#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 20 15:15:11 2022
@author: dinesh
"""
from collections import OrderedDict
from matplotlib import pyplot as plt
from .utils import *
import scipy.interpolate
from scipy import interpolate
from .clustering_utils import *
import glob
import cv2
from PIL import Image
import json
import cv2
import numpy as np
from tqdm import tqdm
def ignore_indexes(tracks_all, labels_all):
# get repeating bounding boxes
get_indexes = lambda x, xs: [i for (y, i) in zip(xs, range(len(xs))) if x == y]
ignore_ind = []
for index, track in enumerate(tracks_all):
print('in ignore', index, len(tracks_all))
if index in ignore_ind:
continue
if labels_all[index] < 1 or labels_all[index] > 3:
ignore_ind.extend([index])
ind = get_indexes(track, tracks_all)
if len(ind) > 30:
ignore_ind.extend(ind)
return ignore_ind
def repeated_indexes_old(tracks_all,ignore_ind, unoccluded_indexes=None):
# get repeating bounding boxes
get_indexes = lambda x, xs: [i for (y, i) in zip(xs, range(len(xs))) if bb_intersection_over_union(x, y) > 0.8 and i not in ignore_ind]
repeat_ind = []
repeat_inds =[]
if unoccluded_indexes == None:
for index, track in enumerate(tracks_all):
if index in repeat_ind or index in ignore_ind:
continue
ind = get_indexes(track, tracks_all)
if len(ind) > 20:
repeat_ind.extend(ind)
repeat_inds.append([ind,track])
else:
for index in unoccluded_indexes:
if index in repeat_ind or index in ignore_ind:
continue
ind = get_indexes(tracks_all[index], tracks_all)
if len(ind) > 3:
repeat_ind.extend(ind)
repeat_inds.append([ind,tracks_all[index]])
return repeat_inds
def get_unoccluded_instances(timestamps_final, tracks_all, ignore_ind=[], threshold = 0.01):
get_indexes = lambda x, xs: [i for (y, i) in zip(xs, range(len(xs))) if x==y]
unoccluded_indexes = []
time_checked = []
stationary_obj = []
count =0
for time in tqdm(np.unique(timestamps_final), desc="Detecting Unocclued objects in Image "):
count += 1
if [time.year,time.month, time.day, time.hour, time.minute, time.second, time.microsecond] in time_checked:
analyze_bb = []
for ind in unoccluded_indexes_time:
for ind_compare in same_time_instances:
iou = bb_intersection_over_union(tracks_all[ind], tracks_all[ind_compare])
if iou < 0.5 and iou > 0:
analyze_bb.extend([ind_compare])
if iou > 0.99:
stationary_obj.extend([str(ind_compare)+'+'+str(ind)])
for ind in analyze_bb:
occ = False
for ind_compare in same_time_instances:
if bb_intersection_over_union_unoccluded(tracks_all[ind], tracks_all[ind_compare], threshold=threshold) > threshold and ind_compare != ind:
occ = True
break
if occ == False:
unoccluded_indexes.extend([ind])
continue
same_time_instances = get_indexes(time,timestamps_final)
unoccluded_indexes_time = []
for ind in same_time_instances:
if tracks_all[ind][4] < 0.9 or ind in ignore_ind:# or ind != 1859:
continue
occ = False
for ind_compare in same_time_instances:
if bb_intersection_over_union_unoccluded(tracks_all[ind], tracks_all[ind_compare], threshold=threshold) > threshold and ind_compare != ind and tracks_all[ind_compare][4] < 0.5:
occ = True
break
if occ==False:
unoccluded_indexes.extend([ind])
unoccluded_indexes_time.extend([ind])
time_checked.append([time.year,time.month, time.day, time.hour, time.minute, time.second, time.microsecond])
return unoccluded_indexes,stationary_obj
def visualize_unoccluded_detection(timestamps_final,tracks_all,segmentation_all, unoccluded_indexes, cwalt_data_path, camera_name, ignore_ind=[]):
tracks_final = []
tracks_final.append([])
try:
os.mkdir(cwalt_data_path + '/' + camera_name+'_unoccluded_car_detection/')
except:
print('Unoccluded debugging exists')
for time in tqdm(np.unique(timestamps_final), desc="Visualizing Unocclued objects in Image "):
get_indexes = lambda x, xs: [i for (y, i) in zip(xs, range(len(xs))) if x==y]
ind = get_indexes(time, timestamps_final)
image_unocc = False
for index in ind:
if index not in unoccluded_indexes:
continue
else:
image_unocc = True
break
if image_unocc == False:
continue
for week_loop in range(5):
try:
image = np.array(Image.open(cwalt_data_path+'/week' +str(week_loop)+'/'+ str(time).replace(' ','T').replace(':','-').split('+')[0] + '.jpg'))
break
except:
continue
try:
mask = image*0
except:
print('image not found for ' + str(time).replace(' ','T').replace(':','-').split('+')[0] + '.jpg' )
continue
image_original = image.copy()
for index in ind:
track = tracks_all[index]
if index in ignore_ind:
continue
if index not in unoccluded_indexes:
continue
try:
bb_left, bb_top, bb_width, bb_height, confidence, id = track
except:
bb_left, bb_top, bb_width, bb_height, confidence = track
if confidence > 0.6:
mask = poly_seg(image, segmentation_all[index])
cv2.imwrite(cwalt_data_path + '/' + camera_name+'_unoccluded_car_detection/' + str(index)+'.png', mask[:, :, ::-1])
def repeated_indexes(tracks_all,ignore_ind, repeat_count = 10, unoccluded_indexes=None):
get_indexes = lambda x, xs: [i for (y, i) in zip(xs, range(len(xs))) if bb_intersection_over_union(x, y) > 0.8 and i not in ignore_ind]
repeat_ind = []
repeat_inds =[]
if unoccluded_indexes == None:
for index, track in enumerate(tracks_all):
if index in repeat_ind or index in ignore_ind:
continue
ind = get_indexes(track, tracks_all)
if len(ind) > repeat_count:
repeat_ind.extend(ind)
repeat_inds.append([ind,track])
else:
for index in unoccluded_indexes:
if index in repeat_ind or index in ignore_ind:
continue
ind = get_indexes(tracks_all[index], tracks_all)
if len(ind) > repeat_count:
repeat_ind.extend(ind)
repeat_inds.append([ind,tracks_all[index]])
return repeat_inds
def poly_seg(image, segm):
poly = np.array(segm).reshape((int(len(segm)/2), 2))
overlay = image.copy()
alpha = 0.5
cv2.fillPoly(overlay, [poly], color=(255, 255, 0))
cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0, image)
return image
def visualize_unoccuded_clusters(repeat_inds, tracks, segmentation_all, timestamps_final, cwalt_data_path):
for index_, repeat_ind in enumerate(repeat_inds):
image = np.array(Image.open(cwalt_data_path+'/'+'T18-median_image.jpg'))
try:
os.mkdir(cwalt_data_path+ '/Cwalt_database/')
except:
print('folder exists')
try:
os.mkdir(cwalt_data_path+ '/Cwalt_database/' + str(index_) +'/')
except:
print(cwalt_data_path+ '/Cwalt_database/' + str(index_) +'/')
for i in repeat_ind[0]:
try:
bb_left, bb_top, bb_width, bb_height, confidence = tracks[i]#bbox
except:
bb_left, bb_top, bb_width, bb_height, confidence, track_id = tracks[i]#bbox
cv2.rectangle(image,(int(bb_left), int(bb_top)),(int(bb_left+bb_width), int(bb_top+bb_height)),(0, 0, 255), 2)
time = timestamps_final[i]
for week_loop in range(5):
try:
image1 = np.array(Image.open(cwalt_data_path+'/week' +str(week_loop)+'/'+ str(time).replace(' ','T').replace(':','-').split('+')[0] + '.jpg'))
break
except:
continue
crop = image1[int(bb_top): int(bb_top + bb_height), int(bb_left):int(bb_left + bb_width)]
cv2.imwrite(cwalt_data_path+ '/Cwalt_database/' + str(index_) +'/o_' + str(i) +'.jpg', crop[:, :, ::-1])
image1 = poly_seg(image1,segmentation_all[i])
crop = image1[int(bb_top): int(bb_top + bb_height), int(bb_left):int(bb_left + bb_width)]
cv2.imwrite(cwalt_data_path+ '/Cwalt_database/' + str(index_) +'/' + str(i)+'.jpg', crop[:, :, ::-1])
if index_ > 100:
break
cv2.imwrite(cwalt_data_path+ '/Cwalt_database/' + str(index_) +'.jpg', image[:, :, ::-1])
def Get_unoccluded_objects(camera_name, debug = False, scale=True):
cwalt_data_path = 'data/' + camera_name
data_folder = cwalt_data_path
json_file_path = cwalt_data_path + '/' + camera_name + '.json'
with open(json_file_path, 'r') as j:
annotations = json.loads(j.read())
tracks_all = [parse_bbox(anno['bbox']) for anno in annotations]
segmentation_all = [parse_bbox(anno['segmentation']) for anno in annotations]
labels_all = [anno['label_id'] for anno in annotations]
timestamps_final = [parse(anno['time']) for anno in annotations]
if scale ==True:
scale_factor = 2
tracks_all_numpy = np.array(tracks_all)
tracks_all_numpy[:,:4] = np.array(tracks_all)[:,:4]/scale_factor
tracks_all = tracks_all_numpy.tolist()
segmentation_all_scaled = []
for list_loop in segmentation_all:
segmentation_all_scaled.append((np.floor_divide(np.array(list_loop),scale_factor)).tolist())
segmentation_all = segmentation_all_scaled
if debug == True:
timestamps_final = timestamps_final[:1000]
labels_all = labels_all[:1000]
segmentation_all = segmentation_all[:1000]
tracks_all = tracks_all[:1000]
unoccluded_indexes, stationary = get_unoccluded_instances(timestamps_final, tracks_all, threshold = 0.05)
if debug == True:
visualize_unoccluded_detection(timestamps_final, tracks_all, segmentation_all, unoccluded_indexes, cwalt_data_path, camera_name)
tracks_all_unoccluded = [tracks_all[i] for i in unoccluded_indexes]
segmentation_all_unoccluded = [segmentation_all[i] for i in unoccluded_indexes]
labels_all_unoccluded = [labels_all[i] for i in unoccluded_indexes]
timestamps_final_unoccluded = [timestamps_final[i] for i in unoccluded_indexes]
np.savez(json_file_path,tracks_all_unoccluded=tracks_all_unoccluded, segmentation_all_unoccluded=segmentation_all_unoccluded, labels_all_unoccluded=labels_all_unoccluded, timestamps_final_unoccluded=timestamps_final_unoccluded )
if debug == True:
repeat_inds_clusters = repeated_indexes(tracks_all_unoccluded,[], repeat_count=1)
visualize_unoccuded_clusters(repeat_inds_clusters, tracks_all_unoccluded, segmentation_all_unoccluded, timestamps_final_unoccluded, cwalt_data_path)
else:
repeat_inds_clusters = repeated_indexes(tracks_all_unoccluded,[], repeat_count=10)
np.savez(json_file_path + '_clubbed', repeat_inds=repeat_inds_clusters)
np.savez(json_file_path + '_stationary', stationary=stationary)