p[0].start)), + letters[i], + fontsize=12, + color="white", + ) ax.add_patch(rect) # histogramm analysis From 4770c9103d7c82540c1c0829da81e6467e801387 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 21:50:40 +0200 Subject: [PATCH 29/33] EX: Use predefined images, use pre-defined samples by default, close plots. --- examples/kernel_interpolation.py | 99 ++++++++++++++------------------ 1 file changed, 42 insertions(+), 57 deletions(-) diff --git a/examples/kernel_interpolation.py b/examples/kernel_interpolation.py index 49b25c23..5b742ea9 100644 --- a/examples/kernel_interpolation.py +++ b/examples/kernel_interpolation.py @@ -8,33 +8,12 @@ # ! ---- DATA MANAGEMENT ---- ! -folder = Path("images") -baseline_path = folder / Path("20220914-142404.TIF") -image_path = folder / Path("20220914-151727.TIF") - - -# Setup DarSIA curvature correction (here only cropping) -curvature_correction = darsia.CurvatureCorrection( - config={ - "crop": { - # Define the pixel values (x,y) of the corners of the ROI. - # Start at top left corner and then continue counterclockwise. - "pts_src": [[300, 600], [300, 4300], [7600, 4300], [7600, 600]], - # Specify the true dimensions of the reference points - "width": 0.92, - "height": 0.5, - } - } -) -transformations = [curvature_correction] - -# Read-in images and restrict to a small sub region for demonstration purpose -baseline = darsia.imread(baseline_path, transformations=transformations).subregion( - voxels=(slice(2300, 2500), slice(2200, 5200)) -) -image = darsia.imread(image_path, transformations=transformations).subregion( - voxels=(slice(2300, 2500), slice(2200, 5200)) -) +# The example images originate from a water tracer experiment with a multi-colored +# indicator. Green color corresponds to concentrations close to 100%, while blue +# color corresponds to concentrations ~99-0%. The images have been cropped and resized +# mainly for data storage reasons. It is recommended to use full resolution images. +baseline = darsia.imread(Path("images/kernel_interpolation_example_base.npz")) +image = darsia.imread(Path("images/kernel_interpolation_example_test.npz")) # ! ---- MAIN CONCENTRATION ANALYSIS AND CALIBRATION ---- ! """ @@ -47,31 +26,7 @@ analysis, which is now available through darsia.extract_characteristic_data(). """ -# Chosen as in thesis: -"""samples = [ - (slice(50, 150), slice(100, 200)), - (slice(50, 150), slice(400, 500)), - (slice(50, 150), slice(600, 700)), - (slice(50, 150), slice(800, 900)), - (slice(50, 150), slice(1000, 1100)), - (slice(50, 150), slice(1200, 1300)), - (slice(50, 150), slice(1400, 1500)), - (slice(50, 150), slice(1600, 1700)), - (slice(50, 150), slice(2700, 2800)), -] -n = len(samples) -concentrations = np.append(np.linspace(1, 0.99, n - 1), 0)""" - - -# Same but under the use of DarSIA -# Ask user to provide characteristic regions with expected concentration values -assistant = darsia.BoxSelectionAssistant(image) -samples = assistant() -n = len(samples) -concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) - # Predefine concentration analysis for now without any model (to be defined later). -# Extract characteristic colors from calibration image in relative colors. analysis = darsia.ConcentrationAnalysis( base=baseline.img_as(float), restoration=darsia.TVD( @@ -79,20 +34,44 @@ ), **{"diff option": "plain"} ) -smooth_RGB = analysis(image.img_as(float)).img -colours_RGB = darsia.extract_characteristic_data( - signal=smooth_RGB, samples=samples, verbosity=True, surpress_plot=True -) + +# The goal is to define ome ROIs for which physical information is known. +# One possibility is to use a GUI for interactive use. This option can +# be activated on demand. For testing purposes this example by default +# uses a pre-defined sample selection. +if True: + samples = [ + (slice(15, 40), slice(20, 45)), + (slice(15, 40), slice(220, 245)), + (slice(15, 40), slice(420, 445)), + (slice(15, 40), slice(720, 745)), + ] +else: + # Same but under the use of a graphical user interface. + # Ask user to provide characteristic regions with expected concentration values + assistant = darsia.BoxSelectionAssistant(image) + samples = assistant() + +# For simplicity the concentrations are pre-defined. These could also be defined +# by the user. +n = len(samples) +concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) # Now