--- a
+++ b/FastRCNN/utils/postProcessing.py
@@ -0,0 +1,118 @@
+from .imageUtils import cropImage
+from skimage import exposure, morphology, measure, draw
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+
+
+def watershed_image(img):
+    """
+    use watershed flooding algorithm to extract the loop contour
+    :param img: type(numpy.ndarray) image in CHW format
+    :return: type(numpy.ndarray) image in HW format
+    """
+    img_gray = img[1,:,:]
+    h, w = img_gray.shape
+    img1 = exposure.equalize_hist(img_gray)
+    # invert the image
+    img2 = np.max(img1) - img1
+    inner = np.zeros((h, w), np.bool)
+    centroid = [round(a) for a in findCentroid(img2)]
+    inner[centroid[0], centroid[1]] = 1
+    min_size = round((h + w) / 20)
+    kernel = morphology.disk(min_size)
+    inner = morphology.dilation(inner, kernel)
+
+    out = np.zeros((h,w), np.bool)
+    out[0, 0] = 1
+    out[h - 1, 0] = 1
+    out[0, w - 1] = 1
+    out[h - 1, w - 1] = 1
+    out = morphology.dilation(out, kernel)
+    out[0, :] = 1
+    out[h - 1, :] = 1
+    out[:, w - 1] = 1
+    out[:, 0] = 1
+
+    markers = np.zeros((h, w), np.int)
+    markers[inner] = 2
+    markers[out] = 1
+
+    labels = morphology.watershed(img2, markers)
+
+    return labels
+
+def findCentroid(img):
+    """
+    find the centroid position of a image by weighted method
+    :param img: (numpy.ndarray) image in HW format
+    :return: (tuple) (y,x) coordinates of the centroid
+    """
+    h, w = img.shape
+    #  add weighted method later
+    return h/2, w/2
+
+
+def flood_fitting(img):
+    """
+    Use watershed flooding algorithm and regional property analysis
+    to output the fitted ellipse parameters
+    :param img: (numpy.ndarray) image in CHW format
+    :return: region property, where property can be accessed through attributes
+            example:
+            area, bbox, centroid, major_axis_length, minor_axis_length, orientation
+    """
+    labels = watershed_image(img)
+    results = measure.regionprops(labels - 1)
+    sorted(results, key=lambda k: k['area'],reverse=True)
+    # return the one with largest area
+    return results[0]
+
+def show_fitted_ellipse(img):
+    """
+    Show fitted ellipse on the image
+    :param img: img in CHW format
+    :return: plot ellipse on top of the image
+    """
+    region1 = flood_fitting(img)
+    rr, cc = draw.ellipse_perimeter(int(region1['centroid'][0]), int(region1['centroid'][1]),
+                                    int(region1['minor_axis_length'] / 2),
+                                    int(region1['major_axis_length'] / 2), -region1['orientation'], img.shape[1:])
+    plt.imshow(img[1,:,:], cmap='gray')
+    plt.plot(cc, rr, '.')
+
+
+def img_ellipse_fitting(img, bboxes):
+    subimages, bboxes = cropImage(img, bboxes)
+    y_points = np.array([])
+    x_points = np.array([])
+    for subim, bbox in zip(subimages, bboxes):
+        region1 = flood_fitting(subim)
+        result = (int(region1['centroid'][0]+bbox[0]), int(region1['centroid'][1]+bbox[1]),
+                  int(region1['minor_axis_length'] / 2), int(region1['major_axis_length'] / 2),
+                  -region1['orientation'])
+        rr,cc = draw.ellipse_perimeter(*result)
+        y_points = np.concatenate((y_points,rr))
+        x_points = np.concatenate((x_points,cc))
+    fig = plt.figure(figsize=(10,10))
+    plt.imshow(img[0,:,:], cmap='gray')
+    plt.scatter(x_points,y_points,s=(1*72./fig.dpi)**2,alpha=0.5)
+
+def img_ellipse_fitting_area(img, bboxes):
+    subimages, bboxes = cropImage(img, bboxes)
+    ellipse_info_list = list()
+    for subim, bbox in zip(subimages, bboxes):
+        region1 = flood_fitting(subim)
+        result = (int(region1['centroid'][0]+bbox[0]), int(region1['centroid'][1]+bbox[1]),
+                  int(region1['minor_axis_length'] / 2), int(region1['major_axis_length'] / 2),
+                  -region1['orientation'])
+        ellipse_info_list.append(result)
+    area = list()
+    for item in enumerate(ellipse_info_list):
+        area.append(math.pi * item[1][1] * item[1][2])
+    plt.hist(area, bins=50)
+    plt.xlabel('Area of ellipse')
+    plt.ylabel('Frequency')
+    plt.title(r'$\mathrm{Distribution\  of\  Ellipse\ Area}$')
+    fig = plt.figure(figsize=(10, 10))
+    plt.show()
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