# multi_lane_detection

**Repository Path**: WUYIP/multi_lane_detection

## Basic Information

- **Project Name**: multi_lane_detection
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-01-22
- **Last Updated**: 2020-12-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# Multi Lane Line Detection using Python and OpenCV
The project was designed based on the site's single lane detection.

If you want to know detail single lane detection, click 
[Single_Lane_detection](https://github.com/tatsuyah/Lane-Lines-Detection-Python-OpenCV)

## How to use
command : python3 lane_v3.py
## Color filter and edge detector
```python
import cv2
import numpy as np


def Color_filtering(image):
    img_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    lower_yellow = np.array([20, 100, 100], dtype= "uint8")
    upper_yellow = np.array([30, 255, 255], dtype="uint8")
    mask_yellow = cv2.inRange(img_hsv, lower_yellow, upper_yellow)
    mask_white = cv2.inRange(gray_image, 200, 255)
    mask_yw = cv2.bitwise_or(mask_white, mask_yellow)
    mask_yw_image = cv2.bitwise_and(gray_image, mask_yw)
    return mask_yw_image

def detect_edge(masked):
    frame = cv2.cvtColor(masked, cv2.COLOR_BGR2GRAY)
    thresh = 200
    frame = cv2.threshold(frame, thresh, 255, cv2.THRESH_BINARY)[1]

    blurred =cv2.GaussianBlur(frame, (11, 11), 0)
    edged = cv2.Canny(blurred, 20, 150)
    return edged
```


## histogram


```python
import numpy as np
import cv2



def get_histogram(binary_warped):
    histogram = np.sum(binary_warped[binary_warped.shape[0] // 2:, :], axis=0)

    return histogram
```



## Multi lane classifier
```python
import cv2
import numpy as np

def multi_lane_classifier(histogram, threshold):
    hist_index_info = np.where(histogram>threshold)
    hist_index = hist_index_info[0]
    new_arr = hist_index.copy()
    new_arr = np.zeros_like(new_arr)
    for i in range(len(hist_index)):
        if i > 0 and i < new_arr.shape[0]:
            new_arr[i] = hist_index[i] - hist_index[i - 1]
    pixel_index = np.where(new_arr > 100)
    lane_num = len(pixel_index[0] ) + 1
    print(pixel_index[0])
    print(lane_num)
    return lane_num, pixel_index, hist_index

```

## where am I


```python
import cv2
import numpy as np

# midpoint = 10
# base_line1 = 4
# base_line2 = 8
# base_line3 = 12
def where_am_I_4(midpoint, base_line1, base_line2, base_line3, base_line4):
    distance_1 = abs(midpoint - base_line1)
    distance_2 = abs(midpoint - base_line2)
    distance_3 = abs(midpoint - base_line3)
    distance_4 = abs(midpoint - base_line4)

    where_list = np.array([distance_1, distance_2, distance_3, distance_4])
    # step_1 = np.where(where_list == where_list.min(), 250, where_list)
    step_1 = np.argmin(where_list)
    where_list[step_1] = 1200
    step_2 = np.argmin(where_list)
    where_list[step_2] = 1200

    min_index = np.where(where_list ==1200)
    if (step_1 == 0 and step_2 == 1) or (step_1 == 1 and step_2 == 0):
        text0 = "Where am I : Lane Num 1"
        return text0
    if (min_index[0][0] == 1 and min_index[0][1] == 2) or (min_index[0][0] == 2 and min_index[0][1] == 1):
        text1 = "Where am I : Lane Num 2"
        return text1
    if (step_1 == 2 and step_2 == 3) or (step_1 == 3 and step_1 == 2):
        text2 = "Where am I : Lane Num 3"
        return text2

def where_am_I_3(midpoint, base_line1, base_line2, base_line3):
    distance_1 = abs(midpoint - base_line1)
    distance_2 = abs(midpoint - base_line2)
    distance_3 = abs(midpoint - base_line3)

    where_list = np.array([distance_1, distance_2, distance_3])
    # step_1 = np.where(where_list == where_list.min(), 250, where_list)
    step_1 = np.argmin(where_list)
    where_list[step_1] = 1200
    step_2 = np.argmin(where_list)
    where_list[step_2] = 1200

    min_index = np.where(where_list ==1200)
    if (step_1 == 0 and step_2 == 1) or (step_1 == 1 and step_2 == 0):
        text0 = "Where am I : Lane Num 1"
        return text0
    if (min_index[0][0] == 1 and min_index[0][1] == 2) or (min_index[0][0] == 2 and min_index[0][1] == 1):
        text1 = "Where am I : Lane Num 2"
        return text1
# text = where_am_I_3(midpoint, base_line1, base_line2, base_line3)
# print(text)
```