def draw_line(line): x1, y1, x2, y2 = line[0] cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2)这段代码出现错误，应该怎么改正

There seems to be a missing comma after `y1`. Here's the corrected code:

def draw_line(line):
    x1, y1, x2, y2 = line[0]
    cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2)

相关问题

智能小车opencv巡线代码

以下是智能小车OpenCV巡线代码的示例：

import cv2
import numpy as np

# 设置ROI区域
def roi(img, vertices):
    mask = np.zeros_like(img)
    cv2.fillPoly(mask, vertices, 255)
    masked = cv2.bitwise_and(img, mask)
    return masked

# 处理图像
def process_img(original_image):
    # 转换为灰度图像
    processed_img = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
    # 高斯模糊
    processed_img = cv2.GaussianBlur(processed_img, (5, 5), 0)
    # 边缘检测
    processed_img = cv2.Canny(processed_img, threshold1=50, threshold2=150)
    # 设置ROI区域
    vertices = np.array([[0, 480], [0, 280], [150, 200], [490, 200], [640, 280], [640, 480]], np.int32)
    processed_img = roi(processed_img, [vertices])
    # 返回处理后的图像
    return processed_img

# 找到直线
def find_lines(img, lines):
    left_line_x = []
    left_line_y = []
    right_line_x = []
    right_line_y = []
    for line in lines:
        for x1, y1, x2, y2 in line:
            slope = (y2 - y1) / (x2 - x1)
            if slope < 0:
                left_line_x.extend([x1, x2])
                left_line_y.extend([y1, y2])
            else:
                right_line_x.extend([x1, x2])
                right_line_y.extend([y1, y2])
    min_y = img.shape[0] * 0.6
    max_y = img.shape[0]
    poly_left = np.poly1d(np.polyfit(left_line_y, left_line_x, deg=1))
    left_x_start = int(poly_left(max_y))
    left_x_end = int(poly_left(min_y))
    poly_right = np.poly1d(np.polyfit(right_line_y, right_line_x, deg=1))
    right_x_start = int(poly_right(max_y))
    right_x_end = int(poly_right(min_y))
    return [[left_x_start, int(max_y), left_x_end, int(min_y)]], [[right_x_start, int(max_y), right_x_end, int(min_y)]]

# 绘制直线
def draw_lines(img, lines):
    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
    for line in lines:
        for x1, y1, x2, y2 in line:
            cv2.line(line_img, (x1, y1), (x2, y2), [0, 255, 0], thickness=5)
    return line_img

# 主函数
def main():
    cap = cv2.VideoCapture(0)
    while True:
        _, frame = cap.read()
        processed_img = process_img(frame)
        lines = cv2.HoughLinesP(processed_img, rho=6, theta=np.pi / 60, threshold=160, lines=np.array([]), minLineLength=40, maxLineGap=25)
        left_line, right_line = find_lines(processed_img, lines)
        line_img = draw_lines(frame, [left_line, right_line])
        result = cv2.addWeighted(frame, 0.9, line_img, 1, 1)
        cv2.imshow("result", result)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

给我利用Python+OpenCV实现照片中的道路检测（lane detection）的代码

好的，以下是利用Python OpenCV实现照片中的道路检测（lane detection）的代码：

import cv2
import numpy as np

def region_of_interest(img, vertices):
    mask = np.zeros_like(img)
    # 填充三角形区域
    cv2.fillPoly(mask, vertices, 255)
    masked_img = cv2.bitwise_and(img, mask)
    return masked_img

def draw_lines(img, lines, color=[0, 0, 255], thickness=3):
    # 创建一个空的坐标列表，用于存放左、右车道线的所有坐标点
    left_line = []
    right_line = []

    for line in lines:
        x1, y1, x2, y2 = line.reshape(4)
        # 计算线段斜率
        slope = (y2 - y1) / (x2 - x1)
        # 排除垂直线和水平线
        if slope != np.nan and abs(slope) > 0.5:
            # 分类左、右车道线的坐标点
            if slope < 0:
                left_line.append((x1, y1))
                left_line.append((x2, y2))
            else:
                right_line.append((x1, y1))
                right_line.append((x2, y2))

    if len(left_line) > 0 and len(right_line) > 0:
        # 将车道线的所有坐标点转换为 NumPy 数组
        left_line = np.array(left_line)
        right_line = np.array(right_line)
        # 计算左、右车道线的一次多项式
        left_fit = np.polyfit(left_line[:, 0], left_line[:, 1], 1)
        right_fit = np.polyfit(right_line[:, 0], right_line[:, 1], 1)
        # 计算左、右车道线与 ROI 底部的交点坐标
        y1 = img.shape[0]
        x1_l = int((y1 - left_fit[1]) / left_fit[0])
        x1_r = int((y1 - right_fit[1]) / right_fit[0])
        # 计算左、右车道线与 ROI 上部的交点坐标
        y2 = int(img.shape[0] * 0.6)
        x2_l = int((y2 - left_fit[1]) / left_fit[0])
        x2_r = int((y2 - right_fit[1]) / right_fit[0])
        # 画出左、右车道线
        cv2.line(img, (x1_l, y1), (x2_l, y2), color, thickness)
        cv2.line(img, (x1_r, y1), (x2_r, y2), color, thickness)

def process_image(img):
    # 转换为灰度图像
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 进行高斯滤波
    kernel_size = 5
    blur_gray = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0)
    # 进行 Canny 边缘检测
    low_threshold = 50
    high_threshold = 150
    edges = cv2.Canny(blur_gray, low_threshold, high_threshold)
    # 设置 ROI 区域的顶点坐标
    imshape = img.shape
    vertices = np.array([[(100, imshape[0]), (450, 320), (520, 320), (imshape[1]-100, imshape[0])]], dtype=np.int32)
    roi_image = region_of_interest(edges, vertices)
    # 进行 Hough 变换检测直线
    rho = 2
    theta = np.pi/180
    threshold = 100
    min_line_len = 50
    max_line_gap = 160
    lines = cv2.HoughLinesP(roi_image, rho, theta, threshold, np.array([]), min_line_len, max_line_gap)
    # 将检测到的直线画在原图上
    line_image = np.zeros_like(img)
    draw_lines(line_image, lines)
    # 将检测到的车道线与原图进行叠加
    result = cv2.addWeighted(img, 0.8, line_image, 1, 0)
    return result

# 对输入视频进行车道线检测并显示处理结果
cap = cv2.VideoCapture('test_video.mp4')
while cap.isOpened():
    ret, frame = cap.read()
    if ret == True:
        result = process_image(frame)
        cv2.imshow('result', result)
        if cv2.waitKey(25) & 0xFF == ord('q'):
            break
    else:
        break
cap.release()
cv2.destroyAllWindows()

以上代码实现了利用 Python OpenCV 对照片中的道路进行检测的功能。其中，车道线检测算法使用了霍夫变换，可以检测出道路上的直线，然后通过一些数学方法来筛选出车道线的坐标点，并将它们转换为一次多项式。最终，我们可以将车道线与原图进行叠加，得到检测结果。