AdroitAnandAI

img = cv2.imread("car.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, gray = cv2.threshold(gray, 250,255,0)

# applying different thresholding techniques on the input image
# all pixels value above 120 will be set to 255
ret, thresh2 = cv2.threshold(img, 120, 255, cv2.THRESH_BINARY_INV)
gray = cv2.cvtColor(thresh2, cv2.COLOR_BGR2GRAY)
contours, _ = cv2.findContours(gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

AdroitAnandAI

  # Detections contains bounding boxes using object detection model
  boxcount = 0
  depths = []
  bboxMidXs = []
  bboxMidYs = []
  # This is computed to reflect real distance during initial camera calibration
  scalingFactor = 1000 
  # Depth scaling factor is based on one-time cam calibration 
  for detection in detections:

AdroitAnandAI

# Sensor Fusion happens at Node 2
def on_message(client, userdata, msg):

    word = msg.payload.decode()

    # objAttributes contains label,
    # theta min and max separated by |
    objAttributes = word.split('|')

    now = time.localtime()

AdroitAnandAI

# This code builds the shape context descriptor, which is the core of our alphanumeral comparison
# https://github.com/AdroitAnandAI/Multilingual-Text-Inversion-Detection-of-Scanned-Images
        # points represents the edge shape
        t_points = len(points)
        # getting euclidian distance
        r_array = cdist(points, points)
        # for rotation invariant feature
        am = r_array.argmax()
        max_points = [am / t_points, am % t_points]
        # normalizing

AdroitAnandAI

	# loop over the set of tracked points
	for i in range(1, len(pts)):
		# if either of the tracked points are None, ignore them
		if pts[i - 1] is None or pts[i] is None:
			continue

		# otherwise, compute the thickness of the line and draw the connecting lines
		thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)
		cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)

AdroitAnandAI

for detection in detections:

    if detection.score > threshold:

        class_id = int(detection.id)-1

        # Potential Objects: person, bicycle, car, bus, 
        # truck, traffic light, street sign, stop sign
        if class_id not in [0, 1, 2, 3, 5, 7, 9, 11, 12]:

AdroitAnandAI

def getObjectDistance (angle_min, angle_max):

    minDist = 0
    lidar = RPLidar(None, PORT_NAME)

    try:
            for scan in lidar_scans(lidar):

                for (_, angle, distance) in scan:
                    scan_data[min([359, floor(angle)])] = distance

AdroitAnandAI

# Code to fit the inverse sigmoid curve to tail end of signal
def sigmoid(x, L ,x0, k, b):
  y = L / (1 + np.exp(k*(x-x0)))+b
  return (y)

def isCurveSigmoid(pixelCounts, count):
  try:
    xIndex = len(pixelCounts)
    p0 = [max(pixelCounts), np.median(xIndex),1,min(pixelCounts)] # this is an mandatory initial guess
    popt, pcov = curve_fit(sigmoid, list(range(xIndex)), pixelCounts, p0, method='lm', maxfev=5000)

AdroitAnandAI

# At the MQTT Transmission side

    data2Transmit = np.array([x, y, theta])

    # Map which is saved as a bytearray is appended at the end
    if scan_count % 30 == 0:
        client.publish("safetycam/topic/slamviz", \\
                           data2Transmit.tobytes() + mapbytes)
            
# At the MQTT receiving side

AdroitAnandAI

## Optimized implementation of Graph SLAM.

## slam takes in 6 arguments and returns mu, 
## mu is the entire path traversed by a robot (all x,y poses) *and* all landmarks locations
def slam(data, N, num_landmarks, world_size, motion_noise, measurement_noise):
    
    coefficients = [1, -1, -1, 1]
    
    # initialize the constraints
    initial_omega_1, initial_xi_1, initial_omega_2, initial_xi_2 = \\