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# Project: finding Lane Lines on the Road
일시: 2017-12-24-17:38
## 프로그램 설치
Docker 이미지 다운로드
``` docker pull udacity/carnd-term1-starter-kit ```
``` git clone https://github.com/udacity/CarND-Term1-Starter-Kit-Test.git cd CarND-Term1-Starter-Kit-Test ```
Docker실행
``` docker run -it –rm -p 8888:8888 -v `pwd`:/src udacity/carnd-term1-starter-kit test.ipynb ```
Docker 접속
``` docker exec -it containerid /bin/bash ```
## Finding Lines of Color
이미지에서 특정 threshold 이하면 까만색으로 바꾼다. 못보던 구문. numpy에서 darray 다루는 방법. multidimentinal index와 slice를 사용한거다.
``` thresholds = (image[:,:,0] < rgb_threshold[0]) \
| (image[:,:,1] < rgb_threshold[1]) \
| (image[:,:,2] < rgb_threshold[2])
color_select[thresholds] = [0, 0, 0] ```
ndarray index and slicing
``` b = np.arange(9*9*3) c = b.reshape(9,9,3)
c[0,0,0] # 첫번째 픽셀의 빨간성분 0 ```
``` c[:,0,0] # 가장 왼쪽 컬럼 빨간 성분 array([ 0, 27, 54, 81, 108, 135, 162, 189, 216]) ```
thresholds를 selector로 사용했다. 다음 코드는 x를 y의 요소의 selector(filter)로 사용하였다. numpy.where를 참고
``` x = np.array([True, False, True, False]) y = np.arange(4) # [0 1 2 3 ] y[x] array([0, 2]) ```
## Canny Edges
Gray → GaussianBlur → Canny 순으로 Edge Detection을 한다.
Gray
``` gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY) ```
GaussianBlur ``` blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0) ```
왜 GaussianBlue를 하지? 노이즈 제거. 커널은 함수로 N x N matrix의 합성곱(convolution)
Canny ``` edges = cv2.Canny(blur_gray, low_threshold, high_threshold) ```
`low_threshold` 아래는 무시. `high_threshold`는 오케이. 둘 사이는 연결된거 보고 판단
## Hough Transform
Duda and Hart, 1972. 이미지에서 line detection하는 알고리즘. 커브 디텍션도 가능 HoughLinesP의 파라미터 바꾸기. `max_line_gap` 과 `min_line_length` 을 적당히 바꾸면 된다. 300, 1을 사용
Canny → Hough Transform → 결과로 나온 라인을 강조
``` lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]),
min_line_length, max_line_gap)
```
- rho : 0 ~ 1 - theta : 0 ~ 180 - threshold : 만나는 점의 수. threshold가 적으면 선검출 많지만
정확도가 떨어짐
- `min_line_length` : 검출되는 최소선의 길이. 적으면 많은 선이 검출 - `max_line_gap` : 선과 선사이의 간격
라인 그리기 ``` for line in lines:
for x1,y1,x2,y2 in line:
cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)
```
참고: https://alyssaq.github.io/2014/understanding-hough-transform/
Hough Transform Quiz
``` import matplotlib.pyplot as plt import matplotlib.image as mpimg import numpy as np import cv2
# Read in and grayscale the image image = mpimg.imread('exit-ramp.jpg') gray = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)
# Define a kernel size and apply Gaussian smoothing kernel_size = 5 blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)
# Define our parameters for Canny and apply low_threshold = 50 high_threshold = 150 edges = cv2.Canny(blur_gray, low_threshold, high_threshold)
# Next we'll create a masked edges image using cv2.fillPoly() mask = np.zeros_like(edges) ignore_mask_color = 255
# This time we are defining a four sided polygon to mask imshape = image.shape vertices = np.array((0,imshape[0]),(0, 0), (imshape[1], 0), (imshape[1],imshape[0]), dtype=np.int32) cv2.fillPoly(mask, vertices, ignore_mask_color) masked_edges = cv2.bitwise_and(edges, mask)
# Define the Hough transform parameters # Make a blank the same size as our image to draw on rho = 1 # distance resolution in pixels of the Hough grid theta = np.pi/180 # angular resolution in radians of the Hough grid threshold = 1 # minimum number of votes (intersections in Hough grid cell) min_line_length = 300 #minimum number of pixels making up a line max_line_gap = 10 # maximum gap in pixels between connectable line segments line_image = np.copy(image)*0 # creating a blank to draw lines on
# Run Hough on edge detected image # Output “lines” is an array containing endpoints of detected line segments lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]),
min_line_length, max_line_gap)
# Iterate over the output “lines” and draw lines on a blank image for line in lines:
for x1,y1,x2,y2 in line:
cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)
# Create a “color” binary image to combine with line image color_edges = np.dstack1)
# Draw the lines on the edge image lines_edges = cv2.addWeighted(color_edges, 0.2, line_image, 1, 0) plt.imshow(lines_edges)
```
## CarND-LaneLines-P1
프로젝트
``` https://github.com/udacity/CarND-LaneLines-P1 cd CarND-LaneLines-P1 docker run -it –rm -p 8888:8888 -v `pwd`:/src udacity/carnd-term1-starter-kit P1.ipynb ```
### 프로젝트 하면서 궁금한 코드
두개 점의 각도 구하기 ?
``` import math angle = math.atan2(y2 - y1, x2 - x1) * 180 / math.PI; ```
두개의 점을 지나는선을 확장하기
``` b = y1 - (y2-y1)/(x2-x1)*x1 y = (y2-y1)/(x2-x1)*540 + b # 540일때 y y = (y2-y1)/(x2-x1)*0 + b # 0일때 y b = y1 - (y2-y1)/(x2-x1)*x1 x1_extend = (330-b)*(x2-x1)/(y2-y1) # y가 330일때 x x2_extend = (540-b)*(x2-x1)/(y2-y1) # y가 540일때 x ```
뭐하는 함수지?
``` cv2.fillPoly(mask, vertices, ignore_mask_color) cv2.bitwise_and(img, mask) ```
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