In [1]:

```
%matplotlib notebook
from matplotlib import pyplot as plt
import numpy as np
```

In [2]:

```
def Panoptic_quality(ground_truth_image,predicted_image):
TP = 0
FP = 0
FN = 0
sum_IOU = 0
matched_instances = {}# Create a dictionary to save ground truth indices in keys and predicted matched instances as velues
# It will also save IOU of the matched instance in [indx][1]
# Find matched instances and save it in a dictionary
for i in np.unique(ground_truth_image):
if i == 0:
pass
else:
temp_image = np.array(ground_truth_image)
temp_image = temp_image == i
matched_image = temp_image * predicted_image
for j in np.unique(matched_image):
if j == 0:
pass
else:
pred_temp = predicted_image == j
intersection = sum(sum(temp_image*pred_temp))
union = sum(sum(temp_image + pred_temp))
IOU = intersection/union
if IOU> 0.5:
matched_instances [i] = j, IOU
# Compute TP, FP, FN and sum of IOU of the matched instances to compute Panoptic Quality
pred_indx_list = np.unique(predicted_image)
pred_indx_list = np.array(pred_indx_list[1:])
# Loop on ground truth instances
for indx in np.unique(ground_truth_image):
if indx == 0:
pass
else:
if indx in matched_instances.keys():
pred_indx_list = np.delete(pred_indx_list, np.argwhere(pred_indx_list == [indx][0]))
TP = TP+1
sum_IOU = sum_IOU+matched_instances[indx][1]
else:
FN = FN+1
FP = len(np.unique(pred_indx_list))
PQ = sum_IOU/(TP+0.5*FP+0.5*FN)
return PQ
```

In [3]:

```
def Panoptic_quality_correction(ground_truth_image,predicted_image):
TP = 0
FP = 0
FN = 0
sum_IOU = 0
matched_instances = {}# Create a dictionary to save ground truth indices in keys and predicted matched instances as velues
# It will also save IOU of the matched instance in [indx][1]
# Find matched instances and save it in a dictionary
for i in np.unique(ground_truth_image):
if i == 0:
pass
else:
temp_image = np.array(ground_truth_image)
temp_image = temp_image == i
matched_image = temp_image * predicted_image
for j in np.unique(matched_image):
if j == 0:
pass
else:
pred_temp = predicted_image == j
intersection = sum(sum(temp_image*pred_temp))
union = sum(sum(temp_image + pred_temp))
IOU = intersection/union
if IOU> 0.5:
matched_instances [i] = j, IOU
# Compute TP, FP, FN and sum of IOU of the matched instances to compute Panoptic Quality
pred_indx_list = np.unique(predicted_image)
pred_indx_list = np.array(pred_indx_list[1:])
# Loop on ground truth instances
for indx in np.unique(ground_truth_image):
if indx == 0:
pass
else:
if indx in matched_instances.keys():
pred_indx_list = np.delete(pred_indx_list, np.argwhere(pred_indx_list == matched_instances[indx][0]))
TP = TP+1
sum_IOU = sum_IOU+matched_instances[indx][1]
else:
FN = FN+1
FP = len(np.unique(pred_indx_list))
PQ = sum_IOU/(TP+0.5*FP+0.5*FN)
return PQ
```

In [4]:

```
gt = np.zeros((100,100))
pred = np.zeros((100,100))
gt[10:20,10:20] = 1
gt[10:20,30:40] = 2
gt[50:60,50:60] = 3
pred[10:20,10:20] = 4
pred[10:20,30:40] = 5
pred[50:60,50:60] = 6
PQ = Panoptic_quality(gt, pred)
print(PQ)
PQ = Panoptic_quality_correction(gt, pred)
print(PQ)
```

0.6666666666666666 1.0