Added colored images

Author: stephanboner

README.md

@@ -0,0 +1,188 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# Script to generate colored image data. With the input arguments one can specify
+# how many gabor patches should be on the image, he image resolution as well as the
+# number of noise patches on the image. Furthermore one can add noise to the color
+# of the patch by defining the standard deviation of color_noise. The metadata of
+# the images are saved in a file named description.csv, containing the image name
+# together with the according orientations and color values.
+# Please note that the generation of the noise patches is quite slow and if somebody
+# comes up with a more efficient method, a pull request is highly appreciated.
+
+import argparse
+
+from PIL import Image
+import numpy as np
+import pandas as pd
+from pathlib import Path
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--n_gabor_patches', type=int, default=3,
+                        help="The number of gabor patches on the image")
+    parser.add_argument('--n_noise_patches', type=int, default=24,
+                        help="The number of gaussian noise patches in the image")
+    parser.add_argument('--color_noise', type=float, default=0,
+                        help="Standard deviation of the gaussian to add noise to the gabor patches (in degrees)")
+    parser.add_argument('--image_height', type=int, default=250,
+                        help='Height of the image. The width will be n_gabor_patches time the height')
+    parser.add_argument('--output_path', type=str, default='images/',
+                        help="Location for saving the images")
+    parser.add_argument('--n_images', type=int, default=100,
+                        help="The number of images generated")
+    return parser.parse_args()
+
+
+# this method is based on this stackoverflow post
+# https://stackoverflow.com/questions/19039674/how-can-i-expand-this-gabor-patch-to-the-size-of-the-bounding-box
+"""
+    lambda_ : int
+        Spatial frequency (px per cycle)
+
+    theta : int or float
+        Grating orientation in degrees (0-180)
+
+    sigma : int or float
+        gaussian standard deviation (in pixels)
+
+    phase : float
+        phase offset of the grating, between 0 and 180
+
+    trim : float
+        used to cut the gaussian at some point
+        preventing it from continuing infinitely
+
+    color_trim : float
+        defines the color border, compare to trim
+
+    color_std_dev : int or float
+        if greater than 0, the given color is interpreted
+        as the mean of a gaussian function and color_std_dev
+        is the standard deviation, used to sample for every pixel
+"""
+
+
+def gabor_patch(size, color, lambda_, theta, sigma, phase, trim=.005,
+                color_trim=.005, color_std_dev=20):
+    X0 = (np.linspace(1, size, size) / size) - .5
+    freq = size / float(lambda_)
+    phaseRad = (phase / 180.) * np.pi
+    Xm, Ym = np.meshgrid(X0, X0)
+    thetaRad = (theta / 180.) * np.pi
+    Xt = Xm * np.cos(thetaRad)
+    Yt = Ym * np.sin(thetaRad)
+    grating = np.sin(((Xt + Yt) * freq * 2 * np.pi) + phaseRad)
+    gauss = np.exp(-((Xm ** 2) + (Ym ** 2)) / (2 * (sigma / float(size)) ** 2))
+    gauss[gauss < trim] = 0
+    mask = np.where(gauss < color_trim, True, False)  # where should it be cut?
+    color_pixel = color / 360 * 255
+    color_std_dev_pixel = color_std_dev / 360 * 255
+    v = (1 - (grating * gauss + 1) / 2) * 255
+    h = np.ones(v.shape) * color_pixel
+    if color_std_dev_pixel > 1e-3:
+        h = np.random.normal(h, color_std_dev_pixel)
+    s = np.clip(np.ones(v.shape) * gauss * 2, 0, 1) * 255
+    h[mask] = 0
+    s[mask] = 0
+    v[gauss == 0] = 127
+    img_data = np.transpose(np.array([h, s, v]))
+    img_data = np.uint8(img_data)
+    return Image.fromarray(img_data, "HSV").convert("RGB")
+
+
+def generate_image(orientations, colors, image_height,
+                    patch_size, color_std_dev):
