"""Inference demo of directional point detector."""
import math
import cv2 as cv
import numpy as np
import torch
from torchvision.transforms import ToTensor
import config
from data import get_predicted_points, pair_marking_points, calc_point_squre_dist, pass_through_third_point
from model import DirectionalPointDetector
from util import Timer
def plot_points(image, pred_points):
"""Plot marking points on the image."""
if not pred_points:
return
height = image.shape[0]
width = image.shape[1]
for confidence, marking_point in pred_points:
p0_x = width * marking_point.x - 0.5
p0_y = height * marking_point.y - 0.5
cos_val = math.cos(marking_point.direction)
sin_val = math.sin(marking_point.direction)
p1_x = p0_x + 50*cos_val
p1_y = p0_y + 50*sin_val
p2_x = p0_x - 50*sin_val
p2_y = p0_y + 50*cos_val
p3_x = p0_x + 50*sin_val
p3_y = p0_y - 50*cos_val
p0_x = int(round(p0_x))
p0_y = int(round(p0_y))
p1_x = int(round(p1_x))
p1_y = int(round(p1_y))
p2_x = int(round(p2_x))
p2_y = int(round(p2_y))
cv.line(image, (p0_x, p0_y), (p1_x, p1_y), (0, 0, 255), 2)
cv.putText(image, str(confidence), (p0_x, p0_y),
cv.FONT_HERSHEY_PLAIN, 1, (0, 0, 0))
if marking_point.shape > 0.5:
cv.line(image, (p0_x, p0_y), (p2_x, p2_y), (0, 0, 255), 2)
else:
p3_x = int(round(p3_x))
p3_y = int(round(p3_y))
cv.line(image, (p2_x, p2_y), (p3_x, p3_y), (0, 0, 255), 2)
def plot_slots(image, pred_points, slots):
"""Plot parking slots on the image."""
if not pred_points or not slots:
return
marking_points = list(list(zip(*pred_points))[1])
height = image.shape[0]
width = image.shape[1]
for slot in slots:
point_a = marking_points[slot[0]]
point_b = marking_points[slot[1]]
p0_x = width * point_a.x - 0.5
p0_y = height * point_a.y - 0.5
p1_x = width * point_b.x - 0.5
p1_y = height * point_b.y - 0.5
vec = np.array([p1_x - p0_x, p1_y - p0_y])
vec = vec / np.linalg.norm(vec)
distance = calc_point_squre_dist(point_a, point_b)
if config.VSLOT_MIN_DIST <= distance <= config.VSLOT_MAX_DIST:
separating_length = config.LONG_SEPARATOR_LENGTH
elif config.HSLOT_MIN_DIST <= distance <= config.HSLOT_MAX_DIST:
separating_length = config.SHORT_SEPARATOR_LENGTH
p2_x = p0_x + height * separating_length * vec[1]
p2_y = p0_y - width * separating_length * vec[0]
p3_x = p1_x + height * separating_length * vec[1]
p3_y = p1_y - width * separating_length * vec[0]
p0_x = int(round(p0_x))
p0_y = int(round(p0_y))
p1_x = int(round(p1_x))
p1_y = int(round(p1_y))
p2_x = int(round(p2_x))
p2_y = int(round(p2_y))
p3_x = int(round(p3_x))
p3_y = int(round(p3_y))
cv.line(image, (p0_x, p0_y), (p1_x, p1_y), (255, 0, 0), 2)
cv.line(image, (p0_x, p0_y), (p2_x, p2_y), (255, 0, 0), 2)
cv.line(image, (p1_x, p1_y), (p3_x, p3_y), (255, 0, 0), 2)
def preprocess_image(image):
"""Preprocess numpy image to torch tensor."""
if image.shape[0] != 512 or image.shape[1] != 512:
image = cv.resize(image, (512, 512))
return torch.unsqueeze(ToTensor()(image), 0)
def detect_marking_points(detector, image, thresh, device):
"""Given image read from opencv, return detected marking points."""
prediction = detector(preprocess_image(image).to(device))
return get_predicted_points(prediction[0], thresh)
def inference_slots(marking_points):
"""Inference slots based on marking points."""
num_detected = len(marking_points)
slots = []
for i in range(num_detected - 1):
for j in range(i + 1, num_detected):
point_i = marking_points[i]
point_j = marking_points[j]
# Step 1: length filtration.
distance = calc_point_squre_dist(point_i, point_j)
if not (config.VSLOT_MIN_DIST <= distance <= config.VSLOT_MAX_DIST
or config.HSLOT_MIN_DIST <= distance <= config.HSLOT_MAX_DIST):
continue
# Step 2: pass through filtration.
if pass_through_third_point(marking_points, i, j):
continue
result = pair_marking_points(point_i, point_j)
if result == 1:
slots.append((i, j))
elif result == -1:
slots.append((j, i))
return slots
def detect_video(detector, device, args):
"""Demo for detecting video."""
timer = Timer()
input_video = cv.VideoCapture(args.video)
frame_width = int(input_video.get(cv.CAP_PROP_FRAME_WIDTH))
frame_height = int(input_video.get(cv.CAP_PROP_FRAME_HEIGHT))
output_video = cv.VideoWriter()
if args.save:
output_video.open('record.avi', cv.VideoWriter_fourcc(*'XVID'),
input_video.get(cv.CAP_PROP_FPS),
(frame_width, frame_height), True)
frame = np.empty([frame_height, frame_width, 3], dtype=np.uint8)
while input_video.read(frame)[0]:
timer.tic()
pred_points = detect_marking_points(
detector, frame, args.thresh, device)
slots = None
if pred_points and args.inference_slot:
marking_points = list(list(zip(*pred_points))[1])
slots = inference_slots(marking_points)
timer.toc()
plot_points(frame, pred_points)
plot_slots(frame, pred_points, slots)
cv.imshow('demo', frame)
cv.waitKey(1)
if args.save:
output_video.write(frame)
print("Average time: ", timer.calc_average_time(), "s.")
input_video.release()
output_video.release()
def detect_image(detector, device, args):
"""Demo for detecting images."""
timer = Timer()
while True:
image_file = input('Enter image file path: ')
image = cv.imread(image_file)
timer.tic()
pred_points = detect_marking_points(
detector, image, args.thresh, device)
slots = None
if pred_points and args.inference_slot:
marking_points = list(list(zip(*pred_points))[1])
slots = inference_slots(marking_points)
timer.toc()
plot_points(image, pred_points)
plot_slots(image, pred_points, slots)
cv.imshow('demo', image)
cv.waitKey(1)
if args.save:
cv.imwrite('save.jpg', image, [int(cv.IMWRITE_JPEG_QUALITY), 100])
def inference_detector(args):
"""Inference demo of directional point detector."""
args.cuda = not args.disable_cuda and torch.cuda.is_available()
device = torch.device('cuda:' + str(args.gpu_id) if args.cuda else 'cpu')
torch.set_grad_enabled(False)
dp_detector = DirectionalPointDetector(
3, args.depth_factor, config.NUM_FEATURE_MAP_CHANNEL).to(device)
dp_detector.load_state_dict(torch.load(args.detector_weights))
dp_detector.eval()
if args.mode == "image":
detect_image(dp_detector, device, args)
elif args.mode == "video":
detect_video(dp_detector, device, args)
if __name__ == '__main__':
inference_detector(config.get_parser_for_inference().parse_args())
