def _detect_lane(self, image, left_fit_prev=None, right_fit_prev=None):
# Undistort image
mtx, dist = self.undis_param
undist = cv2.undistort(image, mtx, dist, None, mtx)
# Apply threshold
thresh = Thresholds(undist, self.debug)
undist_binary = thresh.apply_thresholds(ksize=self.ksize,
s_thresh=self.s_thresh,
sx_thresh=self.sx_thresh,
sy_thresh=self.sy_thresh,
m_thresh=self.m_thresh,
d_thresh=self.d_thresh)
# Warp binary image
transform = PerspectiveTransform(undist_binary, image, self.debug)
binary_warped = transform.warp()
# Find left and right lane markers polynomial
polyfit = FitPolynomial(binary_warped, self.debug)
left_fit, right_fit = polyfit.fit_polynomial(left_fit_prev,
right_fit_prev)
# Warp image with lanes back
out_image = transform.warp_back(undist, polyfit)
# Curvature and offset
curvature = polyfit.curvature()
offset = polyfit.offset()
return out_image, left_fit, right_fit, curvature, offset
def __init__(self, src, dst, n_images=1, calibration=None, line_segments=LINE_SEGMENTS, offset=0):
self.n_images = n_images
self.camera_calibration = calibration
self.line_segments = line_segments
self.image_offset = offset
self.left_line = None
self.right_line = None
self.center_poly = None
self.curvature = 0.0
self.offset = 0.0
self.perspective = PerspectiveTransform(src, dst)
self.distances = []
def __init__(self, window_width=35, window_height=120, margin=40):
# Load previously calibration camera calibraton parameters.
# If camera is not calibrated, look at the calibration.py for howto do it.
self.mtx, self.dist = load_calibration_matrix(
'camera_cal/dist_pickle.p')
self.window_width = window_width
self.window_height = window_height
self.margin = margin
self.perspective = PerspectiveTransform(debug=True)
self.tracker = LaneTracker(self.window_width, self.window_height,
self.margin, 30 / 720, 3.7 / 700)
'./images/' + f for f in os.listdir('./images')
if os.path.isfile(os.path.join('./images', f))
]
# Selecting random file for testing
file_img = example_files[np.random.randint(0, len(example_files))]
# file_img = './images/806123698_321554.jpg' # Good file for testing
img = imageio.imread(file_img)
plt.figure(figsize=(10, 10))
plt.imshow(img)
plt.show()
# Finding corners from input image
corner_points = CornerDetector(img).find_corners4().astype(np.float32)
corner_points[:, [0, 1]] = corner_points[:, [1, 0]]
# Computing the perspective transform
img_p = PerspectiveTransform(img, corner_points).four_point_transform()
# Finding text areas
img_cv = cv2.cvtColor(img_p, cv2.COLOR_RGB2BGR)
# Testing with different structuring element sizes
sizes = [(17, 3), (30, 10), (5, 5), (9, 3)]
for size in sizes:
strs, bound_rects, img_bboxes = TextDetector(img_cv,
size).recognize_text()
plt.figure(figsize=(10, 10))
plt.imshow(cv2.cvtColor(img_bboxes, cv2.COLOR_BGR2RGB))
plt.show()
print(size)
print(*strs, sep='\n')
__author__ = 'z84105425'
# -*- coding:utf-8 -*-
import cv2
from threshold import Threshold
from perspective_transform import PerspectiveTransform
if __name__ == '__main__':
image_path = "bios.jpg"
img = cv2.imread(image_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 获取阈值
th = Threshold(gray.shape[1], gray.shape[0], 0.83, 0.03)
thresh_1 = th.get_thresh_1(gray)
thresh_2 = th.get_thresh_2(gray)
# 二值化
ret1, binary1 = cv2.threshold(gray, thresh_1, 255, cv2.THRESH_BINARY)
ret2, binary2 = cv2.threshold(gray, thresh_2, 255, cv2.THRESH_BINARY)
cv2.namedWindow("binary1", 0)
cv2.namedWindow("binary2", 0)
cv2.imshow("binary1", binary1)
cv2.imshow("binary2", binary2)
cv2.imwrite('binary1.jpg', binary1)
cv2.waitKey(0)
# 透视变换
pt = PerspectiveTransform(10000)
pt.perspective_transform(img, binary1)