+    background_color = "#7f7f7f"
+    n_gabor_patches = len(orientations)
+    img = Image.new("RGB", (image_height * n_gabor_patches, image_height), background_color)
+    lambda_ = 20
+    sigma = 30
+    for i, (o, c) in enumerate(zip(orientations, colors)):
+        phase = np.random.uniform(0, 360)
+        patch = gabor_patch(int(patch_size), c, lambda_, o, sigma, phase, color_std_dev=color_std_dev)
+        img.paste(patch, (int((image_height - patch_size) / 2 + i * image_height), int((image_height - patch_size) / 2)))   
+    return img
+
+
+def generate_random_image(image_height, patches, patch_size, color_std_dev):
+    orientations = []
+    colors = []
+    for _ in range(0, patches):
+        orientation = np.random.uniform(0, 180)
+        color = np.random.uniform(0, 360)
+        orientations.append(orientation)
+        colors.append(color)
+    img = generate_image(orientations, colors, image_height, patch_size, color_std_dev) 
+    return img, orientations, colors
+
+
+def add_noise_patch(img, diameter=50, center=(None, None)):
+    diam = round(diameter)
+    radius = round(diameter / 2)
+    img_width, img_height = img.size
+
+    center_x, center_y = center
+    if center_x is None:
+        center_x = round(np.random.uniform(0, img_width))
+    if center_y is None:
+        center_y = round(np.random.uniform(0, img_height))
+
+    h = np.random.uniform(0, 255, (diam, diam))
+    s = np.clip((np.random.normal(loc=0.5, scale=0.1, size=(diam, diam))), 0, 1) * 255
+    v = np.clip((np.random.normal(loc=0.5, scale=0.1, size=(diam, diam))), 0, 1) * 255
+
+    start_x = center_x - radius
+    start_y = center_y - radius
+
+    for x in range(0, diam):
+        for y in range(0, diam):
+            coord_x = start_x + x
+            coord_y = start_y + y
+            if coord_x > 0 and coord_x < img_width and coord_y > 0 and coord_y < img_height and (
+                    (x - radius) ** 2 + (y - radius) ** 2) < radius ** 2:
+                img.putpixel((coord_x, coord_y), (int(h[x, y]), int(s[x, y]), int(v[x, y])))
+    return img
+
+
+def add_noise_patches(img, number=5, max_diameter=70, min_diameter_scale=0.8):
+    for _ in range(0, number):
+        img = add_noise_patch(img, diameter=max_diameter * np.random.uniform(min_diameter_scale, 1))
+    return img
+
+
+def generate_noisy_image(n_gabor_patches, n_noise_patches, image_height, color_std_dev):
+    patch_size = image_height * 0.8
+    img, orientations, colors = generate_random_image(image_height, n_gabor_patches, patch_size, color_std_dev)
+    noisy_img = add_noise_patches(img.convert("HSV"), n_noise_patches, patch_size / 3).convert("RGB")
+    return noisy_img, orientations, colors
+
+
+if __name__ == '__main__':
+    FLAGS = get_args()
+    number_images = FLAGS.n_images
+    n_gabor_patches = FLAGS.n_gabor_patches
+    n_noise_patches = FLAGS.n_noise_patches
+    color_std_dev = FLAGS.color_noise
+    output_path = FLAGS.output_path
+    image_height = FLAGS.image_height
+
+    Path(output_path).mkdir(parents=True, exist_ok=True)
+
+    columns = ["image_name"]
+    for i in range(0, n_gabor_patches):
+        columns.append("orientation_%d" % i)
+    for i in range(0, n_gabor_patches):
+        columns.append("color_%d" % i)
+    df = pd.DataFrame(columns=columns)
+
+    for i in range(0, number_images):
+        img, orientations, colors = generate_noisy_image(n_gabor_patches, n_noise_patches, image_height, color_std_dev)
+        img_name = ("gabor%d_%06d.png" % (n_gabor_patches, i))
+        row = [img_name]  # just a list
+        row.extend(orientations)
+        row.extend(colors)
+        df = df.append(pd.Series(row, index=columns), ignore_index=True)
+        img.save(output_path + img_name)
+    df.to_csv(output_path + "description.csv")

c_data_generator.py

@@ -0,0 +1,51 @@
+,image_name,orientation_0,orientation_1,orientation_2,color_0,color_1,color_2
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