本文整理汇总了Python中tracker.Tracker.find_window_centroids方法的典型用法代码示例。如果您正苦于以下问题：Python Tracker.find_window_centroids方法的具体用法？Python Tracker.find_window_centroids怎么用？Python Tracker.find_window_centroids使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tracker.Tracker的用法示例。
在下文中一共展示了Tracker.find_window_centroids方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: process_img
# 需要导入模块: from tracker import Tracker [as 别名]
# 或者: from tracker.Tracker import find_window_centroids [as 别名]
def process_img(img):
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)
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)
inner_lane = np.array(list(zip(np.concatenate((left_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, [inner_lane], color=[0, 255, 0])
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, .7, 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'
#.........这里部分代码省略.........