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tl_detection_classification_test.py
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tl_detection_classification_test.py
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'''
Script to test traffic light localization and detection
'''
import numpy as np
import cv2
import tensorflow as tf
from keras.models import load_model
from PIL import Image
import os
from matplotlib import pyplot as plt
import time
from glob import glob
cwd = os.path.dirname(os.path.realpath(__file__))
os.chdir(cwd+'/models')
from object_detection.utils import visualization_utils as vis_util
class TLClassifier(object):
def __init__(self):
self.signal_classes = ['Red', 'Green', 'Yellow']
self.signal_status = None
self.tl_box = None
os.chdir(cwd)
#keras classification model
self.cls_model = load_model('tl_model_1.h5')
#tensorflow localization/detection model
detect_model_name = 'ssd_mobilenet_v1_coco_11_06_2017'
#detect_model_name = 'ssd_inception_v2_coco_11_06_2017'
PATH_TO_CKPT = detect_model_name + '/frozen_inference_graph.pb'
# setup tensorflow graph
self.detection_graph = tf.Graph()
# configuration for possible GPU use
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# load frozen tensorflow detection model and initialize
# the tensorflow graph
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.sess = tf.Session(graph=self.detection_graph, config=config)
self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
self.scores =self.detection_graph.get_tensor_by_name('detection_scores:0')
self.classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
self.num_detections =self.detection_graph.get_tensor_by_name('num_detections:0')
# Helper function to convert image into numpy array
def load_image_into_numpy_array(self, image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
# Helper function to convert normalized box coordinates to pixels
def box_normal_to_pixel(self, box, dim):
height, width = dim[0], dim[1]
box_pixel = [int(box[0]*height), int(box[1]*width), int(box[2]*height), int(box[3]*width)]
return np.array(box_pixel)
def get_localization(self, image, visual=False):
"""Determines the locations of the traffic light in the image
Args:
image: camera image
Returns:
list of integers: coordinates [x_left, y_up, x_right, y_down]
"""
category_index={1: {'id': 1, 'name': u'person'},
2: {'id': 2, 'name': u'bicycle'},
3: {'id': 3, 'name': u'car'},
4: {'id': 4, 'name': u'motorcycle'},
5: {'id': 5, 'name': u'airplane'},
6: {'id': 6, 'name': u'bus'},
7: {'id': 7, 'name': u'train'},
8: {'id': 8, 'name': u'truck'},
9: {'id': 9, 'name': u'boat'},
10: {'id': 10, 'name': u'traffic light'},
11: {'id': 11, 'name': u'fire hydrant'},
13: {'id': 13, 'name': u'stop sign'},
14: {'id': 14, 'name': u'parking meter'}}
with self.detection_graph.as_default():
image_expanded = np.expand_dims(image, axis=0)
(boxes, scores, classes, num_detections) = self.sess.run(
[self.boxes, self.scores, self.classes, self.num_detections],
feed_dict={self.image_tensor: image_expanded})
if visual == True:
vis_util.visualize_boxes_and_labels_on_image_array(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,min_score_thresh=.2,
line_thickness=3)
plt.figure(figsize=(9,6))
plt.imshow(image)
plt.show()
boxes=np.squeeze(boxes)
classes =np.squeeze(classes)
scores = np.squeeze(scores)
cls = classes.tolist()
# Find the first occurence of traffic light detection id=10
idx = next((i for i, v in enumerate(cls) if v == 10.), None)
# If there is no detection
if idx == None:
box=[0, 0, 0, 0]
print('no detection!')
# If the confidence of detection is too slow, 0.3 for simulator
elif scores[idx]<=0.02:
box=[0, 0, 0, 0]
print('low confidence:', scores[idx])
#If there is a detection and its confidence is high enough
else:
#*************corner cases***********************************
dim = image.shape[0:2]
box = self.box_normal_to_pixel(boxes[idx], dim)
box_h = box[2] - box[0]
box_w = box[3] - box[1]
ratio = box_h/(box_w + 0.01)
# if the box is too small, 20 pixels for simulator
if (box_h <10) or (box_w<10):
box =[0, 0, 0, 0]
print('box too small!', box_h, box_w)
# if the h-w ratio is not right, 1.5 for simulator
elif (ratio<1.5):
box =[0, 0, 0, 0]
print('wrong h-w ratio', ratio)
else:
print(box)
print('localization confidence: ', scores[idx])
#****************end of corner cases***********************
self.tl_box = box
return box
def get_classification(self, image):
"""Determines the color of the traffic light in the image
Args:
image (cv::Mat): cropped image containing the traffic light
Returns:
int: ID of traffic light color (specified in styx_msgs/TrafficLight)
"""
# Resize cropped
#img_resize = cv2.resize(image, (32, 32))
img_resize = image
# Color map conversion
img_resize=cv2.cvtColor(img_resize, cv2.COLOR_BGR2RGB)
# Convert to four-dimension input as required by Keras
img_resize = np.expand_dims(img_resize, axis=0).astype('float32')
# Normalization
img_resize/=255.
# Prediction
predict = self.cls_model.predict(img_resize)
predict = np.squeeze(predict, axis =0)
# Get color classification
tl_color = self.signal_classes[np.argmax(predict)]
print(tl_color,', Classification confidence:', predict[np.argmax(predict)])
# TrafficLight message
self.signal_status = tl_color
return self.signal_status
if __name__ == '__main__':
tl_cls =TLClassifier()
os.chdir(cwd)
TEST_IMAGE_PATHS= glob(os.path.join('traffic_light_images/', '*.jpg'))
for i, image_path in enumerate(TEST_IMAGE_PATHS):
print('')
print('*************************************************')
img_full = Image.open(image_path)
img_full_np = tl_cls.load_image_into_numpy_array(img_full)
img_full_np_copy = np.copy(img_full_np)
start = time.time()
b = tl_cls.get_localization(img_full_np, visual=False)
end = time.time()
print('Localization time: ', end-start)
start = time.time()
# If there is no detection or low-confidence detection
if np.array_equal(b, np.zeros(4)):
print ('unknown')
plt.figure(figsize=(9,6))
plt.imshow(img_full_np)
plt.show()
else:
cv2.rectangle(img_full_np,(b[1],b[0]),(b[3],b[2]),(0,255,0),2)
plt.figure(figsize=(9,6))
plt.imshow(img_full_np)
plt.show()
img_np = cv2.resize(img_full_np_copy[b[0]:b[2], b[1]:b[3]], (32, 32))
plt.figure(figsize=(5,5))
plt.imshow(img_np)
plt.show()
tl_cls.get_classification(img_np)
end = time.time()