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traffic_light_detection.py
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import mycamera
import cv2
import numpy as np
import time
from gpiozero import DigitalOutputDevice
from gpiozero import PWMOutputDevice
PWMA = PWMOutputDevice(18)
AIN1 = DigitalOutputDevice(22)
AIN2 = DigitalOutputDevice(27)
PWMB = PWMOutputDevice(23)
BIN1 = DigitalOutputDevice(25)
BIN2 = DigitalOutputDevice(24)
def motor_go(speed):
AIN1.value = 0
AIN2.value = 1
PWMA.value = speed
BIN1.value = 0
BIN2.value = 1
PWMB.value = speed
def motor_back(speed):
AIN1.value = 1
AIN2.value = 0
PWMA.value = speed
BIN1.value = 1
BIN2.value = 0
PWMB.value = speed
def motor_left(speed):
AIN1.value = 1
AIN2.value = 0
PWMA.value = 0.0
BIN1.value = 0
BIN2.value = 1
PWMB.value = speed
def motor_right(speed):
AIN1.value = 0
AIN2.value = 1
PWMA.value = speed
BIN1.value = 1
BIN2.value = 0
PWMB.value = 0.0
def motor_stop():
AIN1.value = 0
AIN2.value = 1
PWMA.value = 0.0
BIN1.value = 1
BIN2.value = 0
PWMB.value = 0.0
speedSet = 0.5
classNames = {0: 'background', 1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle', 5: 'airplane', 6: 'bus',
7: 'train', 8: 'truck', 9: 'boat', 10: 'traffic light', 11: 'fire hydrant', 13: 'stop sign', 14: 'parking meter',
15: 'bench', 16: 'bird', 17: 'cat', 18: 'dog', 19: 'horse', 20: 'sheep', 21: 'cow', 22: 'elephant', 23: 'bear',
24: 'zebra', 25: 'giraffe', 27: 'backpack', 28: 'umbrella', 31: 'handbag', 32: 'tie', 33: 'suitcase', 34: 'frisbee',
35: 'skis', 36: 'snowboard', 37: 'sports ball', 38: 'kite', 39: 'baseball bat', 40: 'baseball glove', 41: 'skateboard',
42: 'surfboard', 43: 'tennis racket', 44: 'bottle', 46: 'wine glass', 47: 'cup', 48: 'fork', 49: 'knife', 50: 'spoon',
51: 'bowl', 52: 'banana', 53: 'apple', 54: 'sandwich', 55: 'orange', 56: 'broccoli', 57: 'carrot', 58: 'hot dog',
59: 'pizza', 60: 'donut', 61: 'cake', 62: 'chair', 63: 'couch', 64: 'potted plant', 65: 'bed', 67: 'dining table',
70: 'toilet', 72: 'tv', 73: 'laptop', 74: 'mouse', 75: 'remote', 76: 'keyboard', 77: 'cell phone', 78: 'microwave',
79: 'oven', 80: 'toaster', 81: 'sink', 82: 'refrigerator', 84: 'book', 85: 'clock', 86: 'vase', 87: 'scissors',
88: 'teddy bear', 89: 'hair drier', 90: 'toothbrush'}
def id_class_name(class_id, classes):
for key, value in classes.items():
if class_id == key:
return value
def main():
camera = mycamera.MyPiCamera(640, 480)
carState = "stop"
try:
model = cv2.dnn.readNetFromTensorflow('/home/pi/AI_CAR/OpencvDnn/models/frozen_inference_graph.pb',
'/home/pi/AI_CAR/OpencvDnn/models/ssd_mobilenet_v2_coco_2018_03_29.pbtxt')
while True:
keyValue = cv2.waitKey(1)
if keyValue == ord('q'):
break
elif keyValue == 82: # Up arrow key
print("go")
carState = "go"
motor_go(speedSet)
elif keyValue == 84: # Down arrow key
print("stop")
carState = "stop"
motor_stop()
elif keyValue == 81: # Left arrow key
print("left")
carState = "left"
motor_left(speedSet)
elif keyValue == 83: # Right arrow key
print("right")
carState = "right"
motor_right(speedSet)
_, image = camera.read()
image = cv2.flip(image, -1)
