Fix window benchmark for medical imaging

Author: weiya711

numpy/image.py

@@ -4,9 +4,35 @@
 import pytest
 import matplotlib.pyplot as plt 
 import sparse
-from util import ImagePydataSparseTensorLoader, safeCastPydataTensorToInts, plot_image
+from util import ImagePydataSparseTensorLoader, safeCastPydataTensorToInts 
 
 
+# plot_image plots the given original, binned, xor, and sparse xor images
+# for the numpy/image.py. Used for debugging only with the --plot flag
+def plot_image(img, img1, img2, xor_img, sparse_xor_img, t1, t2, window=None):
+    f, ax = plt.subplots(2, 3)
+    ax[0, 0].imshow(img1, 'gray')
+    ax[0, 0].title.set_text("Binned Image 1. t1 = " + str(t1))
+
+    ax[0, 1].imshow(img2, 'gray')
+    ax[0, 1].title.set_text("Binned Image 2. t2 = " + str(t2))
+
+    ax[1, 0].imshow(img, 'gray')
+    ax[1, 0].title.set_text("Saturdated Image")
+
+    ax[1, 1].imshow(xor_img, 'gray')
+    ax[1, 1].title.set_text("XOR Image")
+
+    ax[1, 2].imshow(sparse_xor_img, 'gray')
+    ax[1, 2].title.set_text("Sparse XOR Image")
+
+    if window is not None:
+        ax[0, 2].imshow(window, 'gray')
+        ax[0, 2].title.set_text("Fused Window Image")
+
+    f.tight_layout()
+    plt.show()
+
 
 @pytest.mark.parametrize("num", list(range(1, 99))) 
 @pytest.mark.parametrize("pt1", [0.5])
@@ -115,7 +141,81 @@ def dense_bench():
         tacoBench(dense_bench)
 
 #TODO: Add in a benchmark that uses windowing for medical imaging as well. 
+@pytest.mark.parametrize("num", list(range(1, 99))) 
+@pytest.mark.parametrize("pt1", [0.5])
+@pytest.mark.parametrize("window_size", [0.25, 0.2, 0.15, 0.1])
+def bench_edge_detection_window_pydata(tacoBench, num, pt1, window_size, plot):
+        loader = ImagePydataSparseTensorLoader()
+        sparse_bin_img1 = safeCastPydataTensorToInts(loader.sparse_image(num, pt1, 1))
+        sparse_bin_img2 = safeCastPydataTensorToInts(loader.sparse_image(num, pt1+0.05, 2))
+        bin_img1 = loader.dense_image(num, pt1, 1) 
+        bin_img2 = loader.dense_image(num, pt1 + 0.05, 2)
 
