def process_img(image):
original_image = image
# processed_img = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
processed_img = cv2.Canny(original_image, threshold1=200, threshold2=300)
processed_img = roi(processed_img, [vertices])
processed_img = cv2.GaussianBlur(processed_img, (5,5), 0)
lines = cv2.HoughLinesP(processed_img, 1, np.pi/180, 180, np.array([]), 100, 5)
# draw_lines(processed_img, lines)
m1 = 0
m2 = 0
try:
l1, l2, m1, m2 = draw_lanes(original_image, lines)
cv2.line(original_image, (l1[0], l1[1]), (l1[2], l1[3]), [0, 255, 0], 30)
cv2.line(original_image, (l2[0], l2[1]), (l2[2], l2[3]), [0, 255, 0], 30)
except Exception as e:
print(str(e))
pass
try:
for coords in lines:
coords = coords[0]
try:
cv2.line(processed_img, (coords[0], coords[1]), (coords[2], coords[3]), [255, 0, 0], 3)
except Exception as e:
print(str(e))
except Exception as e:
pass
return processed_img, original_image, m1, m2
def process_img(image):
original_image = image
# convert to gray
processed_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# edge detection
processed_img = cv2.Canny(processed_img, threshold1=200, threshold2=300)
processed_img = cv2.GaussianBlur(processed_img, (5, 5), 0)
vertices = np.array([
[10, 500],
[10, 300],
[300, 200],
[500, 200],
[800, 300],
[800, 500],
], np.int32)
processed_img = roi(processed_img, [vertices])
# more info: http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.html
# rho theta thresh min length, max gap:
lines = cv2.HoughLinesP(processed_img, 1, np.pi / 180, 180, 20, 15)
m1 = 0
m2 = 0
try:
l1, l2, m1, m2 = draw_lanes(original_image, lines)
cv2.line(original_image, (l1[0], l1[1]), (l1[2], l1[3]), [0, 255, 0],
30)
cv2.line(original_image, (l2[0], l2[1]), (l2[2], l2[3]), [0, 255, 0],
30)
except Exception as e:
print(str(e))
pass
try:
for coords in lines:
coords = coords[0]
try:
cv2.line(processed_img, (coords[0], coords[1]),
(coords[2], coords[3]), [255, 0, 0], 3)
except Exception as e:
print(str(e))
except Exception as e:
pass
return processed_img, original_image, m1, m2
def process_img(image):
original_image = image
# convert to gray
processed_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# edge detection
processed_img = cv2.Canny(processed_img, threshold1 = 200, threshold2=300)
processed_img = cv2.GaussianBlur(processed_img,(5,5),0)
vertices = np.array([[10,500],[10,300],[300,200],[500,200],[800,300],[800,500],
], np.int32)
processed_img = roi(processed_img, [vertices])
# more info: http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.html
# rho theta thresh min length, max gap:
lines = cv2.HoughLinesP(processed_img, 1, np.pi/180, 180, 20, 15)
m1 = 0
m2 = 0
try:
l1, l2, m1,m2 = draw_lanes(original_image,lines)
cv2.line(original_image, (l1[0], l1[1]), (l1[2], l1[3]), [0,255,0], 30)
cv2.line(original_image, (l2[0], l2[1]), (l2[2], l2[3]), [0,255,0], 30)
except Exception as e:
print(str(e))
pass
try:
for coords in lines:
coords = coords[0]
try:
cv2.line(processed_img, (coords[0], coords[1]), (coords[2], coords[3]), [255,0,0], 3)
except Exception as e:
print(str(e))
except Exception as e:
pass
return processed_img,original_image, m1, m2
processed_img = cv2.Canny(processed_img, threshold1 = 200, threshold2=300)
processed_img = cv2.GaussianBlur(processed_img,(5,5),0)
vertices = np.array([[10,500],[10,300],[300,200],[500,200],[800,300],[800,500],
], np.int32)
processed_img = roi(processed_img, [vertices])
# more info: http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.html
# rho theta thresh min length, max gap:
lines = cv2.HoughLinesP(processed_img, 1, np.pi/180, 180, 20, 15)
m1 = 0
m2 = 0
try:
l1, l2, m1,m2 = draw_lanes(original_image,lines)
cv2.line(original_image, (l1[0], l1[1]), (l1[2], l1[3]), [0,255,0], 30)
cv2.line(original_image, (l2[0], l2[1]), (l2[2], l2[3]), [0,255,0], 30)
except Exception as e:
print(str(e))
pass
try:
for coords in lines:
coords = coords[0]
try:
cv2.line(processed_img, (coords[0], coords[1]), (coords[2], coords[3]), [255,0,0], 3)
except Exception as e:
print(str(e))
except Exception as e:
while(True):
tic = time.time()
# First Try - using PIL ImageGrab method - gives less frame rate
# orig_screengrab = np.array(ImageGrab.grab(bbox=(0,40,640,520)))
# Grabbing + Displaying takes around 0.15 secs per frame of size(480,640)
# Capture Region based on resolution
# 40px to allow for the window title bar, bottom = 480 + 40 = 520
capture_region = (0,40,640,520)
orig_screengrab = grab_screen(capture_region)
# Grabbing + Displaying takes around 0.03 secs per frame of size(480,640)
#processed_image, original_image = process_screengrab(orig_screengrab, capture_region)
processed_image, image_gray, image_with_lanes_bgr = draw_lanes(orig_screengrab, capture_region)
#print(processed_screengrab.shape)
lane_masked_image = find_lane_markers(orig_screengrab)
cv2.imshow('Original ScreeGrab', cv2.cvtColor(orig_screengrab, cv2.COLOR_RGB2BGR))
# output of find_lane_markers
cv2.imshow('Lane Detection', lane_masked_image)
# Draw Lane outputs
cv2.imshow('Grayscale Image ', image_gray)
cv2.imshow('Processed Screengrab',processed_image)
cv2.imshow('Processed with lanes',image_with_lanes_bgr)
print('Grabbing + Displaying took {} seconds'.format(time.time() - tic))
if cv2.waitKey(25) & 0xFF==ord('q'):
cv2.destroyAllWindows()
break
import cv2
import sys
import os
import time
from draw_lanes import draw_lanes
from get_curve import get_curve
sys.path.append("/Lane_Detection")
from lane_detection import lane_detection
#, get_curve, draw_lanes
import matplotlib.pyplot as plt
img = cv2.imread('/home/hackathon/Curved-Lane-Lines/test_images/test3.jpg')
#plt.imshow(img)
size = (100, 100)
curves, lanes, ploty, out_img = lane_detection(img, size)
#curverad =get_curve(out_img, curves[0], curves[2])
#lane_curve = np.mean([curverad[0], curverad[1]])
startx = time.time()
img_resized = cv2.resize(img, (100, 100))
img_n = draw_lanes(img_resized, curves[0], curves[2])
print("\n draw_lanes time is : " + str(time.time() - startx))
f, (ax1) = plt.subplots(1, 1, figsize=(20, 10))
ax1.imshow(img_n)
ax1.set_title('Original Image', fontsize=30)