image_height, image_width, _ = image.shape
model.setInput(cv2.dnn.blobFromImage(image, size=(300, 300), swapRB=True))
output = model.forward()
for detection in output[0, 0, :, :]:
confidence = detection[2]
if confidence > .5:
class_id = detection[1]
class_name = id_class_name(class_id, classNames)
if class_name == 'traffic light':
box_x = int(detection[3] * image_width)
box_y = int(detection[4] * image_height)
box_width = int(detection[5] * image_width)
box_height = int(detection[6] * image_height)
traffic_light_roi = image[box_y:box_height, box_x:box_width]
hsv_roi = cv2.cvtColor(traffic_light_roi, cv2.COLOR_BGR2HSV)
# Define color ranges for red, yellow, green
lower_red1 = np.array([0, 70, 50])
upper_red1 = np.array([10, 255, 255])
lower_red2 = np.array([170, 70, 50])
upper_red2 = np.array([180, 255, 255])
lower_yellow = np.array([15, 70, 50])
upper_yellow = np.array([35, 255, 255])
lower_green = np.array([40, 70, 50])
upper_green = np.array([90, 255, 255])
# Create masks
mask_red1 = cv2.inRange(hsv_roi, lower_red1, upper_red1)
mask_red2 = cv2.inRange(hsv_roi, lower_red2, upper_red2)
mask_red = cv2.bitwise_or(mask_red1, mask_red2)
mask_yellow = cv2.inRange(hsv_roi, lower_yellow, upper_yellow)
mask_green = cv2.inRange(hsv_roi, lower_green, upper_green)
# Split the ROI into three horizontal sections
height_roi, width_roi, _ = traffic_light_roi.shape
section_height = height_roi // 3
red_section = mask_red[0:section_height, :]
yellow_section = mask_yellow[section_height:2*section_height, :]
green_section = mask_green[2*section_height:3*section_height, :]
# Calculate the number of pixels in each section
red_pixels = cv2.countNonZero(red_section)
yellow_pixels = cv2.countNonZero(yellow_section)
green_pixels = cv2.countNonZero(green_section)
# Determine the traffic light color based on the number of pixels
if red_pixels > yellow_pixels and red_pixels > green_pixels:
traffic_light_color = "red"
carState = "stop"
motor_stop()
elif yellow_pixels > red_pixels and yellow_pixels > green_pixels:
traffic_light_color = "yellow"
elif green_pixels > red_pixels and green_pixels > yellow_pixels:
traffic_light_color = "green"
carState = "go"
motor_go(speedSet)
else:
traffic_light_color = "unknown"
print(f"Traffic light color: {traffic_light_color}")
cv2.rectangle(image, (box_x, box_y), (box_width, box_height), (23, 230, 210), thickness=1)
cv2.putText(image, f"Traffic Light: {traffic_light_color}", (box_x, box_y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
else:
print(str(str(class_id) + " " + str(detection[2]) + " " + class_name))
box_x = detection[3] * image_width
box_y = detection[4] * image_height
box_width = detection[5] * image_width
box_height = detection[6] * image_height
cv2.rectangle(image, (int(box_x), int(box_y)), (int(box_width), int(box_height)), (23, 230, 210), thickness=1)
cv2.putText(image, class_name, (int(box_x), int(box_y + .05 * image_height)), cv2.FONT_HERSHEY_SIMPLEX, (.005 * image_width), (0, 0, 255))
cv2.imshow('image', image)
except KeyboardInterrupt:
pass
if __name__ == '__main__':
main()
motor_stop()
cv2.destroyAllWindows()