add kernel interpolation as model trained by the extracted information. """ comments: -- here, the support points as defined above are used to build a Kernel Interpolation +- Here, the support points as defined above are used to build a Kernel Interpolation. + For this, the 'concentration' without any model is used, correpsonding to the difference + to the baseline. - This Kernel interpolation is then used as the model in the darsia.ConcentrationAnalysis - use plain difference to keep all information (no cut off or norm) this is the advantage of the kernel interpolation - it can handle negative colours - finding the right kernel parameters is part of the modeling """ +smooth_RGB = analysis(image.img_as(float)).img +colours_RGB = darsia.extract_characteristic_data( + signal=smooth_RGB, samples=samples, verbosity=True, surpress_plot=True +) analysis.model = darsia.KernelInterpolation( darsia.GaussianKernel(gamma=9.73), colours_RGB, concentrations ) @@ -126,4 +105,10 @@ # indicator = np.arange(101) / 100 # plt.axis("off") # plt.imshow([indicator, indicator, indicator, indicator, indicator]) -plt.show() + +# To enable the example as test, the plots are closed after short time. +# Pause longer if it is desired to keep the images on the screen. +print("Warning: The plot is closed after short time to enable testing.") +plt.show(block=False) +plt.pause(5) +plt.close() From e9990a02034e3a099e37a0205933c6d841b0f1da Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 22:30:03 +0200 Subject: [PATCH 30/33] TST: Add kernel example to integration tests. --- tests/integration/test_examples.py | 25 ++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) diff --git a/tests/integration/test_examples.py b/tests/integration/test_examples.py index fb32f94b..5bf1de29 100644 --- a/tests/integration/test_examples.py +++ b/tests/integration/test_examples.py @@ -54,7 +54,7 @@ def test_imread(): def test_imread_vtu_images(): # Test whether images required for test are available - folder = Path("../../examples/images") + folder = Path(f"{os.path.dirname(__file__)}/../../examples/images") images_exist = len(list(sorted(folder.glob("fracture_flow*.vtu")))) == 2 if not images_exist: @@ -66,3 +66,26 @@ def test_imread_vtu_images(): f'python {str(Path(f"{os.path.dirname(__file__)}/../../examples/vtu_images.py"))}' ) ) + + +def test_kernel_interpolation(): + + # Test whether images required for test are available + folder = Path(f"{os.path.dirname(__file__)}/../../examples/images") + images_exist = all( + [ + len(list(sorted(folder.glob(f"kernel_interpolation_example_{app}.npz")))) + == 1 + for app in ["base", "test"] + ] + ) + + if not images_exist: + pytest.xfail("Images required for test not available.") + + # If so, run test. + assert not ( + os.system( + f'python {str(Path(f"{os.path.dirname(__file__)}/../../examples/kernel_interpolation.py"))}' + ) + ) From f0c81beef5232cf2faa39154dda3f24815c86aa9 Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Mon, 16 Oct 2023 22:31:05 +0200 Subject: [PATCH 31/33] EX: Use OS independent paths --- examples/kernel_interpolation.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/examples/kernel_interpolation.py b/examples/kernel_interpolation.py index 5b742ea9..b86a4469 100644 --- a/examples/kernel_interpolation.py +++ b/examples/kernel_interpolation.py @@ -1,4 +1,4 @@ -from pathlib import Path +import os import matplotlib.pyplot as plt import numpy as np @@ -12,8 +12,12 @@ # indicator. Green color corresponds to concentrations close to 100%, while blue # color corresponds to concentrations ~99-0%. The images have been cropped and resized # mainly for data storage reasons. It is recommended to use full resolution images. -baseline = darsia.imread(Path("images/kernel_interpolation_example_base.npz")) -image = darsia.imread(Path("images/kernel_interpolation_example_test.npz")) +baseline_path = ( + f"{os.path.dirname(__file__)}/images/kernel_interpolation_example_base.npz" +) +image_path = f"{os.path.dirname(__file__)}/images/kernel_interpolation_example_test.npz" +baseline = darsia.imread(baseline_path) +image = darsia.imread(image_path) # ! ---- MAIN CONCENTRATION ANALYSIS AND CALIBRATION ---- ! """ @@ -32,7 +36,7 @@ restoration=darsia.TVD( weight=0.025, eps=1e-4, max_num_iter=100, method="isotropic Bregman" ), - **{"diff option": "plain"} + **{"diff option": "plain"}, ) # The goal is to define ome ROIs for which physical information is known. From ae3b9e66f37c5e98654bb6385ac8fce68ae016f1 Mon Sep 17 00:00:00 2001 From: Moritz Marquardt Date: Tue, 17 Oct 2023 10:16:19 +0200 Subject: [PATCH 32/33] MAINT: remove indicator plot --- examples/kernel_interpolation.