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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 20 15:16:56 2022
@author: dinesh
"""
import json
import cv2
from PIL import Image
import numpy as np
from dateutil.parser import parse
def bb_intersection_over_union(box1, box2):
#print(box1, box2)
boxA = box1.copy()
boxB = box2.copy()
boxA[2] = boxA[0]+boxA[2]
boxA[3] = boxA[1]+boxA[3]
boxB[2] = boxB[0]+boxB[2]
boxB[3] = boxB[1]+boxB[3]
# 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 = abs(max((xB - xA, 0)) * max((yB - yA), 0))
if interArea == 0:
return 0
# compute the area of both the prediction and ground-truth
# rectangles
boxAArea = abs((boxA[2] - boxA[0]) * (boxA[3] - boxA[1]))
boxBArea = abs((boxB[2] - boxB[0]) * (boxB[3] - boxB[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 iou
def bb_intersection_over_union_unoccluded(box1, box2, threshold=0.01):
#print(box1, box2)
boxA = box1.copy()
boxB = box2.copy()
boxA[2] = boxA[0]+boxA[2]
boxA[3] = boxA[1]+boxA[3]
boxB[2] = boxB[0]+boxB[2]
boxB[3] = boxB[1]+boxB[3]
# 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 = abs(max((xB - xA, 0)) * max((yB - yA), 0))
if interArea == 0:
return 0
# compute the area of both the prediction and ground-truth
# rectangles
boxAArea = abs((boxA[2] - boxA[0]) * (boxA[3] - boxA[1]))
boxBArea = abs((boxB[2] - boxB[0]) * (boxB[3] - boxB[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)
#print(iou)
# return the intersection over union value
occlusion = False
if iou > threshold and iou < 1:
#print(boxA[3], boxB[3], boxB[1])
if boxA[3] < boxB[3]:# and boxA[3] > boxB[1]:
if boxB[2] > boxA[0]:# and boxB[2] < boxA[2]:
#print('first', (boxB[2] - boxA[0])/(boxA[2] - boxA[0]))
if (min(boxB[2],boxA[2]) - boxA[0])/(boxA[2] - boxA[0]) > threshold:
occlusion = True
if boxB[0] < boxA[2]: # boxB[0] > boxA[0] and
#print('second', (boxA[2] - boxB[0])/(boxA[2] - boxA[0]))
if (boxA[2] - max(boxB[0],boxA[0]))/(boxA[2] - boxA[0]) > threshold:
occlusion = True
if occlusion == False:
iou = iou*0
#asas
# asas
#iou = 0.9 #iou*0
#print(box1, box2, iou, occlusion)
return iou
def draw_tracks(image, tracks):
"""
Draw on input image.
Args:
image (numpy.ndarray): image
tracks (list): list of tracks to be drawn on the image.
Returns:
numpy.ndarray: image with the track-ids drawn on it.
"""
for trk in tracks:
trk_id = trk[1]
xmin = trk[2]
ymin = trk[3]
width = trk[4]
height = trk[5]
xcentroid, ycentroid = int(xmin + 0.5*width), int(ymin + 0.5*height)
text = "ID {}".format(trk_id)
cv2.putText(image, text, (xcentroid - 10, ycentroid - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.circle(image, (xcentroid, ycentroid), 4, (0, 255, 0), -1)
return image
def draw_bboxes(image, tracks):
"""
Draw the bounding boxes about detected objects in the image.
Args:
image (numpy.ndarray): Image or video frame.
bboxes (numpy.ndarray): Bounding boxes pixel coordinates as (xmin, ymin, width, height)
confidences (numpy.ndarray): Detection confidence or detection probability.
class_ids (numpy.ndarray): Array containing class ids (aka label ids) of each detected object.
Returns:
numpy.ndarray: image with the bounding boxes drawn on it.
"""
for trk in tracks:
xmin = int(trk[2])
ymin = int(trk[3])
width = int(trk[4])
height = int(trk[5])
clr = (np.random.randint(0, 255), np.random.randint(0, 255), np.random.randint(0, 255))
cv2.rectangle(image, (xmin, ymin), (xmin + width, ymin + height), clr, 2)
return image
def num(v):
number_as_float = float(v)
number_as_int = int(number_as_float)
return number_as_int if number_as_float == number_as_int else number_as_float
def parse_bbox(bbox_str):
bbox_list = bbox_str.strip('{').strip('}').split(',')
bbox_list = [num(elem) for elem in bbox_list]
return bbox_list
def parse_seg(bbox_str):
bbox_list = bbox_str.strip('{').strip('}').split(',')
bbox_list = [num(elem) for elem in bbox_list]
ret = bbox_list # []
# for i in range(0, len(bbox_list) - 1, 2):
# ret.append((bbox_list[i], bbox_list[i + 1]))
return ret
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