def street_crosser():
play_sound_notification("look_right")
time.sleep(1)
approximation(5)
play_sound_notification("look_left")
time.sleep(1)
approximation(5)
play_sound_notification("all_clear")
from gps_tracker import play_sound_notification
play_sound_notification("welcome")
print('PASSSSSSSSSOOOOOOOOOOOOOOOOUUUUUUUUUUUUUUUUUU')
import gps_tracker
import detector
import RPi.GPIO as GPIO
import time
GPIO.cleanup()
gps_tracker.play_sound_notification('welcome')
while True:
try:
current_location = gps_tracker.get_current_location()
if current_location != (0.0, 0.0):
break
except:
continue
gps_tracker.play_sound_notification("ready")
street_crosser = 13
GPIO.setmode(GPIO.BCM)
GPIO.setup(street_crosser, GPIO.IN)
while True:
if not(GPIO.input(street_crosser)):
time.sleep(0.5)
detector.street_crosser()
GPIO.cleanup()
break
import gps_tracker
import detector
import RPi.GPIO as GPIO
import time
street_crosser = 13
GPIO.setmode(GPIO.BCM)
GPIO.setup(street_crosser, GPIO.IN)
while GPIO.input(street_crosser):
continue
gps_tracker.play_sound_notification('welcome')
while True:
try:
current_location = gps_tracker.get_current_location()
if current_location != (0.0, 0.0):
break
except:
continue
# Google MAPS Direction API Request
org = 'Lanches Mac Fei, Av. Humberto A C Branco, 3972 - Assunção, São Bernardo do Campo - SP, 09850-305'.replace(' ',
'+')
destination = '-23.7259703,-46.5797997'
travel_mode = 'walking'
resp = gps_tracker.path_finder(org, destination, travel_mode, True)
steps = resp
print(steps)
def approximation(limit):
detect = 0
MODEL_NAME = 'obj_detection_tflite'
GRAPH_NAME = 'detect.tflite'
LABELMAP_NAME = 'labelmap.txt'
min_conf_threshold = 0.6
imW, imH = 1280, 720
pkg = importlib.util.find_spec('tflite_runtime')
if pkg:
from tflite_runtime.interpreter import Interpreter
else:
from tensorflow.lite.python.interpreter import Interpreter
CWD_PATH = os.getcwd()
PATH_TO_CKPT = os.path.join(CWD_PATH, MODEL_NAME, GRAPH_NAME)
PATH_TO_LABELS = os.path.join(CWD_PATH, MODEL_NAME, LABELMAP_NAME)
with open(PATH_TO_LABELS, 'r') as f:
labels = [line.strip() for line in f.readlines()]
if labels[0] == '???':
del (labels[0])
interpreter = Interpreter(model_path=PATH_TO_CKPT)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]
floating_model = (input_details[0]['dtype'] == np.float32)
input_mean = 127.5
input_std = 127.5
pi_camera = PiCamera(resolution=(imW, imH), framerate=30).start()
time.sleep(1)
p_height = 0
p_width = 0
detections = 0
approximation_detected = False
timer_mark = timer_start = time.time()
while timer_mark - timer_start < limit:
print(timer_mark - timer_start)
frame1 = pi_camera.read()
frame = frame1.copy()
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb, (width, height))
input_data = np.expand_dims(frame_resized, axis=0)
if floating_model:
input_data = (np.float32(input_data) - input_mean) / input_std
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
boxes = interpreter.get_tensor(output_details[0]['index'])[0] # Bounding box coordinates of detections objects
classes = interpreter.get_tensor(output_details[1]['index'])[0] # Class index of detections objects
scores = interpreter.get_tensor(output_details[2]['index'])[0] # Confidence of detections objects
for i in range(len(scores)):
if (scores[i] > min_conf_threshold) and (scores[i] <= 1.0):
y_min = int(max(1, (boxes[i][0] * imH)))
x_min = int(max(1, (boxes[i][1] * imW)))
y_max = int(min(imH, (boxes[i][2] * imH)))
x_max = int(min(imW, (boxes[i][3] * imW)))
object_name = labels[int(classes[i])]
if object_name == 'car' or object_name == 'bus' or object_name == 'truck':
detections += 1
if (y_max - y_min) > p_height * 1.15 or (x_max - x_min) > p_width * 1.15\
and detections > 1:
play_sound_notification("waiting")
limit += 3
p_height = y_max - y_min
p_width = x_max - x_min
timer_mark = time.time()
cv2.destroyAllWindows()
pi_camera.stop()