connectivity

Author: yxdragon

imagepy/ipyalg/graph/connect.py

@@ -0,0 +1,51 @@
+import numpy as np
+from numba import jit
+from scipy.ndimage import generate_binary_structure
+
+def neighbors(shape, conn=1):
+    dim = len(shape)
+    block = generate_binary_structure(dim, conn)
+    block[tuple([1]*dim)] = 0
+    idx = np.where(block>0)
+    idx = np.array(idx, dtype=np.uint8).T
+    idx = np.array(idx-[1]*dim)
+    acc = np.cumprod((1,)+shape[::-1][:-1])
+    return np.dot(idx, acc[::-1])
+
+@jit(nopython=True)
+def search(img, nbs):
+    s, line = 0, img.ravel()
+    rst = np.zeros((len(line),2), img.dtype)
+    for i in range(len(line)):
+        if line[i]==0:continue
+        for d in nbs:
+            if line[i+d]==0: continue
+            if line[i]==line[i+d]: continue
+            rst[s,0] = line[i]
+            rst[s,1] = line[i+d]
+            s += 1
+    return rst[:s]
+
+def connect(img, conn=1):
+    buf = np.pad(img, 1, 'constant')
+    nbs = neighbors(buf.shape, conn)
+    rst = search(buf, nbs)
+    if len(rst)==0: return rst
+    rst.sort()
+    return np.unique(rst, axis=0)
+
+def mapidx(idx):
+    dic = {}
+    for i in np.unique(idx): dic[i] = []
+    for i,j in idx:
+        dic[i].append(j)
+        dic[j].append(i)
+    return dic
+
+if __name__ == '__main__':
+    img = np.array([[1,1,1,1,1],
+                    [1,1,2,2,1],
+                    [1,3,0,0,1],
+                    [1,3,1,1,4]])
+    rst = connect(img, 2)
+    print(rst)

imagepy/menus/Analysis/Region Analysis/__init__.py

@@ -1,2 +1,2 @@
 #
-catlog = ['regionprops_plgs', '-', 'statistic_plgs']
+catlog = ['regionprops_plgs', 'connect_plg', '-', 'statistic_plgs']

imagepy/menus/Analysis/Region Analysis/connect_plg.py

@@ -0,0 +1,51 @@
+from imagepy import IPy
+import numpy as np
+from imagepy.core.engine import Simple
+from skimage.measure import regionprops
+from imagepy.core.mark import GeometryMark
+from scipy.ndimage import label, generate_binary_structure
+from imagepy.ipyalg.graph.connect import connect, mapidx
+import pandas as pd
+
+# center, area, l, extent, cov
+class Plugin(Simple):
+    title = 'Connective Analysis'
+    note = ['8-bit', '16-bit', 'int']
+    para = {'con':'8-connect', 'labled':False, 'slice':False}
+    view = [(list, 'con', ['4-connect', '8-connect'], str, 'conection', 'pix'),
+            (bool, 'labled', 'it is a label image'),
+            (bool, 'slice', 'slice')]
+
+    #process
+    def run(self, ips, imgs, para = None):
+        if not para['slice']:imgs = [ips.img]
+        k = ips.unit[0]
+
+        titles = ['Slice', 'ID'][0 if para['slice'] else 1:] + ['Center-X','Center-Y', 'N', 'Neighbors']
+        buf = imgs[0].astype(np.uint32)
+        data, mark = [], {'type':'layers', 'body':{}}
+        for i in range(len(imgs)):
+            if para['labled']: buf = imgs[i]
+            else: label(imgs[i], generate_binary_structure(2, 1), output=buf)
+            conarr = connect(buf, 1 if para['con']=='4-connect' else 2)
+            conmap = mapidx(conarr)
+
+            ls = regionprops(buf)
+            dt = [[i]*len(ls), list(range(1,1+len(ls)))]
+            
+            if not para['slice']:dt = dt[1:]
+
+            layer = {'type':'layer', 'body':[]}
+            texts = [(i.centroid[::-1])+('id=%d'%i.label,) for i in ls]
+            lines = [(ls[i-1].centroid[::-1], ls[j-1].centroid[::-1]) for i,j in conarr]
+            layer['body'].append({'type':'texts', 'body':texts})
+            layer['body'].append({'type':'lines', 'body':lines})
+            mark['body'][i] = layer
+
+            dt.append([round(i.centroid[1]*k,1) for i in ls])
+            dt.append([round(i.centroid[0]*k,1) for i in ls])
+            neibs = [conmap[i.label] if i.label in conmap else [] for i in ls]
+            dt.extend([[len(i) for i in neibs], neibs])
+            data.extend(list(zip(*dt)))
+        ips.mark = GeometryMark(mark)
+        IPy.show_table(pd.DataFrame(data, columns=titles), ips.title+'-region')