from PIL import ImageGrab
import time
import matplotlib.pyplot as plt
from scipy.misc import imresize
from calibration_utils import calibrate_camera, undistort
from binarization_utils import binarize
from perspective_utils import birdeye
from line_utils import get_fits_by_sliding_windows, draw_back_onto_the_road, Line, get_fits_by_previous_fits
import imageio
imageio.plugins.ffmpeg.download()
from moviepy.editor import VideoFileClip
import numpy as np
from globals import xm_per_pix, time_window
processed_frames = 0 # counter of frames processed (when processing video)
line_lt = Line(buffer_len=time_window) # line on the left of the lane
line_rt = Line(buffer_len=time_window) # line on the right of the lane
def prepare_out_blend_frame(blend_on_road, img_binary, img_birdeye, img_fit,
line_lt, line_rt, offset_meter):
"""
Prepare the final pretty pretty output blend, given all intermediate pipeline images
:param blend_on_road: color image of lane blend onto the road
:param img_binary: thresholded binary image
:param img_birdeye: bird's eye view of the thresholded binary image
:param img_fit: bird's eye view with detected lane-lines highlighted
:param line_lt: detected left lane-line
:param line_rt: detected right lane-line
:param offset_meter: offset from the center of the lane
def main():
logging.basicConfig(level=logging.DEBUG)
cap = cv2.VideoCapture('footage/7_edit.avi')
'''
cap = cv2.VideoCapture(1)
cap.set(cv2.CAP_PROP_AUTO_EXPOSURE, 0.25)
cap.set(cv2.CAP_PROP_EXPOSURE, 0.01)
cap.set(cv2.CAP_PROP_FRAME_WIDTH,1280)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT,720)
'''
DIM = (640, 480)
K = np.array([[359.0717640266508, 0.0, 315.08914578097387],
[0.0, 358.06497428501837, 240.75242680088732],
[0.0, 0.0, 1.0]])
D = np.array([[-0.041705903204711826], [0.3677107787593379],
[-1.4047363783373128], [1.578157237454529]])
profile = (DIM, K, D)
CC = CameraCalibration(profile)
yellow_HSV_th_min = np.array([0, 70, 70])
yellow_HSV_th_max = np.array([50, 255, 255])
line_lt = Line(buffer_len=time_window) # line on the left of the lane
line_rt = Line(buffer_len=time_window) # line on the right of the lane
processed_frames = 0
### traffic light detector setup ###
'''
red_bound = ([0,0,0], [255,255,360])
green_bound = ([0,0,0], [255,255,360])
color_bounds = {'red':red_bound, 'green':green_bound}
reference_images = []
reference_paths = glob('./reference/*.jpg')
for path in reference_paths:
reference_images.append(cv2.imread(path))
TLD = TrafficLightDetector(reference_images, color_bounds)
'''
while cap.isOpened():
ret, frame = cap.read()
### traffic light detection
# TODO: replace placeholder values
'''
while True:
state = TLD.get_state(frame)
logging.debug('traffic light state:', str(state))
if state == 'green':
comm.write_serial_message('s30')
break
'''
frame = CC.undistort(frame)
### calibration ###
### crop to ROI ###
### perspective transform ###
img, warped = debug_roi(frame, None, None)
### binarize frame ###
### get steering angle v1 ###
'''
_, lines = pathfinder.get_line_segments(warped)
grey = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
turn_angle = pathfinder.compute_turn_angle(grey)
print('turn angle:', turn_angle)
cv2.imshow('lines', lines)
'''
### get steering angle v2 ###
#mask, res = filter_hsv_colour(img, yellow_HSV_th_max, yellow_HSV_th_min)
img_binary = binarization_utils.binarize(warped, verbose=False)
cv2.imshow('img binary', img_binary)
### contours ###
_, contours, hierarchy = cv2.findContours(img_binary,
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
img_binary_colour = cv2.cvtColor(img_binary, cv2.COLOR_GRAY2BGR)
if len(contours) > 0:
for cnt in contours:
if cnt.size >= 5:
