merge changes from 'fat-shift' branch

.github/workflows/macos.yml

@@ -88,6 +88,8 @@ jobs:
         mkdir ITK-build-${PYTHON_VERSION_STR}-${_PYTHON_HOST_PLATFORM}
         cd ITK-build-${PYTHON_VERSION_STR}-${_PYTHON_HOST_PLATFORM}
         cmake \
+            -DCMAKE_POLICY_VERSION_MINIMUM=3.5 \
+            -DITK_USE_SYSTEM_PNG=ON \
             -DBUILD_SHARED_LIBS=OFF \
             -DBUILD_TESTING=OFF \
             -DBUILD_EXAMPLES=OFF \

samseg/Fatshift.py

@@ -0,0 +1,119 @@
+import numpy as np
+
+from .GMM_fat_shift import GMM_fat_shift
+
+
+class Fatshift:
+    def __init__(self, fat_shift, sigma_d,
+                 numberOfGaussiansPerClass, classNames, initialWs,
+                 classPriors, classPosteriors, imageBuffer, mask):
+
+        self.numberOfGaussiansPerClass = numberOfGaussiansPerClass
+        self.classNames = classNames
+        self.ws = initialWs
+        self.classPriors = classPriors
+        self.classPosteriors = classPosteriors
+        self.imageBuffer = imageBuffer
+        self.mask = mask
+
+        self.fat_shift = fat_shift  # 3
+        self.sigma_d = sigma_d  # 1
+        self.gmm_fat_shift = None
+
+
+    def fitModel(self):
+        self.gmm_fat_shift = GMM_fat_shift(self.fat_shift, self.sigma_d,
+                                           self.numberOfGaussiansPerClass,
+                                           self.classNames,
+                                           self.ws,
+                                           self.classPriors,
+                                           self.classPosteriors,
+                                           self.imageBuffer,
+                                           self.mask)
+
+        self.estimateModelParameters()
+
+
+    # iteration loop
+    def estimateModelParameters(self):
+        phi, mu_s, sigma_s, mu_0k, lambda_k, alpha_k, beta_k = self.gmm_fat_shift.initializeGMMParameters()
+
+        iters = [1] * 10 + [1] * 20
+
+        old_elbo = self.gmm_fat_shift.evaluateELBO()
+
+        fixed_posteriors = self.gmm_fat_shift.fixed_posteriors
+        imagedata = self.gmm_fat_shift.imagedata
+
+        """
+        fat_gaussians = self.gmm_fat_shift.fat_gaussians
+        water_gaussians = self.gmm_fat_shift.water_gaussians
+
+        post_f = np.sum(fixed_posteriors[:, :, :, fat_gaussians], axis=3)
+        post_f = np.squeeze(post_f[:, 150, :])
+        phi_f = np.squeeze(np.sum(phi[:, :, :, fat_gaussians], axis=3))
+        phi_w = np.sum(phi[:, :, :, water_gaussians], axis=3)
+        fig, axs = plt.subplots(3, 3)
+
+        im10 = axs[1,0].imshow(np.squeeze(phi_w[:, 150, :]))
+        im11 = axs[1,1].imshow(np.squeeze(phi[:, 150, :, fat_gaussians[0]]))
+        im12 = axs[1,2].imshow(np.squeeze(phi[:, 150, :, fat_gaussians[1]]))
+        im00 = axs[0,0].imshow(np.exp(np.squeeze(imagedata[:, 150, :])), cmap="gray")
+        im01 = axs[0,1].imshow(np.exp(np.squeeze(mu_s[:, 150, :])), cmap="gray")
+        post_rgb = np.zeros((post_f.shape[0], post_f.shape[1], 3))
+        post_rgb[:, :, 0] = post_f
+        post_rgb[:, :, 1] = np.squeeze(phi_f[:, 150, :])
+        im02 = axs[0,2].imshow(post_rgb)
+        axs[2,2].plot(0, 0, 'o')
+        print('before pause')
+        fig.canvas.draw()
+        fig.canvas.flush_events()
+        plt.pause(0.05)
+        print('after pause')
+        """
+
+        iters_elbo = []
+        elbo_diff = []
+        for j, niter in enumerate(iters):
+            print(niter)
+
+            """
+            phi_f = np.squeeze(np.sum(phi[:, :, :, fat_gaussians], axis=3))
+            phi_w = np.sum(phi[:, :, :, water_gaussians], axis=3)
+            im10.set_data(np.squeeze(phi_w[:,150,:]))
+            im11.set_data(np.squeeze(phi[:, 150, :, fat_gaussians[0]]))
+            im12.set_data(np.squeeze(phi[:, 150, :, fat_gaussians[1]]))
+            im00.set_data(np.exp(np.squeeze(imagedata[:, 150, :])))
+            im01.set_data(np.exp(np.squeeze(mu_s[:, 150, :])))
+
+            post_rgb = np.zeros((post_f.shape[0], post_f.shape[1], 3))
+            post_rgb[:, :, 0] = post_f
+            post_rgb[:, :, 1] = np.squeeze(phi_f[:, 150, :])
+            im02.set_data(post_rgb)
+            """
+
+            print('blaa')
+            phi, mu_s, sigma_s, mu_0k, lambda_k, alpha_k, beta_k = self.gmm_fat_shift.fitGMMParameters(niter)
+
+            new_elbo = self.gmm_fat_shift.evaluateELBO()            
+            iters_elbo.append(j)
+            print("ELBO: " + str(old_elbo - new_elbo))
+            elbo_diff.append(old_elbo-new_elbo)
+
+            """
+            axs[2,2].plot(iters_elbo, elbo_diff)
+            """
+
+            print(mu_0k)
+            print(lambda_k)
+            print(alpha_k)
+            print(beta_k)
+            old_elbo = np.copy(new_elbo)
+
+            """
+            fig.canvas.draw()
+            fig.canvas.flush_events()
+            """
+
+
+