Adding blur, hist, iir_blur benchmarks for numpy & dace

npbench/benchmarks/image_processing/blur/blur.py

@@ -5,7 +5,7 @@
 from pathlib import Path
 
 def initialize():
-    image_path = Path(__file__).parent.parent / "images" / "gray.png"
+    image_path = Path(__file__).parent.parent.parent.parent.parent / "data/image_processing" / "gray.png"
     image = np.array(Image.open(image_path), dtype=np.uint8)
     output = np.empty((image.shape[0] - 2, image.shape[1] - 2), dtype=np.uint8)
 

npbench/benchmarks/image_processing/blur/blur_dace.py

@@ -18,5 +18,3 @@ def blur(image: dc.uint8[2560, 1536], output: dc.uint8[2558, 1534]):
     blur_y[blur_y < 0.0] = 0.0
     blur_y[blur_y > 255.0] = 255.0
     output = blur_y.astype(np.uint8)
-
-    return output

npbench/benchmarks/image_processing/blur/blur_numpy.py

@@ -19,5 +19,3 @@ def blur(image, output):
 
     # img = Image.fromarray(output)
     # img.save("blur_output.png")
-
-    return output

npbench/benchmarks/image_processing/hist/hist.py

@@ -5,7 +5,7 @@
 from pathlib import Path
 
 def initialize():
-    image_path = Path(__file__).parent.parent / "images" / "rgb.png"
+    image_path = Path(__file__).parent.parent.parent.parent.parent / "data/image_processing" / "rgb.png"
     image = np.array(Image.open(image_path), dtype=np.uint8)
     output = np.empty((image.shape[0], image.shape[1], 3), dtype=np.uint8)
 

npbench/benchmarks/image_processing/hist/hist_dace.py

@@ -1,13 +1,12 @@
+from mimetypes import init
 import numpy as np
 import dace
 
 @dace.program
 def hist(image: dace.uint8[2560, 1536, 3], output: dace.uint8[2560, 1536, 3]):
     imagef = image.astype(np.float32)
-
-    gray = np.empty((imagef.shape[0], imagef.shape[1]), dtype=np.float32)
-    gray[:,:] = 0.299 * imagef[:,:,0] + 0.587 * imagef[:,:,1] + 0.114 * imagef[:,:,2]
-
+    gray = 0.299 * imagef[:,:,0] + 0.587 * imagef[:,:,1] + 0.114 * imagef[:,:,2]
+
     Cr = (imagef[:,:,0] - gray) * 0.713 + 128
     Cb = (imagef[:,:,2] - gray) * 0.564 + 128
 
@@ -16,43 +15,45 @@ def hist(image: dace.uint8[2560, 1536, 3], output: dace.uint8[2560, 1536, 3]):
     binned_gray = gray.astype(np.uint8)
 
     # histogram
-    hist = np.ndarray((256,), dtype=np.float32)
-    for y, x in dace.map[0:binned_gray.shape[0], 0:binned_gray.shape[1]]:
+    hist = np.zeros((256,), dtype=np.uint32)
+    for y, x in dace.map[0:2560, 0:1536]:
         with dace.tasklet:
             p << binned_gray[y, x]
             out >> hist(1, lambda x, y: x + y)[:]
 
             out[p] = 1
 
     npixels = gray.shape[0] * gray.shape[1]
-    density = hist / npixels
+    density = np.ndarray((256,), dtype=np.float32)
+    density[:] = hist / npixels
 
     # cumsum    
     cdf = np.ndarray((256,), dtype=np.float32)
-    sum = 0
+    sum: dace.float32 = 0.0
     for i in range(256):
         sum = sum + density[i]
         cdf[i] = sum
 
-    eq = cdf[binned_gray]
+    eq = np.ndarray((2560, 1536), dtype=np.float32)
+    for y, x in dace.map[0:2560, 0:1536]:
+        eq[y, x] = cdf[binned_gray[y, x]]
+
     eq = eq * 255.0
     eq[eq < 0] = 0
     eq[eq > 255] = 255
 
-    red = eq + (Cr - 128) * 1.4
-    red[red < 0] = 0
-    red[red > 255] = 255
+    red = eq + (Cr - 128.0) * 1.4
+    red[red < 0.0] = 0.0
+    red[red > 255.0] = 255.0
 
