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iou.py
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iou.py
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import numpy as np
import re
import progressbar
import time
def bb_intersection_over_union(boxA, boxB):
# determine the (x, y)-coordinates of the intersection rectangle
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
# compute the area of intersection rectangle
# interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
interArea = max(0, abs(xB - xA + 1)) * max(0, abs(yB - yA + 1))
# print(interArea)
# compute the area of both the prediction and ground-truth
# rectangles
boxAArea = abs(boxA[2] - boxA[0] + 1) * abs(boxA[3] - boxA[1] + 1)
boxBArea = abs(boxB[2] - boxB[0] + 1) * abs(boxB[3] - boxB[1] + 1)
# print(boxAArea)
# print(boxBArea)
# compute the intersection over union by taking the intersection
# area and dividing it by the sum of prediction + ground-truth
# areas - the interesection area
iou = interArea / float(boxAArea + boxBArea - interArea)
# return the intersection over union value
return iou
def bb_iou(boxA, boxB):
# determine the (x, y)-coordinates of the intersection rectangle
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
# compute the area of intersection rectangle
interArea = (xB - xA) * (yB - yA)
interArea = abs(interArea)
# compute the area of both the prediction and ground-truth
# rectangles
boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
boxAArea = abs(boxAArea)
boxBArea = abs(boxBArea)
# compute the intersection over union by taking the intersection
# area and dividing it by the sum of prediction + ground-truth
# areas - the interesection area
if float(boxAArea + boxBArea - interArea) == 0:
iou = 0
else:
iou = interArea / float(boxAArea + boxBArea - interArea)
iou = abs(iou)
# return the intersection over union value
return iou
def get_iou(ground_truth, pred):
# coordinates of the area of intersection.
ix1 = np.maximum(ground_truth[0], pred[0])
iy1 = np.maximum(ground_truth[1], pred[1])
ix2 = np.minimum(ground_truth[2], pred[2])
iy2 = np.minimum(ground_truth[3], pred[3])
# Intersection height and width.
i_height = np.maximum(iy2 - iy1 + 1, np.array(0.))
print(i_height)
i_width = np.maximum(ix2 - ix1 + 1, np.array(0.))
print(i_width)
area_of_intersection = i_height * i_width
print(area_of_intersection)
# Ground Truth dimensions.
gt_height = ground_truth[3] - ground_truth[1] + 1
print(gt_height)
gt_width = ground_truth[2] - ground_truth[0] + 1
print(gt_width)
# Prediction dimensions.
pd_height = pred[3] - pred[1] + 1
print(pd_height)
pd_width = pred[2] - pred[0] + 1
print(pd_width)
area_of_union = gt_height * gt_width + pd_height * pd_width - area_of_intersection
print(area_of_union)
iou = area_of_intersection / area_of_union
print(iou)
return iou
SMOOTH = 1e-6
def iou_numpy(outputs: np.array, labels: np.array):
outputs = outputs.squeeze(1)
intersection = (outputs & labels).sum((1, 2))
union = (outputs | labels).sum((1, 2))
iou = (intersection + SMOOTH) / (union + SMOOTH)
thresholded = np.ceil(np.clip(20 * (iou - 0.5), 0, 10)) / 10
return thresholded # Or thresholded.mean()
# iterate over boubding boxes in grounth_truth.txt and filtered_results.txt
# and calculate the iou for each pair of boxes
# if the iou is greater than 0.5, the box is considered a true positive
def calculate_iou():
gt = open("gt.txt", "r")
gt_lines = len(gt.readlines())
fr = open("filtered_pd.txt", "r")
fr_lines = len(fr.readlines())
gt = open("gt.txt", "r")
fr = open("filtered_results.txt", "r")
print(gt_lines)
print(fr_lines)
true_positives = 0
false_positives = 0
for line in fr:
lin = line.split()
# print(lin)
for line in gt:
fr = line.split()
# print(fr[:-1])
# print(lin[0])
# print(fr[0])
if lin[0] == fr[0]:
print("match")
# print(lin[0])
# print(fr[0])
li = [int(lin[2]), int(lin[3]), int(lin[4]), int(lin[5])]
fi = [int(fr[2]), int(fr[3]), int(fr[4]), int(fr[5])]
iou = bb_intersection_over_union(li, fi)
print(iou)
if iou > 0.5:
true_positives += 1
print(True)
else:
false_positives += 1
print(False)
else:
# print("no match")
pass
print('True positives: ', true_positives)
print('False positives: ', false_positives)
def neo_calculate_iou():
gt = open("gt.txt", "r")
fr = open("filtered_pd.txt", "r")
gt_lines = len(gt.readlines())
fr_lines = len(fr.readlines())
print(gt_lines)
print(fr_lines)
print(gt_lines * fr_lines)
ground = []
filtered = []
true_positives = 0
false_positives = 0
for line in fr:
lin = line.split()
filtered.append(lin)
for line in gt:
fr = line.split()
ground.append(fr)
matches = 0
for i in filtered:
# print(i[0])
for j in ground:
# print(j[0])
# print(lin[0])
# print(fr[0])
if i[0] == j[0]:
# print(lin[0])
# print(fr[0])
# print("match")
matches += 1
li = [int(i[2]), int(i[3]), int(i[4]), int(i[5])]
fi = [int(j[2]), int(j[3]), int(j[4]), int(j[5])]
# print(li)
# print(fi)
iou = bb_intersection_over_union(li, fi)
if iou > 0.5:
true_positives += 1
# print(True)
else:
false_positives += 1
# print(False)
else:
# print("no match")
pass
print('True positives: ', true_positives)
print('False positives: ', false_positives)
print('Matches: ', matches)
print(true_positives + false_positives)
# print(matches)
def new_calculate_iou():
gt = open("gt.txt", "r")
fr = open("filtered_pd.txt", "r")
gt_lines = len(gt.readlines())
fr_lines = len(fr.readlines())
print(gt_lines)
print(fr_lines)
total_count = gt_lines * fr_lines
print(gt_lines * fr_lines)
gt = open("gt.txt", "r")
fr = open("filtered_pd.txt", "r")
ground = []
filtered = []
true_positives = 0
false_positives = 0
count = 0
for line in fr:
lin = line.split()
filtered.append(lin)
for line in gt:
fr = line.split()
ground.append(fr)
matches = 0
widgets = [' [',
progressbar.Timer(format= 'elapsed time: %(elapsed)s'),
'] ',
progressbar.Bar('/'),' (',
progressbar.ETA(), ') ',
]
bar = progressbar.ProgressBar(max_value=543150,
widgets=widgets).start()
for i in filtered:
# print(i[0])
count += 1
for j in ground:
count += 1
if re.search('|'.join(j), i[0]):
# print("match")
matches += 1
bar.update(count)
li = [int(i[2]), int(i[3]), int(i[4]), int(i[5])]
fi = [int(j[2]), int(j[3]), int(j[4]), int(j[5])]
# print(li)
# print(fi)
iou = bb_iou(li, fi)
# print(iou)
if iou > 0.75:
true_positives += 1
# print(True)
else:
false_positives += 1
# print(False)
else:
# print("no match")
pass
print('True positives: ', true_positives)
print('False positives: ', false_positives)
print('Matches: ', matches)
print(true_positives + false_positives)
# print(matches)
if __name__ == '__main__':
# calculate_iou()
new_calculate_iou()