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/examples/kernel_interpolation.py b/examples/kernel_interpolation.py index b86a4469..62486463 100644 --- a/examples/kernel_interpolation.py +++ b/examples/kernel_interpolation.py @@ -90,10 +90,11 @@ plt.xlabel("horizontal pixel") plt.ylabel("concentration") -concentration_image[ - concentration_image > 1 -] = 1 # für visualisierung von größer 1 values -concentration_image[concentration_image < 0] = 0 +# darsia.CombinedModel([kernel_interpolation, darsia.ClipModel(**{"max value": 1.})]) +# concentration_image[ +# concentration_image > 1 +# ] = 1 # to visualise values > 1 as 1 +# concentration_image[concentration_image < 0] = 0 fig = plt.figure() fig.suptitle("original image and resulting concentration") ax = plt.subplot(212) @@ -105,11 +106,6 @@ ax.set_ylabel("vertical pixel") ax.set_xlabel("horizontal pixel") -# plt.figure("indicator") -# indicator = np.arange(101) / 100 -# plt.axis("off") -# plt.imshow([indicator, indicator, indicator, indicator, indicator]) - # To enable the example as test, the plots are closed after short time. # Pause longer if it is desired to keep the images on the screen. print("Warning: The plot is closed after short time to enable testing.") From 6f6799aaecd9a1ef52989991beb168622678d1fe Mon Sep 17 00:00:00 2001 From: Jakub Both Date: Tue, 17 Oct 2023 10:22:40 +0200 Subject: [PATCH 33/33] EX: Add clip as part of model, use concentrations in % and add colorbar. --- examples/kernel_interpolation.py | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/examples/kernel_interpolation.py b/examples/kernel_interpolation.py index b86a4469..7556efa4 100644 --- a/examples/kernel_interpolation.py +++ b/examples/kernel_interpolation.py @@ -57,9 +57,9 @@ samples = assistant() # For simplicity the concentrations are pre-defined. These could also be defined -# by the user. +# by the user. Here, we use concentrations in %, such that 100 means fully saturated. n = len(samples) -concentrations = np.append(np.linspace(1, 0.99, n - 1), 0) +concentrations = 100 * np.append(np.linspace(1, 0.99, n - 1), 0) # Now add kernel interpolation as model trained by the extracted information. """ @@ -71,13 +71,19 @@ - use plain difference to keep all information (no cut off or norm) this is the advantage of the kernel interpolation - it can handle negative colours - finding the right kernel parameters is part of the modeling +- also clip the interpolated values at 100% """ smooth_RGB = analysis(image.img_as(float)).img colours_RGB = darsia.extract_characteristic_data( signal=smooth_RGB, samples=samples, verbosity=True, surpress_plot=True ) -analysis.model = darsia.KernelInterpolation( - darsia.GaussianKernel(gamma=9.73), colours_RGB, concentrations +analysis.model = darsia.CombinedModel( + [ + darsia.KernelInterpolation( + darsia.GaussianKernel(gamma=9.73), colours_RGB, concentrations + ), + darsia.ClipModel(**{"max value": 100.0}), + ] ) # Finally, apply the (full) concentration analysis to analyze the test image @@ -90,26 +96,18 @@ plt.xlabel("horizontal pixel") plt.ylabel("concentration") -concentration_image[ - concentration_image > 1 -] = 1 # für visualisierung von größer 1 values -concentration_image[concentration_image < 0] = 0 fig = plt.figure() fig.suptitle("original image and resulting concentration") ax = plt.subplot(212) -ax.imshow(concentration_image) +im = ax.imshow(concentration_image) ax.set_ylabel("vertical pixel") ax.set_xlabel("horizontal pixel") +cb = fig.colorbar(im, orientation="horizontal", label="concentration [%]") ax = plt.subplot(211) ax.imshow(skimage.img_as_ubyte(image.img)) ax.set_ylabel("vertical pixel") ax.set_xlabel("horizontal pixel") -# plt.figure("indicator") -# indicator = np.arange(101) / 100 -# plt.axis("off") -# plt.imshow([indicator, indicator, indicator, indicator, indicator]) - # To enable the example as test, the plots are closed after short time. # Pause longer if it is desired to keep the images on the screen. print("Warning: The plot is closed after short time to enable testing.")