-    green = eq - 0.343 * (Cb - 128) - 0.711 * (Cr - 128)
-    green[green < 0] = 0
-    green[green > 255] = 255
+    green = eq - 0.343 * (Cb - 128.0) - 0.711 * (Cr - 128.0)
+    green[green < 0.0] = 0.0
+    green[green > 255.0] = 255.0
 
-    blue = eq + 1.765 * (Cb - 128)
-    blue[blue < 0] = 0
-    blue[blue > 255] = 255
+    blue = eq + 1.765 * (Cb - 128.0)
+    blue[blue < 0.0] = 0.0
+    blue[blue > 255.0] = 255.0
 
     output[:,:,0] = red.astype(np.uint8)
     output[:,:,1] = green.astype(np.uint8)
     output[:,:,2] = blue.astype(np.uint8)
-
-    return output

npbench/benchmarks/image_processing/hist/hist_numpy.py

@@ -4,28 +4,38 @@
 
 def hist(image, output):
     image = image.astype(np.float32)
-
-    gray = np.empty(image.shape[:2], dtype=np.float32)
-    gray[:,:] = 0.299 * image[:,:,0] + 0.587 * image[:,:,1] + 0.114 * image[:,:,2]
+    gray = 0.299 * image[:,:,0] + 0.587 * image[:,:,1] + 0.114 * image[:,:,2]
 
     Cr = (image[:,:,0] - gray) * 0.713 + 128
     Cb = (image[:,:,2] - gray) * 0.564 + 128
 
     binned_gray = np.clip(gray, 0, 255).astype(np.uint8)
 
     # [0, ..., 256] where 256 is the rightmost edge
-    hist, bins = np.histogram(binned_gray, bins=np.arange(257), density=True)
-    cdf = np.cumsum(hist)
+    hist, _ = np.histogram(binned_gray, bins=np.arange(257))
+
+    npixels = gray.shape[0] * gray.shape[1]
+    density = np.ndarray((256,), dtype=np.float32)
+    density[:] = hist / npixels
+
+    cdf = np.cumsum(density)
 
     eq = cdf[binned_gray]
     eq = eq * 255.0
     eq = np.clip(eq, 0, 255)
 
-    output[:,:,0] = np.clip(eq + (Cr - 128) * 1.4, 0, 255).astype(np.uint8)
-    output[:,:,1] = np.clip(eq - 0.343 * (Cb - 128) - 0.711 * (Cr - 128), 0, 255).astype(np.uint8)
-    output[:,:,2] = np.clip(eq + 1.765 * (Cb - 128), 0, 255).astype(np.uint8)
+    red = eq + (Cr - 128.0) * 1.4
+    red = np.clip(red, 0, 255)
+
+    green = eq - 0.343 * (Cb - 128.0) - 0.711 * (Cr - 128.0)
+    green = np.clip(green, 0, 255)
+
+    blue = eq + 1.765 * (Cb - 128.0)
+    blue = np.clip(blue, 0, 255)
+
+    output[:,:,0] = red.astype(np.uint8)
+    output[:,:,1] = green.astype(np.uint8)
+    output[:,:,2] = blue.astype(np.uint8)
 
     # img = Image.fromarray(output)
     # img.save("hist_output.png")
-
-    return output

npbench/benchmarks/image_processing/iir_blur/iir_blur.py

@@ -5,7 +5,7 @@
 from pathlib import Path
 
 def initialize():
-    image_path = Path(__file__).parent.parent / "images" / "rgb.png"
+    image_path = Path(__file__).parent.parent.parent.parent.parent / "data/image_processing" / "rgb.png"
     image = np.array(Image.open(image_path), dtype=np.uint8)
     output = np.empty_like(image)
 

npbench/benchmarks/image_processing/iir_blur/iir_blur_dace.py

@@ -28,5 +28,3 @@ def iir_blur(image: dace.uint8[2560, 1536, 3], output: dace.uint8[2560, 1536, 3]
     blur[blur < 0] = 0
     blur[blur > 255] = 255
     output = blur.astype(np.uint8)
-
-    return output

npbench/benchmarks/image_processing/iir_blur/iir_blur_numpy.py

@@ -26,5 +26,3 @@ def iir_blur(image, output):
 
     # img = Image.fromarray(output)
     # img.save("iir_blur_output.png")
-
-    return output