IOU.py

# import the necessary packages
from collections import namedtuple
import numpy as np
# import cv2

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)
	# compute the area of both the prediction and ground-truth
	# rectangles
	boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
	boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
	# 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

Data_Values = []
Map = []
Title = []

# Read lines from the file and process each line
with open("sorted_class0.txt", 'r') as file:
    for line in file:
        # Split the line into elements using space as a delimiter
        elements = line.split()
        
        # Ensure the line has at least 4 elements before adding them to the list
        if len(elements) >= 4:
            Data_Values.append(elements[-4:])

# Read lines from the file and process each line
with open("sorted_class1.txt", 'r') as file:
    for line in file:
        # Split the line into elements using space as a delimiter
        elements = line.split()
        
        # Ensure the line has at least 4 elements before adding them to the list
        if len(elements) >= 4:
            Map.append(elements[-4:])

# Read lines from the file and process each line
with open("sorted_class2.txt", 'r') as file:
    for line in file:
        # Split the line into elements using space as a delimiter
        elements = line.split()
        
        # Ensure the line has at least 4 elements before adding them to the list
        if len(elements) >= 4:
            Title.append(elements[-4:])

# print(Data_Values)
# print(Map)
# print(Title)

# define the `Detection` object
Detection = namedtuple("Detection", ["image_path", "gt", "pred"])

detections = [
    Detection("dataset/kaggle/working/annotated/prediction_266.jpg", Data_Values[1], [54,66,198,113])
]

# define the list of example detections
# examples = [
# 	Detection("image_0002.jpg", [39, 63, 203, 112], [54, 66, 198, 114]),
# 	Detection("image_0016.jpg", [49, 75, 203, 125], [42, 78, 186, 126]),
# 	Detection("image_0075.jpg", [31, 69, 201, 125], [18, 63, 235, 135]),
# 	Detection("image_0090.jpg", [50, 72, 197, 121], [54, 72, 198, 120]),
# 	Detection("image_0120.jpg", [35, 51, 196, 110], [36, 60, 180, 108])]

# loop over the example detections
# for detection in examples:
# 	# load the image
# 	image = cv2.imread(detection.image_path)
# 	# draw the ground-truth bounding box along with the predicted
# 	# bounding box
# 	cv2.rectangle(image, tuple(detection.gt[:2]), 
# 		tuple(detection.gt[2:]), (0, 255, 0), 2)
# 	cv2.rectangle(image, tuple(detection.pred[:2]), 
# 		tuple(detection.pred[2:]), (0, 0, 255), 2)
# 	# compute the intersection over union and display it
# 	iou = bb_intersection_over_union(detection.gt, detection.pred)
# 	cv2.putText(image, "IoU: {:.4f}".format(iou), (10, 30),
# 		cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
# 	print("{}: {:.4f}".format(detection.image_path, iou))
# 	# show the output image
# 	cv2.imshow("Image", image)
# 	cv2.waitKey(0)