/
example.py
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/
example.py
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from evaluation import Evaluator
import numpy as np
from PIL import Image
import os
import matplotlib.pyplot as plt
palette = [255, 255, 255, 231, 10, 156, 231, 60, 26, 231, 129, 69, 231, 217, 30, 231, 30, 74, 231, 79, 200, 231, 148, 243, 231, 237, 204, 231, 49, 247, 68, 164, 255, 67, 252, 216, 67, 65, 3, 68, 115, 129, 68, 183, 172, 68, 116, 24, 68, 185, 68, 68, 234, 193, 68, 47, 237, 68, 135, 198, 73, 28, 217, 73, 97, 4, 73, 185, 221, 73, 254, 9, 73, 47, 134, 73, 116, 178, 73, 48, 30, 73, 117, 73, 73, 167, 199, 73, 236, 243, 72, 134, 200, 72, 184, 70, 72, 252, 113, 72, 85, 74, 72, 154, 118, 72, 203, 243, 72, 16, 31, 73, 204, 139, 73, 17, 182, 73, 67, 52, 71, 221, 9, 71, 34, 53, 71, 83, 179, 71, 152, 222, 71, 241, 183, 71, 54, 226, 71, 103, 96, 71, 172, 140, 72, 104, 248, 72, 173, 35, 71, 33, 157, 71, 121, 118, 71, 190, 162, 71, 239, 32, 71, 52, 75, 70, 141, 36, 70, 209, 79, 70, 3, 205, 70, 72, 249, 71, 4, 101, 76, 153, 120, 76, 221, 163, 76, 54, 124, 76, 123, 167, 76, 172, 37, 76, 241, 81, 76, 73, 42, 76, 142, 85, 76, 191, 211, 76, 4, 254, 75, 3, 103, 75, 52, 228, 75, 121, 16, 75, 210, 233, 75, 23, 20, 75, 72, 146, 75, 141, 189, 75, 229, 150, 75, 42, 194, 75, 91, 64, 74, 90, 168, 74, 159, 212, 74, 208, 81, 74, 21, 125, 74, 110, 86, 74, 178, 129, 74, 228, 255, 74, 41, 42, 74, 129, 3, 74, 198, 47, 74, 158, 60, 74, 246, 21, 74, 59, 64, 74, 108, 190, 74, 177, 234, 73, 9, 195, 73, 78, 238, 74, 128, 108, 73, 197, 151, 73, 29, 112, 126, 126, 200, 126, 175, 70, 126, 244, 113, 126, 77, 74, 126, 145, 118, 126, 195, 243, 126, 8, 31, 126, 96, 248, 126, 165, 35, 126, 214, 161, 125, 213, 10, 125, 26, 53, 126, 75, 179, 126, 144, 222, 125, 233, 183, 125, 45, 227, 125, 95, 96, 125, 164, 140, 125, 252, 101, 125, 65, 144, 125, 25, 157, 125, 113, 118, 125, 182, 162, 125, 231, 32, 125, 44, 75, 125, 132, 36, 125, 201, 79, 125, 251, 205, 125, 64, 249, 125, 152, 210, 130, 144, 120, 130, 213, 163, 130, 46, 124, 130, 114, 167, 130, 164, 37, 130, 233, 81, 130, 65, 42, 130, 134, 85, 130, 183, 211, 130, 252, 254, 129, 251, 103, 129, 44, 229, 129, 113, 16, 129, 201, 233, 129, 14, 20, 129, 64, 146, 129, 133, 190, 129, 221, 151, 129, 34, 194, 129, 83, 64, 129, 82, 168, 128, 151, 212, 129, 200, 81, 129, 13, 125, 128, 101, 86, 128, 170, 129, 128, 220, 255, 128, 33, 42, 128, 121, 3, 128, 190, 47, 129, 250, 207, 128, 238, 21, 128, 51, 65, 128, 100, 190, 128, 169, 234, 128, 1, 195, 128, 70, 238, 128, 120, 108, 128, 188, 151, 128, 21, 112, 128, 81, 17, 128, 149, 60, 133, 170, 27, 133, 239, 70, 133, 33, 196, 133, 102, 239, 133, 190, 200, 133, 3, 244, 133, 52, 114, 133, 121, 157, 133, 220, 153, 133, 13, 22, 133, 82, 66, 132, 70, 136, 132, 139, 179, 132, 189, 49, 132, 1, 92, 132, 90, 53, 132, 159, 97, 132, 208, 223, 132, 51, 218, 132, 120, 6, 132, 169, 131, 132, 238, 175, 131, 226, 245, 131, 39, 32, 132, 89, 158, 131, 157, 201, 131, 246, 162, 131, 59, 206, 192, 223, 252, 192, 36, 40, 192, 125, 1, 192, 193, 44, 192, 243, 170, 192, 56, 213, 192, 144, 174, 192, 213, 218, 192, 6, 87, 192, 75, 131, 197, 107, 132, 197, 156, 2, 197, 225, 45, 197, 57, 6, 197, 126, 50, 197, 175, 175, 197, 244, 219, 197, 77, 180, 197, 146, 223, 197, 195, 93, 197, 194, 197, 197, 6, 241, 197, 56, 111, 197, 125, 154, 197, 213, 115, 197, 26, 158, 197, 75, 28, 227, 227, 227, 228, 228, 228, 229, 229, 229, 230, 230, 230, 231, 231, 231, 232, 232, 232, 233, 233, 233, 234, 234, 234, 235, 235, 235, 236, 236, 236, 237, 237, 237, 238, 238, 238, 239, 239, 239, 240, 240, 240, 241, 241, 241, 242, 242, 242, 243, 243, 243, 244, 244, 244, 245, 245, 245, 246, 246, 246, 247, 247, 247, 248, 248, 248, 249, 249, 249, 250, 250, 250, 251, 251, 251, 252, 252, 252, 253, 253, 253, 254, 254, 254, 255, 255, 255]
def save_indexed_png(path, gt):
if len(np.unique(gt)) < 256:
gt = gt.astype(np.uint8)
# gt = relabel(gt).astype(np.uint8)
gt_save = Image.fromarray(gt, mode='P')
gt_save.putpalette(palette)
gt_save.save(path)
else:
# gt = relabel(gt).astype(np.uint8)
cv2.imwrite(path, gt.astype(np.uint16))
def read_indexed_png(path):
mask = Image.open(os.path.join(path))
return np.array(mask)
e = Evaluator(dimension=2, allow_overlap=True, match_method='hungarian', image_average=False)
# cell segmentations
cell_gt = read_indexed_png('./demo/cells_gt.png')
cell_pred = read_indexed_png('./demo/cells_pred.png')
e.add_example(cell_pred, cell_gt)
e.AJI()
e.SBD()
e.PQ()
e.mAP()
e.sortedAP()
# leave segmentations
e.clear()
for f in os.listdir('./demo/CVPPP_leaves'):
gt = read_indexed_png(os.path.join('./demo/CVPPP_leaves', f, 'gt.png'))
pred = read_indexed_png(os.path.join('./demo/CVPPP_leaves', f, 'pred.png'))
e.add_example(pred, gt)
scores, aps = e.sortedAP()
# aps contain the pair of (jac, ap)
aps = np.array(aps)
plt.plot(aps[:,0], aps[:,1])
plt.xlabel("Jaccard Index (IoU)")
plt.ylabel('AP (Average Precision)')
plt.show()