def test(img):
"""
ASSUMES:
-img: numpy.array, cv2 image in BGR,
RETURNS:
-img_ROI: numpy.array, cv2 image in BGR, image that shows ONLY around
the biggest red region in img
-centerX: int, x coordinate of the center of the ROI in img_ROI
-centerY: int, y coordinate of the center of the ROI in img_ROI
"""
#color filtering
_,img_red = color_filter(img)
# noise canceling
img_noiseless = noise_cancel(img_red)
# resize
img_noiseless_small = cv2.resize(img_noiseless,(200,100))
img_small = cv2.resize(img,(200,100))
# region of interest
mask,centerX,centerY,(h,w) = mask4ROI(img_noiseless_small,isColoredPixel)
img_ROI = region_of_interest(img_small,mask)
return img_ROI,centerX,centerY,(h,w)
def final_viz(undist, left_fit, right_fit, m_inv, left_curve, right_curve,
vehicle_offset):
ploty = np.linspace(0, undist.shape[0] - 1, undist.shape[0])
left_fitx = left_fit[0] * ploty**2 + left_fit[1] * ploty + left_fit[2]
right_fitx = right_fit[0] * ploty**2 + right_fit[1] * ploty + right_fit[2]
# 创建图片
color_warp = np.zeros((1080, 1920, 3), dtype='uint8')
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array(
[np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))
newwarp = cv2.warpPerspective(color_warp, m_inv,
(undist.shape[1], undist.shape[0]))
result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
# 图片右上角显示曲率半径，中心偏移量
avg_curve = (left_curve + right_curve) / 2
string1 = 'R_mean : %.1f m' % avg_curve
if left_fit[0] > 0 and avg_curve > 500:
string2 = "gentle right"
elif left_fit[0] > 0 and avg_curve <= 500:
string2 = "hard right"
elif left_fit[0] < 0 and avg_curve > 500:
string2 = "gentle left"
elif left_fit[0] < 0 and avg_curve <= 500:
string2 = "hard left"
string3 = 'central offset: %.1f m' % vehicle_offset
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(result, string1, (1500, 100), font, 0.9, (0, 0, 0), 4,
cv2.LINE_AA)
cv2.putText(result, string2, (1500, 300), font, 0.9, (0, 0, 0), 4,
cv2.LINE_AA)
cv2.putText(result, string3, (1500, 200), font, 0.9, (0, 0, 0), 4,
cv2.LINE_AA)
# 图片上方显示消除失真图，鸟瞰图，车道线检测图
small_undist = cv2.resize(undist, (0, 0), fx=0.2, fy=0.2)
#这里的鸟瞰图其实就是透射变换
bird_eye, _, _, _ = perspective_transform(undist)
small_bird_eye = cv2.resize(bird_eye, (0, 0), fx=0.2, fy=0.2)
img = cv2.cvtColor(undist, cv2.COLOR_BGR2RGB)
vertices = np.int32([[(50, 1080), (700, 760), (920, 760), (1400, 1080)]])
masked_image = region_of_interest(img, vertices)
cv2.line(masked_image, (50, 1080), (700, 760), (0, 0, 0), 10)
cv2.line(masked_image, (920, 760), (1400, 1080), (0, 0, 0), 25)
img, abs_bin, mag_bin, dir_bin, hls_bin = combined_thresh(masked_image)
binary_warped, binary_unwarped, m, m_inv = perspective_transform(img)
ret = line_fit(binary_warped)
out_image = viz2(binary_warped, ret)
small_out_image = cv2.resize(out_image, (0, 0), fx=0.2, fy=0.2)
x1 = 0
y1 = 100
x2 = small_out_image.shape[0]
y2 = small_out_image.shape[1]
y3 = small_out_image.shape[1] * 2
y4 = small_out_image.shape[1] * 3
result[x1 + 100:x2 + 100, y1:y2 + 100, :] = small_undist
result[x1 + 100:x2 + 100, y2 + 200:y3 + 200, :] = small_bird_eye
result[x1 + 100:x2 + 100, y3 + 300:y4 + 300, :] = small_out_image
