add code sample for "Processing RAW Images in Python"

Author: paroj

example.py

@@ -0,0 +1,66 @@
+# Code Sample for "Processing RAW Images in Python", Pavel Rojtberg, 2015
+
+import libraw              # for loading
+import numpy as np         # for processing
+
+proc = libraw.LibRaw()     # create RAW processor
+proc.open_file("file.dng") # open file
+proc.unpack()              # extract mosaic from file
+mosaic = proc.imgdata.rawdata.raw_image
+
+## Listing 1: Mapping to Linear Values
+linear = range(2**16)              # a linear LUT
+lin_lut = proc.imgdata.color.curve # linearization LUT
+
+if any(lin_lut != linear):
+    mosaic = lin_lut[mosaic]       # apply LUT
+
+black = mosaic.min()#proc.imgdata.color.black
+saturation = proc.imgdata.color.maximum
+
+uint14_max = 2**14 - 1
+mosaic -= black                    # black subtraction
+mosaic *= int(uint14_max/(saturation - black))
+mosaic = np.clip(mosaic,0,uint14_max)  # clip to range
+
+## Listing 2: White Balancing
+assert(proc.imgdata.idata.cdesc == b"RGBG")
+
+cam_mul = proc.imgdata.color.cam_mul # RGB multipliers
+cam_mul /= cam_mul[1]                # scale green to 1
+mosaic[0::2, 0::2] *= cam_mul[0]     # scale reds
+mosaic[1::2, 1::2] *= cam_mul[2]     # scale blues
+mosaic = np.clip(mosaic,0,uint14_max)# clip to range
+
+## Listing 3: Demosaicing
+def demosaic(m):
+    r = m[0::2, 0::2]
+    g = np.clip(m[0::2, 1::2]//2 + m[1::2, 0::2]//2,
+                0, 2 ** 16 - 1)
+    b = m[1::2, 1::2]
+    return np.dstack([r, g, b])
+
+mosaic *= 2**2        # expand to 16bit for demosaicing
+img = demosaic(mosaic)          # displayable rgb image
+
+## Listing 4: Color Space Conversion
+cam2srgb = proc.imgdata.color.rgb_cam[:, 0:3]
+cam2srgb = np.round(cam2srgb*255).astype(np.int16)
+img = img // 2**8        # reduce dynamic range to 8bpp      
+shape = img.shape
+pixels = img.reshape(-1, 3).T     # 3xN array of pixels
+pixels = cam2srgb.dot(pixels)//255
+img = pixels.T.reshape(shape)
+img = np.clip(img, 0, 255).astype(np.uint8)
+
+## Listing 5: Gamma Correction
+gcurve = [(i / 255) ** (1 / 2.2) * 255 for i in range(256)]
+gcurve = np.array(gcurve, dtype=np.uint8)
+
+img = gcurve[img]  # apply gamma LUT
+
+## show info and save output
+print("libraw version:", libraw.version())
+print("white balance multipliers", cam_mul[:-1])
+import matplotlib.image
+matplotlib.image.imsave("out.png", img)