def init_new_vehicles(labels):
# Iterate through labels in integrated heatmap
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
xleft = np.min(nonzerox)
ytop = np.min(nonzeroy)
xright = np.max(nonzerox)
ybot = np.max(nonzeroy)
# Search over all vehicles for best overlap to associate
max_iou = -1.0
for v in vehicle_list:
if v.age_predicted < max_age_predicted:
centerx_v = v.fit_centroid_x
xleft_v = np.int(centerx_v - 0.5 * v.fit_width)
xright_v = np.int(centerx_v + 0.5 * v.fit_width)
centery_v = v.fit_centroid_y
ytop_v = np.int(centery_v - 0.5 * v.fit_height)
ybot_v = np.int(centery_v + 0.5 * v.fit_height)
# Calc intersection over union:
area_det = (xright - xleft + 1) * (ybot - ytop + 1)
area_v = (xright_v - xleft_v + 1) * (ybot_v - ytop_v + 1)
intersection = max(
0,
min(xright, xright_v) - max(xleft, xleft_v)) * max(
0,
min(ybot, ybot_v) - max(ytop, ytop_v))
union = area_det + area_v - intersection
iou = intersection / union
if iou > max_iou:
max_iou = iou
# If overlap is small -> init new track
if max_iou < min_iou:
new_vehicle = Vehicle()
new_vehicle.centroid_x = 0.5 * (xleft + xright)
new_vehicle.centroid_y = 0.5 * (ytop + ybot)
new_vehicle.width = xright - xleft
new_vehicle.height = ybot - ytop
new_vehicle.fit_centroid_x = new_vehicle.centroid_x
new_vehicle.fit_centroid_y = new_vehicle.centroid_y
new_vehicle.fit_width = new_vehicle.width
new_vehicle.fit_height = new_vehicle.height
new_vehicle.allx.extend(np.arange(xleft, xright + 1))
new_vehicle.ally.extend(np.arange(ytop, ybot + 1))
new_vehicle.detected = True
new_vehicle.age_predicted = 0
vehicle_list.append(new_vehicle)
