diff --git a/README.md b/README.md
index da4edf8..9f7ec21 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,7 @@ A list of super awesome scripts which help automate the boring tasks.
 |[raw_to_jpeg](https://github.com/adityaarakeri/super-scripts/tree/master/raw_to_jpeg) | [Sadeed](https://github.com/Sadeed)
 | [weather_in_a_city](https://github.com/adityaarakeri/super-scripts/tree/master/weather_in_a_city) | [vish1811](https://github.com/vish1811)
 | [Movie_Recommendation](https://github.com/adityaarakeri/super-scripts/tree/master/Movie_Recommendation) | [vish1811](https://github.com/vish1811)
+| [gradient_map](https://github.com/adityaarakeri/super-scripts/tree/master/gradient_map) | [vmunhoz](https://github.com/vmunhoz)
 
 ## Contribuition Guidelines
 - Make a **separate folder** for your script.
diff --git a/gradient_map/README.md b/gradient_map/README.md
new file mode 100644
index 0000000..da2eb5d
--- /dev/null
+++ b/gradient_map/README.md
@@ -0,0 +1,20 @@
+# Gradient Map
+
+Apply gradient map to an image file
+
+![](example.png)
+
+Requirements:
+*   `cv2`, `numpy` and `matplotlib`
+
+Params:
+*   `python gradiend_map.py -i filename.jpg` Input image file. (jpg or png)
+*   `python gradiend_map.py -o filename.jpg` Output file name. (default: image_mapped.jpg)
+*   `python gradiend_map.py -w` Hexadecimal color for white end of spectrum. (default: #000000)
+*   `python gradiend_map.py -b` Hexadecimal color for black end of spectrum. (default: #ffffff)
+*   `python gradiend_map.py -h` to show more info
+
+Usage:
+*   `python gradiend_map.py -i 'image.jpg'` Creates a black and white copy of the image named image_mapped.jpg
+*   `python gradiend_map.py -i 'image.jpg' -o 'something.jpg'` Creates a black and white copy of the image named something.jpg
+*   `python gradiend_map.py -i 'image.jpg' -w '#ffff00' -b '#ff0000'` Creates a image going from yellow to red named image_mapped.jpg
diff --git a/gradient_map/example.png b/gradient_map/example.png
new file mode 100644
index 0000000..4175356
Binary files /dev/null and b/gradient_map/example.png differ
diff --git a/gradient_map/gradient_map.py b/gradient_map/gradient_map.py
new file mode 100644
index 0000000..d61ba2c
--- /dev/null
+++ b/gradient_map/gradient_map.py
@@ -0,0 +1,81 @@
+import argparse
+import cv2
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+
+def apply_colormap(img_path, colors, output):
+    input_img = cv2.imread(img_path, 0)
+
+    rgb_colors = []
+    for c in colors:
+        rgb_colors.append(hex_to_rgb(c))
+
+    cmap = make_cmap(rgb_colors)
+
+    colored_image = apply_custom_colormap(input_img, cmap)
+    cv2.imwrite(output, colored_image)
+
+
+def hex_to_rgb(h):
+    h = h.lstrip('#')
+    return tuple(int(h[i:i+2], 16) for i in (0, 2, 4))
+
+
+def make_cmap(colors):
+    '''
+    Creates a custom color map with equaly spaced colors
+    colors: Tuple containing the RGB values. Must be an int from 0 to 255.
+    '''
+
+    bit_rgb = np.linspace(0, 1, 256)
+    position = np.linspace(0, 1, len(colors))
+
+    for i in range(len(colors)):
+        colors[i] = (bit_rgb[colors[i][0]],
+                     bit_rgb[colors[i][1]],
+                     bit_rgb[colors[i][2]])
+
+    colors_dict = {'red': [], 'green': [], 'blue': []}
+    for pos, color in zip(position, colors):
+        colors_dict['red'].append((pos, color[0], color[0]))
+        colors_dict['green'].append((pos, color[1], color[1]))
+        colors_dict['blue'].append((pos, color[2], color[2]))
+
+    cmap = mpl.colors.LinearSegmentedColormap('custom_colormap', colors_dict, 256)
+    return cmap
+
+
+def apply_custom_colormap(source_image, cmap):
+    assert source_image.dtype == np.uint8, 'must be np.uint8 image'
+    # Squeeze image to turn it to black and white
+    if source_image.ndim == 3:
+        source_image = source_image.squeeze(-1)
+
+    # Initialize the matplotlib color map
+    sm = plt.cm.ScalarMappable(cmap=cmap)
+
+    # Obtain linear color range
+    color_range = sm.to_rgba(np.linspace(0, 1, 256))[:, 0:3]  # color range RGBA => RGB
+    color_range = (color_range*255.0).astype(np.uint8)  # [0,1] => [0,255]
+    color_range = np.squeeze(np.dstack(
+        [color_range[:, 2], color_range[:, 1], color_range[:, 0]]), 0)  # RGB => BGR
+
+    # Apply colormap for each channel individually
+    channels = [cv2.LUT(source_image, color_range[:, i]) for i in range(3)]
+    return np.dstack(channels)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Gradient Map')
+
+    parser.add_argument('-i', '--image', help='Input image file. (jpg or png)', default='image.jpg')
+    parser.add_argument('-o', '--output', help='Output file name. (default: image_mapped.jpg)', default='image_mapped.jpg')
+    parser.add_argument('-w', '--whitecolor', help='Hexadecimal color for white. (default: #000000)', default='#000000')
+    parser.add_argument('-b', '--blackcolor', help='Hexadecimal color for black. (default: #ffffff)', default='#ffffff')
+
+    args = vars(parser.parse_args())
+
+    apply_colormap(args['image'], [args['blackcolor'], args['whitecolor']], args['output'])
+    print('Color map applied: {}'.format(args['output']))