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image_gen.py
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import numpy as np
import cv2
import pickle
import glob
from tracker import Tracker
dist_pickle = pickle.load(open('camera_cal/calibration_pickle.p', 'rb'))
mtx = dist_pickle['mtx']
dist = dist_pickle['dist']
def abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(0, 255)):
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
binary_output = np.zeros_like(scaled_sobel)
binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
return binary_output
def mag_thresh(img, sobel_kernel=3, thresh=(0, 255)):
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sobel_x = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))
sobel_y = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))
gradmag = np.sqrt(sobel_x**2 + sobel_y**2)
gradmag = (gradmag/np.max(gradmag)*255).astype(np.uint8)
binary_output = np.zeros_like(gradmag)
binary_output[(gradmag >= thresh[0]) & (gradmag <= thresh[1])] = 1
return binary_output
def dir_thresh(img, sobel_kernel=3, thresh=(0, np.pi/2)):
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sobel_x = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))
sobel_y = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))
with np.errstate(divide='ignore', invalid='ignore'):
absgraddir = np.absolute(np.arctan(sobel_y/sobel_x))
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
return binary_output
def color_thresh(img, sthresh=(0, 255), vthresh=(0, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:, :, 2]
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= sthresh[0]) & (s_channel <= sthresh[1])] = 1
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
v_channel = hsv[:, :, 2]
v_binary = np.zeros_like(v_channel)
v_binary[(v_channel >= vthresh[0]) & (v_channel <= vthresh[1])] = 1
output = np.zeros_like(s_channel)
output[(s_binary == 1) & (v_binary == 1)] = 1
return output
def window_mask(width, height, img_ref, center, level):
output = np.zeros_like(img_ref)
output[int(img_ref.shape[0]-(level+1)*height):int(img_ref.shape[0]-level*height),max(0,int(center-width/2)):min(int(center+width/2),img_ref.shape[1])] = 1
return output
if __name__ == '__main__':
images = glob.glob('./solution/first_frame*.jpg')
for idx, fname in enumerate(images):
img = cv2.imread(fname)
img = cv2.undistort(img, mtx, dist, None, mtx)
preprocess_image = np.zeros_like(img[:,:,0])
gradx = abs_sobel_thresh(img, orient='x', thresh=(12, 255))
grady = abs_sobel_thresh(img, orient='y', thresh=(25, 255))
c_binary = color_thresh(img, sthresh=(100, 255), vthresh=(50, 255))
preprocess_image[((gradx == 1) & (grady == 1) | (c_binary == 1))] = 255
img_size = (img.shape[1], img.shape[0])
bot_width = .76
mid_width = .08
height_pct = .62
bottom_trim = .935
src = np.float32([
[img.shape[1]*(.5-mid_width/2), img.shape[0]*height_pct],
[img.shape[1]*(.5+mid_width/2), img.shape[0]*height_pct],
[img.shape[1]*(.5+bot_width/2), img.shape[0]*bottom_trim],
[img.shape[1]*(.5-bot_width/2), img.shape[0]*bottom_trim],
])
offset = img.shape[1]*.25
dst = np.float32([
[offset, 0],
[img.shape[1]-offset, 0],
[img.shape[1]-offset, img.shape[0]],
[offset, img.shape[0]]
])
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
warped = cv2.warpPerspective(preprocess_image, M, img_size, flags=cv2.INTER_LINEAR)
write_name = f'./solution/warped_first_frame_old_soln.jpg'
cv2.imwrite(write_name, warped)
