Upload morphsnakes .py

morphsnakes .py

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+"""
+====================
+Morphological Snakes
+====================
+
+*Morphological Snakes* [1]_ are a family of methods for image segmentation.
+Their behavior is similar to that of active contours (for example, *Geodesic
+Active Contours* [2]_ or *Active Contours without Edges* [3]_). However,
+*Morphological Snakes* use morphological operators (such as dilation or
+erosion) over a binary array instead of solving PDEs over a floating point
+array, which is the standard approach for active contours. This makes
+*Morphological Snakes* faster and numerically more stable than their
+traditional counterpart.
+
+There are two *Morphological Snakes* methods available in this implementation:
+*Morphological Geodesic Active Contours* (**MorphGAC**, implemented in the
+function ``morphological_geodesic_active_contour``) and *Morphological Active
+Contours without Edges* (**MorphACWE**, implemented in the function
+``morphological_chan_vese``).
+
+**MorphGAC** is suitable for images with visible contours, even when these
+contours might be noisy, cluttered, or partially unclear. It requires, however,
+that the image is preprocessed to highlight the contours. This can be done
+using the function ``inverse_gaussian_gradient``, although the user might want
+to define their own version. The quality of the **MorphGAC** segmentation
+depends greatly on this preprocessing step.
+
+On the contrary, **MorphACWE** works well when the pixel values of the inside
+and the outside regions of the object to segment have different averages.
+Unlike **MorphGAC**, **MorphACWE** does not require that the contours of the
+object are well defined, and it works over the original image without any
+preceding processing. This makes **MorphACWE** easier to use and tune than
+**MorphGAC**.
+
+References
+----------
+
+.. [1] A Morphological Approach to Curvature-based Evolution of Curves and
+       Surfaces, Pablo Márquez-Neila, Luis Baumela and Luis Álvarez. In IEEE
+       Transactions on Pattern Analysis and Machine Intelligence (PAMI),
+       2014, :DOI:`10.1109/TPAMI.2013.106`
+.. [2] Geodesic Active Contours, Vicent Caselles, Ron Kimmel and Guillermo
+       Sapiro. In International Journal of Computer Vision (IJCV), 1997,
+       :DOI:`10.1023/A:1007979827043`
+.. [3] Active Contours without Edges, Tony Chan and Luminita Vese. In IEEE
+       Transactions on Image Processing, 2001, :DOI:`10.1109/83.902291`
+
+"""
+import os
+
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage import data, img_as_float
+from skimage.segmentation import (morphological_chan_vese,
+                                  morphological_geodesic_active_contour,
+                                  inverse_gaussian_gradient,
+                                  checkerboard_level_set)
+from cv2 import imread
+from cv2 import imshow
+from cv2 import waitKey
+import cv2
+def store_evolution_in(lst):
+    """Returns a callback function to store the evolution of the level sets in
+    the given list.
+    """
+
+    def _store(x):
+        lst.append(np.copy(x))
+
+    return _store
+
+images = sorted(os.listdir('./mini/'))
+# print(images)
+for k, img in enumerate(images):
+# Morphological ACWE
+    image1 = imread(f'./mini/{img}', 0)
+    image = image1.copy()
+    # Initial level set
+    init_ls = checkerboard_level_set(image.shape, 6)
+    # List with intermediate results for plotting the evolution
+    evolution = []
+    callback = store_evolution_in(evolution)
+    ls = morphological_chan_vese(image, num_iter=35, init_level_set=init_ls,
+                                 smoothing=3, iter_callback=callback)
+
+    fig, axes = plt.subplots(2, 2, figsize=(8, 8))
+    ax = axes.flatten()
+
+    ax[0].imshow(image, cmap="gray")
+    ax[0].set_axis_off()
+    ax[0].contour(ls, [0.5], colors='r')
+    ax[0].set_title("Morphological ACWE segmentation", fontsize=12)
+    withe = np.ones_like(image, dtype='uint8') * 255
+
+    ret1,thresh1 = cv2.threshold(withe,70,255,0)
+    ret,thresh = cv2.threshold(image1,70,255,0)
+
+    new = np.bitwise_and(cv2.bitwise_not(ls),thresh)[140:420,70:420]
+
+    for i in range(new.shape[0]):
+        for j in range(new.shape[1]):
+            if new[i][j] > 0:
+                new[i][j] -= 130
+
+
+    cv2.imwrite(f'./output/{img}', new)
+
+
+# ax[1].imshow(ls, cmap="gray")
+# ax[1].set_axis_off()
+# contour = ax[1].contour(evolution[2], [0.5], colors='g')
+# contour.collections[0].set_label("Iteration 2")
+# contour = ax[1].contour(evolution[7], [0.5], colors='y')
+# contour.collections[0].set_label("Iteration 7")
+# contour = ax[1].contour(evolution[-1], [0.5], colors='r')
+# contour.collections[0].set_label("Iteration 35")
+# ax[1].legend(loc="upper right")
+# title = "Morphological ACWE evolution"
+# ax[1].set_title(title, fontsize=12)
+
+
+# Morphological GAC
+# image = img_as_float(data.coins())
+#gimage = inverse_gaussian_gradient(image)
+
+# Initial level set
+#init_ls = np.zeros(image.shape, dtype=np.int8)
+#init_ls[10:-10, 10:-10] = 1
+# List with intermediate results for plotting the evolution
+#evolution = []
+#callback = store_evolution_in(evolution)
+#ls = morphological_geodesic_active_contour(gimage, num_iter=230,
+ #                                          init_level_set=init_ls,
+#                                           smoothing=1, balloon=-1,
+#                                           threshold=0.69,
+#                                           iter_callback=callback)
+
+# ax[2].imshow(new, cmap="gray")
+# ax[2].set_axis_off()
+#ax[2].contour(ls, [0.5], colors='r')
+#ax[2].set_title("Morphological GAC segmentation", fontsize=12)
+
+#ax[3].imshow(ls, cmap="gray")
+#ax[3].set_axis_off()
+#contour = ax[3].contour(evolution[0], [0.5], colors='g')
+#contour.collections[0].set_label("Iteration 0")
+#contour = ax[3].contour(evolution[100], [0.5], colors='y')
+#contour.collections[0].set_label("Iteration 100")
+#contour = ax[3].contour(evolution[-1], [0.5], colors='r')
+#contour.collections[0].set_label("Iteration 230")
+#ax[3].legend(loc="upper right")
+#title = "Morphological GAC evolution"
+#ax[3].set_title(title, fontsize=12)
+
+# fig.tight_layout()
+# plt.show()