+        mid0 = int(bin_img1.shape[0]/2)
+        mid1 = int(bin_img1.shape[1]/2) 
+
+        win_len0 = int(window_size * bin_img1.shape[0])
+        win_len1 = int(window_size * bin_img1.shape[1])
+
+        if plot:
+            print(sparse_bin_img1.shape)
+            print(sparse_bin_img2.shape)
+
+        def sparse_bench():
+            swin1 = sparse_bin_img1[mid0 - win_len0:mid0 + win_len0, mid1 - win_len1:mid1 + win_len1]
+            swin2 = sparse_bin_img2[mid0 - win_len0:mid0 + win_len0, mid1 - win_len1:mid1 + win_len1]
+            sparse_xor_img = np.logical_xor(swin1, swin2).astype('int')
+            return sparse_xor_img
+
+        def dense_bench():
+            win1 = bin_img1[mid0 - win_len0:mid0 + win_len0, mid1 - win_len1:mid1 + win_len1]
+            win2 = bin_img2[mid0 - win_len0:mid0 + win_len0, mid1 - win_len1:mid1 + win_len1]
+            xor_img = np.logical_xor(win1, win2).astype('int')
+            return xor_img
+
+        ret = tacoBench(sparse_bench)
+        sparse_xor_img = sparse_bench()
+        xor_img = dense_bench()
+        
+        if plot:
+            print(sparse_xor_img)
+            print("sparse img1 nnz =", sparse_bin_img1.nnz, "    ", np.sum(bin_img1 != 0))
+            print("sparse img2 nnz =", sparse_bin_img2.nnz, "    ", np.sum(bin_img2 != 0))
+            num_elements = float(np.prod(bin_img1.shape))
+            print("Sparse xor NNZ = ", sparse_xor_img.nnz, "\t", "Dense xor NNZ = ", np.sum(xor_img != 0))
+            print("Sparsity img 1 ", np.sum(bin_img1 != 0) / num_elements)
+            print("Sparsity img 2 ", np.sum(bin_img2 != 0) / num_elements)
+            print("Sparsity xor ", np.sum(xor_img != 0) / num_elements)
+            sparse_xor_img = sparse_xor_img.todense()
+            t1 = round(loader.max[num]*pt1, 2)
+            t2 = round(loader.max[num]*(pt1 + 0.05), 2)
+            print(xor_img)
+            plot_image(loader.img[num], bin_img1, bin_img2, xor_img, sparse_xor_img, t1, t2)
+
+        assert(sparse_xor_img.nnz == np.sum(xor_img != 0))
+
+@pytest.mark.parametrize("num", list(range(1, 99))) 
+@pytest.mark.parametrize("pt1", [0.5])
+@pytest.mark.parametrize("window_size", [0.25, 0.2, 0.15, 0.1])
+def bench_edge_detection_window_dense(tacoBench, num, pt1, window_size):
+        loader = ImagePydataSparseTensorLoader()
+        bin_img1 = loader.dense_image(num, pt1, 1) 
+        bin_img2 = loader.dense_image(num, pt1 + 0.05, 2)
+
+        mid0 = int(bin_img1.shape[0]/2)
+        mid1 = int(bin_img1.shape[1]/2) 
+
+        win_len0 = int(window_size * bin_img1.shape[0])
+        win_len1 = int(window_size * bin_img1.shape[1])
+
+        def dense_bench():
+            win1 = bin_img1[mid0 - win_len0:mid0 + win_len0, mid1 - win_len1:mid1 + win_len1]
+            win2 = bin_img2[mid0 - win_len0:mid0 + win_len0, mid1 - win_len1:mid1 + win_len1]
+            xor_img = np.logical_xor(win1, win2).astype('int')
+            return xor_img
+
+        tacoBench(dense_bench)
+            
 if __name__=="__main__":
     main()
 

numpy/util.py

@@ -311,32 +311,6 @@ def load_image(image_folder, num):
     img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
     return img
 
-# plot_image plots the given original, binned, xor, and sparse xor images
-# for the numpy/image.py. Used for debugging only with the --plot flag
-def plot_image(img, img1, img2, xor_img, sparse_xor_img, t1, t2, window=None):
-    f, ax = plt.subplots(2, 3)
-    ax[0, 0].imshow(img1, 'gray')
-    ax[0, 0].title.set_text("Binned Image 1. t1 = " + str(t1))
-
-    ax[0, 1].imshow(img2, 'gray')
-    ax[0, 1].title.set_text("Binned Image 2. t2 = " + str(t2))
-
-    ax[1, 0].imshow(img, 'gray')
-    ax[1, 0].title.set_text("Saturdated Image")
-
-    ax[1, 1].imshow(xor_img, 'gray')
-    ax[1, 1].title.set_text("XOR Image")
-
-    ax[1, 2].imshow(sparse_xor_img, 'gray')
-    ax[1, 2].title.set_text("Sparse XOR Image")
-
-    if window is not None:
-        ax[0, 2].imshow(window, 'gray')
-        ax[0, 2].title.set_text("Fused Window Image")
-
-    f.tight_layout()
-    plt.show()
-
 # thresh thresholdes the given image by a threshold
 def thresh(images, t=85):
     if len(images.shape) < 3: