-
Notifications
You must be signed in to change notification settings - Fork 0
/
vdtools.py
789 lines (612 loc) · 29.5 KB
/
vdtools.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
from skimage.feature import hog
from sklearn.svm import LinearSVC
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
from sklearn.metrics import recall_score
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from scipy.ndimage.measurements import label
import math
# from keras.models import load_model
import glob
import pickle
import time
class WindowFinder(object):
"""Finds windows in an image that contain a car."""
def __init__(self):
### Hyperparameters, if changed ->(load_saved = False) If
### the classifier is changes load_feaures can be True
self.load_saved = True# Loads classifier and scaler
self.load_features = True # Loads saved features (to train new classifier)
self.sample_size = 15000 # How many to sample from training set
self.color_space = 'HSV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
self.orient = 8 # HOG orientations
self.pix_per_cell = 12 # HOG pixels per cell
self.cell_per_block = 2 # HOG cells per block
self.hog_channel = 0 # Can be 0, 1, 2, or "ALL"
self.spatial_size = (8, 8) # Spatial binning dimensions
self.hist_bins = 12 # Number of histogram bins
self.spatial_feat = True # Spatial features on or off
self.hist_feat = True # Histogram features on or off
self.hog_feat = True # HOG features on or off
# The locations of all the data.
self.notcar_data_folders = ['./data/non-vehicles/Extras',
'./data/non-vehicles/GTI']
self.car_data_folders = ['./data/vehicles/GTI_MiddleClose',
'./data/vehicles/GTI_Far',
'./data/vehicles/KITTI_extracted',
'./data/vehicles/GTI_Right',
'./data/vehicles/GTI_Left']
######Classifiers
self.pred_thresh = 0.55 #Increase to decrease likelihood of detection.
###### Variable for Classifier and Feature Scaler ##########
self.untrained_clf = RandomForestClassifier(n_estimators=100, max_features = 2,
min_samples_leaf = 4,max_depth = 25)
self.trained_clf, self.scaler = self.__get_classifier_and_scaler()
###### Variables for CNN ##########
# print('Loading Neural Network...')
# self.nn = load_model('models/keras(32x32).h5')
# self.nn_train_size = (32,32) # size of training data used for CNN
# self.nn.summary()
# print('Neural Network Loaded.')
def __get_classifier_and_scaler(self):
"""
Gets the classifier and scaler needed for the rest of the operations. Loads from cache if
load_saved is set to true.
"""
if self.load_saved:
print('Loading saved classifier and scaler...')
clf = pickle.load( open( "./cache/clf.p", "rb" ) )
print('%s loaded...' % self.untrained_clf.__class__.__name__)
scaler = pickle.load(open( "./cache/scaler.p", "rb" ))
else:
# Split up data into randomized training and test sets
print('Training a %s...' % self.untrained_clf.__class__.__name__)
rand_state = np.random.randint(0, 100)
# TODO: Get scaled_X, and y here.
car_features, notcar_features = self.__get_features()
scaled_X, y, scaler = self.__get_scaled_X_y(car_features, notcar_features)
X_train, X_test, y_train, y_test = train_test_split(
scaled_X, y, test_size=0.05, random_state=rand_state)
# Use a linear SVC
clf = self.untrained_clf
# Check the training time for the SVC
t=time.time()
clf.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train CLF...')
# Check the score of the SVC
preds = clf.predict(X_test)
print('Test Recall of CLF = ', round(recall_score(y_test, preds), 4))
# Check the prediction time for a single sample
t=time.time()
print('Pickling classifier and scaler...')
pickle.dump( clf, open( "./cache/clf.p", "wb" ) )
pickle.dump( scaler, open( "./cache/scaler.p", "wb" ) )
return clf, scaler
def __get_features(self):
"""
Gets features either by loading them from cache, or by extracting them from the data.
"""
if self.load_features:
print('Loading saved features...')
car_features, notcar_features = pickle.load( open( "./cache/features.p", "rb" ) )
else:
print("Extracting features from %s samples..." % self.sample_size)
notcars = []
cars = []
for folder in self.notcar_data_folders:
image_paths =glob.glob(folder+'/*')
for path in image_paths:
notcars.append(path)
for folder in self.car_data_folders:
image_paths =glob.glob(folder+'/*')
for path in image_paths:
cars.append(path)
cars = cars[0:self.sample_size]
notcars = notcars[0:self.sample_size]
start = time.clock()
car_features = self.__extract_features(cars)
notcar_features = self.__extract_features(notcars)
end = time.clock()
print("Running time : %s seconds" % (end - start))
print('Pickling features...')
pickle.dump( (car_features, notcar_features), open( "./cache/features.p", "wb" ) )
return car_features, notcar_features
def __extract_features(self, imgs):
"""
Extract features from image files.
"""
# Create a list to append feature vectors to
features = []
# Iterate through the list of images
for file in imgs:
# Read in each one by one
image = mpimg.imread(file)
# Get features for one image
file_features = self.__single_img_features(image)
#Append to master list
features.append(file_features)
# Return list of feature vectors
return features
def __single_img_features(self, img):
"""
Define a function to extract features from a single image window
This function is very similar to extract_features()
just for a single image rather than list of images
Define a function to extract features from a single image window
This function is very similar to extract_features()
just for a single image rather than list of images
"""
#1) Define an empty list to receive features
img_features = []
#2) Apply color conversion if other than 'RGB'
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)# convert it to HLS
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
#3) Compute spatial features if flag is set
if self.spatial_feat == True:
spatial_hls = self.__bin_spatial(hls)
spatial_rgb = self.__bin_spatial(img)
img_features.append(spatial_hls)
img_features.append(spatial_rgb)
#5) Compute histogram features if flag is set
if self.hist_feat == True:
hist_features_hls = self.__color_hist(hls)
hist_features_rgb = self.__color_hist(img)
#6) Append features to list
img_features.append(hist_features_hls)
img_features.append(hist_features_rgb)
#7) Compute HOG features if flag is set
if self.hog_feat == True:
hog_features = self.__get_hog_features(gray, vis=False, feature_vec=True)
# if self.hog_channel == 'ALL':
# hog_features = []
# for channel in range(img.shape[2]):
# hog_features.extend(self.__get_hog_features(img[:,:,channel],
# vis=False, feature_vec=True))
# else:
# hog_features = self.__get_hog_features(feature_image[:,:,self.hog_channel],
# vis=False, feature_vec=True)
#8) Append features to list
img_features.append(hog_features)
#9) Return concatenated array of features
return np.concatenate(img_features)
def __get_scaled_X_y(self, car_features, notcar_features):
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)
# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))
#TODO: save X_scaler as a self variable, pickle along with the classifier and features.
return scaled_X, y, X_scaler
# Define a function to return HOG features and visualization
def __get_hog_features(self, img, vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis == True:
features, hog_image = hog(img, orientations=self.orient,
pixels_per_cell=(self.pix_per_cell, self.pix_per_cell),
cells_per_block=(self.cell_per_block, self.cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
# Otherwise call with one output
else:
features = hog(img, orientations=self.orient,
pixels_per_cell=(self.pix_per_cell, self.pix_per_cell),
cells_per_block=(self.cell_per_block, self.cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features
# Define a function to compute binned color features
def __bin_spatial(self, img):
# Use cv2.resize().ravel() to create the feature vector
features = cv2.resize(img, self.spatial_size).ravel()
# Return the feature vector
return features
# Define a function to compute color histogram features
# NEED TO CHANGE bins_range if reading .png files with mpimg!
def __color_hist(self, img, bins_range=(0, 256)):
# Compute the histogram of the color channels separately
channel1_hist = np.histogram(img[:,:,0], bins=self.hist_bins, range=bins_range)
channel2_hist = np.histogram(img[:,:,1], bins=self.hist_bins, range=bins_range)
channel3_hist = np.histogram(img[:,:,2], bins=self.hist_bins, range=bins_range)
# Concatenate the histograms into a single feature vector
hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
# Return the individual histograms, bin_centers and feature vector
return hist_features
# Define a function to extract features from a list of images
# Have this function call bin_spatial() and color_hist()
def __classify_windows(self, img, windows):
"""
Define a function you will pass an image
and the list of windows to be searched (output of slide_windows())
"""
#1) Create an empty list to receive positive detection windows
on_windows = []
#2) Iterate over all windows in the list
for window in windows:
######### Classifier HOG Feature Prediction #########
test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))
features = self.__single_img_features(test_img)
test_features = self.scaler.transform(np.array(features).reshape(1, -1))
prediction = self.trained_clf.predict_proba(test_features)[:,1]
## SVC prediction
# prediction = self.trained_clf.predict(test_features)
######### Neural Network Predicion ########
# test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]],
# self.nn_train_size)
# test_img = self.__normalize_image(test_img)
# test_img = np.reshape(test_img, (1,self.nn_train_size[0],self.nn_train_size[1],3))
# prediction = self.nn.predict_classes(test_img, verbose=0)
if prediction >= self.pred_thresh:
on_windows.append(window)
#8) Return windows for positive detections
# print("Number of hot windows:", len(on_windows))
# print("Number of windows:", len(windows))
return on_windows
def __normalize_image(self, img):
img = img.astype(np.float32)
# Normalize image
img = img / 255.0 - 0.5
return img
def __visualise_searchgrid_and_hot(self, img, windows, hot_windows, ax=None):
"""
Draws the search grid and the hot windows.
"""
# print('Hot Windows...', hot_windows)
search_grid_img = self.__draw_boxes(img, windows, color=(0, 0, 255), thick=6)
hot_window_img = self.__draw_boxes(img, hot_windows, color=(0, 0, 255), thick=6)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,6))
f.tight_layout()
ax1.imshow(search_grid_img)
ax1.set_title('Search Grid')
ax2.imshow(hot_window_img)
ax2.set_title('Hot Boxes')
plt.show()
return
# Define a function to draw bounding boxes
def __draw_boxes(self, img, bboxes, color=(0, 0, 255), thick=6):
"""Draws boxes on image from a list of windows"""
# Make a copy of the image
imcopy = np.copy(img)
# Iterate through the bounding boxes
for bbox in bboxes:
# Draw a rectangle given bbox coordinates
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
# Return the image copy with boxes drawn
return imcopy
def __slide_windows(self, img, x_start_stop,
y_start_stop, xy_window,
xy_overlap,
visualise=False):
"""
Define a function that takes an image, start and stop positions in both x and y,
window size (x and y dimensions), and overlap fraction (for both x and y). Send
the results to __search_windows to get the classifications.
"""
# If x and/or y start/stop positions not defined, set to image size
if x_start_stop[0] == None:
x_start_stop[0] = 0
if x_start_stop[1] == None:
x_start_stop[1] = img.shape[1]
if y_start_stop[0] == None:
y_start_stop[0] = 0
if y_start_stop[1] == None:
y_start_stop[1] = img.shape[0]
# Compute the span of the region to be searched
xspan = x_start_stop[1] - x_start_stop[0]
yspan = y_start_stop[1] - y_start_stop[0]
# Compute the number of pixels per step in x/y
nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
# Compute the number of windows in x/y
nx_windows = np.int(xspan/nx_pix_per_step) - 1
ny_windows = np.int(yspan/ny_pix_per_step) - 1
# Initialize a list to append window positions to
window_list = []
# Loop through finding x and y window positions
# Note: you could vectorize this step, but in practice
# you'll be considering windows one by one with your
# classifier, so looping makes sense
for ys in range(ny_windows):
for xs in range(nx_windows):
# Calculate window position
startx = xs*nx_pix_per_step + x_start_stop[0]
endx = startx + xy_window[0]
starty = ys*ny_pix_per_step + y_start_stop[0]
endy = starty + xy_window[1]
# Append window position to list
window_list.append(((startx, starty), (endx, endy)))
return window_list
def get_hot_windows(self, img, visualise=False):
"""
Defines a function that takes an image, and return all of the hot_windows. Or windows that contain a car
"""
windows_list = []
# define the minimum window size
x_min =[600, 1280]
y_min =[400, 530]
xy_min = (80, 80)
# define the maxium window size
x_max =[300, 1280]
y_max =[400, 700]
xy_max = (195, 195)
# intermedian windows
n = 4 # the number of total window sizes
x = []
y = []
xy =[]
# chose the intermediate sizes by interpolation.
for i in range(n):
x_start_stop =[int(x_min[0] + i*(x_max[0]-x_min[0])/(n-1)),
int(x_min[1] + i*(x_max[1]-x_min[1])/(n-1))]
y_start_stop =[int(y_min[0] + i*(y_max[0]-y_min[0])/(n-1)),
int(y_min[1] + i*(y_max[1]-y_min[1])/(n-1))]
xy_window =[int(xy_min[0] + i*(xy_max[0]-xy_min[0])/(n-1)),
int(xy_min[1] + i*(xy_max[1]-xy_min[1])/(n-1))]
x.append(x_start_stop)
y.append(y_start_stop)
xy.append(xy_window)
windows1 = self.__slide_windows(img, x_start_stop= x[0], y_start_stop = y[0],
xy_window= xy[0], xy_overlap=(0.5, 0.5))
windows2 = self.__slide_windows(img, x_start_stop= x[1], y_start_stop = y[1],
xy_window= xy[1], xy_overlap=(0.5, 0.5))
windows3 = self.__slide_windows(img, x_start_stop= x[2], y_start_stop = y[2],
xy_window= xy[2], xy_overlap=(0.5, 0.5))
windows4 = self.__slide_windows(img, x_start_stop= x[3], y_start_stop = y[3],
xy_window= xy[3], xy_overlap=(0.5, 0.5))
windows_list = list(windows1 + windows2 + windows3 + windows4)
hot_windows = self.__classify_windows(img, windows_list)
if visualise:
window_img = self.__draw_boxes(img, hot_windows, color=(0, 0, 255), thick=6)
plt.figure(figsize=(10,6))
plt.imshow(window_img)
plt.tight_layout()
plt.show()
# return window_img
return hot_windows
class HeatMapper(object):
"""The Heat Mapper takes in an image, and makes a blank
heatmap.
