Initial add of EDS module

nafuma/eds/__init__.py

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+from . import io, plot

nafuma/eds/io.py

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+from PIL import Image
+import numpy as np
+import cv2
+
+def read_image(path, weight=None, colour=None, resize=None):
+
+    img = np.array(Image.open(path))
+
+    if colour is not None:
+        img = change_colour(img, colour)
+
+    if resize is not None:
+        img = resize_image(img, resize)
+
+    if weight is not None:
+        img = scale_image(img, weight)
+
+    return img
+
+
+def scale_image(image, factor):
+
+
+    for i in range(0,image.shape[0]):
+        for j in range(0, image.shape[1]):
+            image[i][j][0] = image[i][j][0]*factor
+            image[i][j][1] = image[i][j][1]*factor
+            image[i][j][2] = image[i][j][2]*factor
+
+    return image
+
+
+def resize_image(image, factor):
+
+    y, x = image.shape[0:2]
+
+    new_y, new_x = int(y*factor), int(x*factor)
+
+    image = image[:new_y, :new_x]
+
+    res = cv2.resize(image, dsize=(x, y), interpolation=cv2.INTER_CUBIC)
+
+    return res
+
+
+def add_images(image1, image2):
+
+    assert image1.shape == image2.shape
+
+    compound_image = np.zeros((image1.shape[0], image1.shape[1], image1.shape[2]))
+    for i in range(image1.shape[0]):
+        for j in range(image1.shape[1]):
+            compound_image[i][j] = [0, 0, 0]
+
+            compound_image[i][j][0] = int(int(image1[i][j][0]) + int(image2[i][j][0]))
+            compound_image[i][j][1] = int(int(image1[i][j][1]) + int(image2[i][j][1]))
+            compound_image[i][j][2] = int(int(image1[i][j][2]) + int(image2[i][j][2]))
+
+
+
+    return compound_image
+
+
+
+
+def get_colour(image):
+
+
+    colour = [0, 0, 0]
+    for i in range(image.shape[0]):
+        for j in range(image.shape[1]):
+            if image[i][j][0] > colour[0]:
+                colour[0] = image[i][j][0]
+            
+            if image[i][j][1] > colour[1]:
+                colour[1] = image[i][j][1]
+            
+            if image[i][j][2] > colour[2]:
+                colour[2] = image[i][j][2]
+
+    colour = np.array(colour)
+
+    return colour
+            
+
+def change_colour(image, new_colour):
+
+    new_colour = np.array(new_colour)
+
+    old_colour = get_colour(image)
+
+
+    for i in range(image.shape[0]):
+        for j in range(image.shape[1]):
+            factor = max(image[i][j]) / max(old_colour)
+            image[i][j] = new_colour.astype(float) * factor
+
+
+    return image

nafuma/eds/plot.py

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+import nafuma.auxillary as aux
+import nafuma.plotting as btp
+import nafuma.eds.io as io
+
+import numpy as np
+
+def show_image(data, options={}):
+
+
+    default_options = {
+        'hide_x_labels': True,
+        'hide_y_labels': True,
+        'hide_x_ticklabels': True,
+        'hide_y_ticklabels': True,
+        'hide_x_ticks': True,
+        'hide_y_ticks': True,
+        'colours': None,
+        'show_image': True
+    }
+    
+    options = aux.update_options(options=options, required_options=default_options.keys(), default_options=default_options)
+
+    
+
+    if not isinstance(data['path'], list):
+        data['path'] = [data['path']]
+
+
+    if not 'image' in data.keys():
+
+        data['image'] = [None for _ in range(len(data['path']))]
+
+        if not 'weights' in data.keys():
+            data['weights'] = [1.0 for _ in range(len(data['path']))]
+
+        if not options['colours']:
+            options['colours'] = [None for _ in range(len(data['path']))]
+    
+        for i, (path, weight, colour) in enumerate(zip(data['path'], data['weights'], options['colours'])):
+            data['image'][i] = io.read_image(path=path, weight=weight, colour=colour, resize=options['resize'])
+
+    
+    images = []
+    for i, image in enumerate(data['image']):
+        images.append(image)
+#
+    final_image = np.mean(images, axis=0) / 255
+    if len(data['path']) > 1:
+        data['image'].append(final_image)
+
+    if options['show_image']:
+        fig, ax = btp.prepare_plot(options)
+        ax.imshow(final_image)
+        btp.adjust_plot(fig=fig, ax=ax, options=options)
+
+        return data['image'], fig, ax
+    
+    else:
+        return data['image'], None, None
+