I have a label_image array and I am deriving the outlines/boundaries of the objects on that array. Currently I am doing that by getting all unique labels, iterating over them and then find the contours of each object. Like in the loop below, where I am populating a dict with keys the label and values the contours
import cv2
import pandas as pd
import numpy as np
def extract_borders(label_image):
labels = np.unique(label_image[label_image > 0])
d = {}
for label in labels:
y = label_image == label
y = y * 255
y = y.astype('uint8')
contours, hierarchy = cv2.findContours(y, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = np.squeeze(contours)
d[label] = contours.tolist()
df = pd.DataFrame([d]).T
df = df.reset_index()
df.columns = ['label', 'coords']
return df
if __name__ == "__main__":
label_img = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 0, 0, 0],
[0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 0, 0, 0],
[0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 4, 4, 0, 0, 0],
[0, 0, 0, 2, 2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 4, 4, 0, 0, 0],
[0, 0, 0, 2, 2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 4, 4, 0, 0, 0],
[0, 0, 0, 2, 2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 4, 4, 0, 0, 0],
[0, 0, 0, 2, 2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 4, 4, 0, 0, 0],
[0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 4, 4, 0, 0, 0],
[0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 0, 0, 0],
[0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
])
res = extract_borders(label_img)
print(res)
When labels are thousands this can be a real bottleneck. Is there a more efficient way to do this please? Maybe there is a function I am not aware of... I want to be able to assign the label to the corresponding contours.
The code above prints:
label coords
0 1 [[5, 6], [5, 9], [9, 9], [9, 6]]
1 2 [[3, 3], [3, 12], [11, 12], [11, 10], [5, 10],...
2 3 [[12, 5], [11, 6], [10, 6], [10, 9], [11, 9], ...
3 4 [[12, 3], [12, 4], [14, 4], [15, 5], [15, 10],...
The DIPlib library has a function to extract the chain code for each object in the image. It does require, however, that each object is connected (the pixels with the same label must form a connected component). Using Mark's large example image, this takes computation time from 154.8s to 0.781s, 200 times faster. And most of that time, I think, is dedicated to converting the chain code into a polygon, into a numpy array, into a list, and finally into a pandas table. Lots of conversions...
One thing to note: the chain codes returned by dip.GetImageChainCodes are as you'd expect: they trace the outer pixels of each object. However, converting these to a polygon does something different: the polygon doesn't link the outer pixels, but goes around them, following the "crack" between the pixels. And it cuts pixel corners doing so. This leads to a polygon that much better describes the actual object, its area is exactly half a pixel smaller than the number of pixels in the object, and its length is much closer to the perimeter of the underlying object (before discretizing it into a set of pixels). This idea comes from Steve Eddins at the MathWorks.
import pandas as pd
import numpy as np
import diplib as dip
import cv2
import time
def extract_borders(label_image):
labels = np.unique(label_image[label_image > 0])
d = {}
for label in labels:
y = label_image == label
y = y * 255
y = y.astype('uint8')
contours, hierarchy = cv2.findContours(y, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = np.squeeze(contours)
d[label] = contours.tolist()
df = pd.DataFrame([d]).T
df = df.reset_index()
df.columns = ['label', 'coords']
return df
def extract_borders_dip(label_image):
cc = dip.GetImageChainCodes(label_img) # input must be an unsigned integer type
d = {}
for c in cc:
d[c.objectID] = np.array(c.Polygon()).tolist()
df = pd.DataFrame([d]).T
df = df.reset_index()
df.columns = ['label', 'coords']
return df
if __name__ == "__main__":
label_img = np.arange(2500, dtype=np.uint16).reshape((50,50))
label_img = cv2.resize(label_img, (4000,4000), interpolation=cv2.INTER_NEAREST)
start = time.process_time()
res = extract_borders(label_img)
print('OP code:', time.process_time() - start)
print(res)
start = time.process_time()
res = extract_borders_dip(label_img)
print('DIPlib code: ', time.process_time() - start)
print(res)