- add_heat(windows), will add heat in the windows.
- apply_threshold() thresholds the heatmap
- get_heatmap returns the heatmap.
- visualise_heatmap_and_result() gives a dubugging image.
"""
def __init__(self, img):
self.img = img
self.heatmap = np.zeros_like(img[:,:,0]).astype(np.float)
def add_heat(self, bbox_list):
"""
Adds +1 heat for all areas in boxes
"""
# Iterate through list of bboxes
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
# Assuming each "box" takes the form ((x1, y1), (x2, y2))
self.heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1
# Return updated heatmap
return True
def apply_threshold(self, threshold):
# Zero out pixels below the threshold
self.heatmap[self.heatmap <= threshold] = 0
# Return thresholded map
return True
def __draw_labeled_bboxes(self, img, labels):
"""
Iterate through all detected cars.
"""
for car_number in range(1, labels[1]+1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
# Draw the box on the image
cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
# Return the image
return img
def get_heatmap(self):
"""
Returns the heatmap.
"""
return self.heatmap
def get_heatmap_max(self):
"""
Returns the heatmap.
"""
return self.heatmap.max()
def get_heatmap_and_result(self, ax=None):
labels = label(self.heatmap)
draw_img = self.__draw_labeled_bboxes(np.copy(self.img), labels)
print('Cars found:', labels[1])
# plt.imshow(labels[0], cmap='gray')
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,6))
f.tight_layout()
ax1.imshow(self.heatmap, cmap='hot')
ax1.set_title('Heat Map')
ax2.imshow(draw_img)
ax2.set_title('Draw Window')
return f
def apply_upper_threshold(self, upper):
"""
Bounds the heatmap by upper threshold.
"""
# Zero out pixels below the threshold
self.heatmap[self.heatmap > upper] = 1
class Car():
def __init__(self):
self.average_centroid= (0,0) # average centroid
self.width = 0 # average box width
self.height = 0 # average height
self.detected = 0.5 # moving average
class VehicleTracker(object):
"""docstring for VehicleDetector"""
def __init__(self):
##TODO: Need to clean this up
img = mpimg.imread('./test_images/test6.jpg')
self.heatmap = np.zeros_like(img[:,:,0]).astype(np.float)
self.detected_cars = []
def image_pipeline(self, img, hot_windows, return_img=True):
# make a copy of the incial image
draw_img = np.copy(img)
# find windows that contains cars
# draw windows that contains cars
# draw_img = self.__draw_boxes(draw_img, hot_windows, color=(0, 0, 255), thick=2)
# create a new heat map
new_heatMapper = HeatMapper(img)
new_heatMapper.add_heat(hot_windows)
new_heatMapper.apply_upper_threshold(1)
new_heatmap = new_heatMapper.get_heatmap()
# update the heatmap with the moving average algorithm
# so that, if car image are no longer detacted, that area "cool" down
self.heatmap = 0.9*self.heatmap + 0.1*new_heatmap
# Blend imgage to heatmap
wrap_img = np.zeros_like(img) # inicalize
wrap_img[:,:,1] = self.heatmap[:]*250 # adding heat map
draw_img = cv2.addWeighted(draw_img, 1, wrap_img, 0.5, 0)
# create a new heatmap to show the heatmap with more certainty
# by thresholding the heatmap value
certain_heatmap = np.copy(self.heatmap)
# get area of higher certainty by thredholding the heatmap
certain_heatmap= self.__apply_lower_threshold(certain_heatmap, 0.97)
# Find bounding boxes
labels = label(certain_heatmap)
bounding_boxes = self.__find_labeled_bboxes(img, labels)
# find centroy and size of bounding box
centroids, box_size = self.__find_box_centroid_size(bounding_boxes)
new_cars = [] # inicalize a list of new found cars
for n in range(len(centroids)):
# find nearby car object
car_found, k = self.__track_car(centroids[n], self.detected_cars)
if car_found == True:
# update detected car object
# update centroid using moving average
self.detected_cars[k].average_centroid = (int(0.9*self.detected_cars[k].average_centroid[0] + 0.1*centroids[n][0]),