cv2.putText(result, "Undist", (100, 80), font, 0.9, (0, 0, 0), 4,
cv2.LINE_AA)
cv2.putText(result, "Bird's Eye", (580, 80), font, 0.9, (0, 0, 0), 4,
cv2.LINE_AA)
cv2.putText(result, "Line Search", (1080, 80), font, 0.9, (0, 0, 0), 4,
cv2.LINE_AA)
return result
result = False
return result
return result
while True:
(ret, frame) = camera.read()
if not ret:
print("faile to get frame")
break
h, w = frame.shape[:2]
vertices = np.array([[30, h - 1], [w / 5, h / 2 - h / 10 + 15],
[w - w / 5, h / 2 - h / 10 + 15], [w - 30, h - 1]],
np.int32) #for set the Roi
Roi_frame = region_of_interest.region_of_interest(frame, [vertices])
gray = cv2.cvtColor(Roi_frame, cv2.COLOR_BGR2GRAY)
if first_frame is None:
first_frame = gray
continue
delta_frame = cv2.absdiff(first_frame, gray)
blur_delta = cv2.GaussianBlur(delta_frame, (3, 3), 0)
ad_th_delta = cv2.adaptiveThreshold(blur_delta, 255,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, 21, 10)
Roi_sub_frame0 = ad_th_delta[(h // 2 + h // 10) + 10:(h // 2 + h // 10) +
40, w // 2 - 15:w // 2 + 15]
Roi_sub_frame1 = ad_th_delta[(h // 4) * 3 - 10:(h // 4 * 3) + 20,
Driver main for module_orientation
To test module_orientation, use
"python module_orientation.py -i {depthimage.npy}"
Also note that region_of_interest should be in the same folder as module_orientation
"""
import argparse
from region_of_interest import region_of_interest
# # Create object for parsing command-line options
parser = argparse.ArgumentParser(description="Read .npy file and test for get_module_depth.\
To read a .npy file, type \"python get_module_depth.py --i (image name).npy)\"")
# # Add argument which takes path to a bag file as an input
parser.add_argument("-i", "--input", type=str, help="Path to the .npy file")
# # Parse the command line arguments to an object
args = parser.parse_args()
if args.input:
depthNpy = args.input
else:
raise FileNotFoundError("No input parameter has been given. For help type --help")
depthImage = np.load(depthNpy)
# values for the test depth image
center = (650, 560)
roi = region_of_interest(depthImage, depthImage[560][650], center)
get_module_orientation(roi)
# 批量读取图片
image_files = os.listdir('test_images')
for image_file in image_files:
out_image_file = image_file.split('.')[0] + '.png' # write to png format
img = mpimg.imread('test_images/' + image_file)
# 校正图片
img = cv2.undistort(img, mtx, dist, None, mtx)
plt.imshow(img)
plt.savefig('example_images/undistort' + out_image_file)
# ROI操作
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# 多边形角点（左下，左上，右上，右下）
vertices = np.int32([[(50, 1080), (700, 760), (920, 760), (1400, 1080)]])
masked_image = region_of_interest(img, vertices)
# 消除ROI边界影响，宽度适当调整
cv2.line(masked_image, (50, 1080), (700, 760), (0, 0, 0), 10)
cv2.line(masked_image, (920, 760), (1400, 1080), (0, 0, 0), 25)
# 二值化
img, abs_bin, mag_bin, dir_bin, hls_bin = combined_thresh(masked_image)
plt.imshow(img, cmap='gray', vmin=0, vmax=1)
plt.savefig('example_images/binary' + out_image_file)
# 透视变换
img, binary_unwarped, m, m_inv = perspective_transform(img)
plt.imshow(img, cmap='gray', vmin=0, vmax=1)
plt.savefig('example_images/warped' + out_image_file)
# 多项式拟合