+"""Image filters module.
+
+All filters return a filtered copy of the image, the original image is
+preserved.
+"""
+
from PIL import Image, ImageFilter
import pcf
+def color_enhance(image):
+ """Stretch all color channels to their full range."""
+ image_l = image.load()
+ min_r, min_g, min_b = 999, 999, 999
+ max_r, max_g, max_b = -1, -1, -1
+
+ for x in xrange(image.size[0]):
+ for y in xrange(image.size[1]):
+ min_r = min(min_r, image_l[x, y][0])
+ max_r = max(max_r, image_l[x, y][0])
+ min_g = min(min_g, image_l[x, y][1])
+ max_g = max(max_g, image_l[x, y][1])
+ min_b = min(min_b, image_l[x, y][2])
+ max_b = max(max_b, image_l[x, y][2])
+
+ new_image = Image.new('RGB', image.size)
+ new_image_l = new_image.load()
+ for x in xrange(image.size[0]):
+ for y in xrange(image.size[1]):
+ r, g, b = image_l[x, y]
+ r = (r - min_r) * 255 / (max_r - min_r)
+ g = (g - min_g) * 255 / (max_g - min_g)
+ b = (b - min_b) * 255 / (max_b - min_b)
+ new_image_l[x, y] = (r, g, b)
+ # print min_r, max_r, r, g, b
+
+ return new_image
+
def edge_detection(image):
- image = image.filter(ImageFilter.GaussianBlur())
+ """Edge detection (on BW images)."""
+ new_image = image.filter(ImageFilter.GaussianBlur())
# GaussianBlur is undocumented class, it might not work in future versions
# of PIL
- image = Image.fromstring('L', image.size,
+ new_image = Image.fromstring('L', image.size,
pcf.edge(image.size, image.tostring()))
- return image
+ return new_image
def peaks(image):
+ """Peak filter (on BW images)."""
image_l = image.load()
new_image = Image.new('L', image.size)
new_image_l = new_image.load()
return new_image
def high_pass(image, height):
+ """High pass filter (on BW images)."""
image_l = image.load()
new_image = Image.new('L', image.size)
new_image_l = new_image.load()
return new_image
+# TODO factor these into one method
+# TODO comment it
def components(image):
image_l = image.load()
new_image = Image.new('L', image.size)
c += 1
new_image_l[int(round(float(x_c)/c)), int(round(float(y_c)/c))] = 255
-
return new_image
def components2(image):