int(0.9*self.detected_cars[k].average_centroid[1] + 0.1*centroids[n][1]))
# update bounding box width using moving average
self.detected_cars[k].width = math.ceil(0.9*self.detected_cars[k].width + 0.1*box_size[n][0]) # round up
# update bounding box height using moving average
self.detected_cars[k].height = math.ceil(0.9*self.detected_cars[k].height + 0.1*box_size[n][1])
# update detected value
self.detected_cars[k].detected = self.detected_cars[k].detected + 0.23
else: # add new car
new_car = Car()
# initialize the car object using the size
# and centroid of the bounding box
new_car.average_centroid = centroids[n]
new_car.width = box_size[n][0]
new_car.height = box_size[n][1]
new_cars.append(new_car)
# combine new_cars to detected cars
detected_cars2 = list(self.detected_cars) # make a copy
self.detected_cars = new_cars[:] # add new cars
if detected_cars2: # if is not empty
for car in detected_cars2:
# if the detected value greater than the threshold add to the list
# if not discard
if car.detected > 0.17:
# add to the detected cars list
self.detected_cars.append(car)
# find car object that is consistent
car_boxes = self.__find_car_box(detected_threshold = 0.51) #0.51
# draw bounding boxes on car object that is more certain
draw_img = self.__draw_boxes(draw_img, car_boxes, color=(128, 0, 0), thick=5)
# depreciate old car values, so if it no longer detacted the value fade away
for car in self.detected_cars:
car.detected = car.detected*0.85 # depreciate old value
if return_img:
return draw_img
else:
return car_boxes, wrap_img
def __apply_lower_threshold(self, heatmap, lower):
# Zero out pixels below the threshold
heatmap[heatmap < lower] = 0
# Return thresholded map
return heatmap
def __find_labeled_bboxes(self, img, labels):
# Iterate through all detected cars
bboxes = []
for car_number in range(1, labels[1]+1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
# append the bounding box to a list
bboxes.append(bbox)
# Return the bounding boxes
return bboxes
def __find_box_centroid_size(self, bboxes):
box_centroids = []
box_size = []
for box in bboxes:
x = int((box[0][0] + box[1][0])/2)
y = int((box[0][1] + box[1][1])/2)
box_centroids.append((x,y))
width = int((box[1][0] - box[0][0])/2)
height = int((box[1][1] - box[0][1])/2)
box_size.append((width,height))
return box_centroids, box_size
def __cal_dist(self,centroid1, centroid2):
x1 = centroid1[0]
y1 = centroid1[1]
x2 = centroid2[0]
y2 = centroid2[1]
return np.sqrt((x1-x2)**2 + (y1-y2)**2)
# define a function to find nearby car object
def __track_car(self, cntrd,old_Cars):
threshod_dist = 50 # the maxium distance to consider nearby
Dist = [] # a list of distance
if not old_Cars: # if the list of nearby cars is empty
# return car not found
car_found = False
car_id = 0
return car_found,car_id
else:
for car in old_Cars:
# cacualte the distance
dist = self.__cal_dist(cntrd, car.average_centroid)
Dist.append(dist)
car_id = np.argmin(Dist)
if Dist[car_id] < threshod_dist:
car_found = True
else:
car_found = False
return car_found, car_id
def __find_car_box(self, detected_threshold = 0.51):
"""
Define bounding box of detected cars.
"""
box = []
for car in self.detected_cars:
if car.detected > detected_threshold:
offset = car.average_centroid
width = car.width
height = car.height
bbox0 = (int(-width+offset[0]),
int(-height+offset[1]))
bbox1 = (int(width+offset[0]),
int(height+offset[1]))
box.append((bbox0,bbox1))
return box
# Define a function to draw bounding boxes
def __draw_boxes(self, img, bboxes, color=(0, 0, 255), thick=6):
"""Draws boxes on image from a list of windows"""
# Make a copy of the image
imcopy = np.copy(img)
# Iterate through the bounding boxes
for bbox in bboxes:
# Draw a rectangle given bbox coordinates
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
# Return the image copy with boxes drawn
return imcopy