epsilon = 0.1 * cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, epsilon, True)
ellipse = cv2.fitEllipse(cnt)
cv2.ellipse(img_binary_colour, ellipse, (255, 0, 0), 5)
rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(img_binary_colour, [box], 0, (0, 0, 255),
5)
### basic pathfinder ###
#img_binary_colour = img_binary_colour[:][100:]
_, lines = pathfinder.get_line_segments(img_binary_colour)
turn_angle = pathfinder.compute_turn_angle(img_binary_colour)
print('turn angle:', turn_angle)
turn_angle += 90
turn_angle_message = str('a%d' % int(turn_angle))
comm.write_serial_message(turn_angle_message)
#comm.write_serial_message('s50')
cv2.putText(lines, str(int(turn_angle)), (200, 450),
cv2.FONT_HERSHEY_SIMPLEX, 4, (0, 255, 0), 4, cv2.LINE_AA)
cv2.imshow('lines', lines)
'''
keep_state = False
if processed_frames > 0 and keep_state and line_lt.detected and line_rt.detected:
line_lt, line_rt, img_fit = line_utils.get_fits_by_previous_fits(img_binary, line_lt, line_rt, verbose=False)
else:
line_lt, line_rt, img_fit = line_utils.get_fits_by_sliding_windows(img_binary, line_lt, line_rt, n_windows=9, verbose=False)
offset_meter = compute_offset_from_center(line_lt, line_rt, frame_width=frame.shape[1])
'''
#print(offset_meter)
#Minv = np.zeros(shape=(3, 3))
# draw the surface enclosed by lane lines back onto the original frame
#blend_on_road = line_utils.draw_back_onto_the_road(img, Minv, line_lt, line_rt, keep_state)
#cv2.imshow('asfdfd', blend_on_road)
# stitch on the top of final output images from different steps of the pipeline
#blend_output = prepare_out_blend_frame(blend_on_road, img_binary, warped, img_fit, line_lt, line_rt, offset_meter)
#processed_frames += 1
#cv2.imshow('mask', mask)
#cv2.imshow('res', res)
cv2.imshow('frame', frame)
cv2.imshow('warped', warped)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
import os
import matplotlib.pyplot as plt
from calibration_utils import calibrate_camera, undistort
from threshold import binarize, binarize_with_threshold
from perspective_transform import perspective_transform
import pickle
import glob
from line_utils import Line, get_fits_by_sliding_windows, get_fits_by_previous_fits, compute_offset_from_center
from globals import *
from visualization import final_viz, prepare_out_blend_frame
import argparse
from moviepy.editor import VideoFileClip
#global variables
processed_frames = 0
line_lt = Line(buffer_len=window_size) # line on the left of the lane
line_rt = Line(buffer_len=window_size) # line on the right of the lane
def process_pipeline(frame, keep_state=True):
"""
Apply whole lane detection pipeline to an input color frame.
:param frame: input color frame
:param keep_state: if True, lane-line state is conserved (this permits to average results)
:return: output blend with detected lane overlaid
"""
global line_lt, line_rt, processed_frames
# step 2
# TODO 4: call undistort funcction and pass it to img_undistorted
(60, 60), font, 1, (255, 255, 255), 2)
cv2.putText(blend_on_road,
'Offset from center: {:.02f}m'.format(offset_meter), (60, 90),
font, 1, (255, 255, 255), 2)
processed_frames += 1
return blend_on_road
if __name__ == '__main__':
# step 1: calibrate the camera
mtx, dist = calibrate('camera_cal')
mode = "video"
processed_frames = 0
line_lt, line_rt = Line(buffer_len=10), Line(buffer_len=10)
if mode == "image":
for test_img in glob.glob('test_images/*.jpg'):
img = mpimg.imread(test_img)
img_out = process_pipeline(img, True)
plt.imshow(img_out)
plt.show()
elif mode == "video":
selector = 'project'
clip = VideoFileClip(
'{}_video.mp4'.format(selector)).fl_image(process_pipeline)
clip.write_videofile('out_{}.mp4'.format(selector), audio=False)