window_width = 25
window_height = 80
curve_centers = Tracker(Mywindow_width=window_width, Mywindow_height=window_height, Mymargin=25, My_ym=10/720, My_xm=4/384, Mysmooth_factor=15)
window_centroids = curve_centers.find_window_centroids(warped)
l_points = np.zeros_like(warped)
r_points = np.zeros_like(warped)
leftx = []
rightx = []
for level in range(len(window_centroids)):
leftx.append(window_centroids[level][0])
rightx.append(window_centroids[level][1])
l_mask = window_mask(window_width, window_height, warped, window_centroids[level][0], level)
r_mask = window_mask(window_width, window_height, warped, window_centroids[level][1], level)
l_points[(l_points == 255) | (l_mask == 1)] = 255
r_points[(r_points == 255) | (r_mask == 1)] = 255
# Draw
# template = np.array(r_points+l_points, np.uint8)
# zero_channel=np.zeros_like(template)
# template = np.array(cv2.merge((zero_channel, template, zero_channel)), np.uint8)
# warpage = np.array(cv2.merge((warped, warped, warped)), np.uint8)
# result = cv2.addWeighted(warpage, 1, template, 0.5, 0.0)
# result = warped
yvals = range(0, warped.shape[0])
res_yvals = np.arange(warped.shape[0]-(window_height/2), 0, -window_height)
left_fit = np.polyfit(res_yvals, leftx, 2)
left_fitx = left_fit[0]*yvals*yvals + left_fit[1]*yvals + left_fit[2]
left_fitx = np.array(left_fitx, np.int32)
right_fit = np.polyfit(res_yvals, rightx, 2)
right_fitx = right_fit[0]*yvals*yvals + right_fit[1]*yvals + right_fit[2]
right_fitx = np.array(right_fitx, np.int32)
left_lane = np.array(list(zip(np.concatenate((left_fitx-window_width/2,left_fitx[::-1]+window_width/2), axis=0),
np.concatenate((yvals, yvals[::-1]), axis=0))), np.int32)
right_lane = np.array(list(zip(np.concatenate((right_fitx-window_width/2,right_fitx[::-1]+window_width/2), axis=0),
np.concatenate((yvals, yvals[::-1]), axis=0))), np.int32)
middle_marker = np.array(list(zip(np.concatenate((right_fitx-window_width/2,right_fitx[::-1]+window_width/2), axis=0),
np.concatenate((yvals, yvals[::-1]), axis=0))), np.int32)
road = np.zeros_like(img)
road_bkg = np.zeros_like(img)
cv2.fillPoly(road, [left_lane], color=[255, 0,0])
cv2.fillPoly(road, [right_lane], color=[0, 0, 255])
cv2.fillPoly(road_bkg, [left_lane], color=[255, 255, 255])
cv2.fillPoly(road_bkg, [right_lane], color=[255, 255, 255])
road_warped = cv2.warpPerspective(road, Minv, img_size, flags=cv2.INTER_LINEAR)
road_warped_bkg = cv2.warpPerspective(road_bkg, Minv, img_size, flags=cv2.INTER_LINEAR)
base = cv2.addWeighted(img, 1.0, road_warped_bkg, -1.0, 0.0)
result = cv2.addWeighted(base, 1.0, road_warped, 1.0, 0.0)
ym_per_pix = curve_centers.ym_per_pix
xm_per_pix= curve_centers.xm_per_pix
curve_fit_cr = np.polyfit(np.array(res_yvals,np.float32)*ym_per_pix, np.array(leftx, np.float32)*xm_per_pix, 2)
curverad = ((1 + (2*curve_fit_cr[0]*yvals[-1]*ym_per_pix + curve_fit_cr[1])**2)**1.5) / np.absolute(2*curve_fit_cr[0])
camera_center = (left_fitx[-1] + right_fitx[-1])/2
center_diff = (camera_center-warped.shape[1]/2)*xm_per_pix
side_pos = 'left'
if center_diff <=0:
side_pos = 'right'
cv2.putText(result, 'Radius of Curvature = '+str(round(curverad, 3))+'(m)',(50,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
cv2.putText(result, 'Vehicle is '+str(abs(round(center_diff, 3)))+'m '+side_pos+' of center',(50,100),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
# result = road
write_name = f'./solution/first_frame_old_soln.jpg'
cv2.imwrite(write_name, result)