Output a triangle in Python

你好，以下是 Python 输出三角形的代码：

rows = int(input("请输入行数："))
for i in range(1, rows+1):
    for j in range(1, rows-i+1):
        print(end=" ")
    for j in range(1, i+1):
        print("*", end=" ")
    print()

希望能对你有所帮助。

相关问题

Write python program to print out a neatly formatted multiplication table, up to 12 x 12. It should be a triangle shape with minimum size

Sure, I can help you with that.

Here's a Python program that prints out a neatly formatted multiplication table up to 12 x 12:

for i in range(1, 13):
    for j in range(1, i+1):
        product = i * j
        print('{:2d} x {:2d} = {:3d}'.format(j, i, product), end='  ')
    print('\n')

This program uses two nested `for` loops to generate the product of each number up to 12 with every other number up to that same limit. The `format()` method is used to format the output with leading zeros for single-digit numbers and adequate spacing. The output of this program will be a neatly formatted multiplication table in a triangle shape with minimum size as requested.

I hope this helps! Let me know if you have any other questions.

python dlib 换脸 实现一下

您好，关于您提到的Python Dlib换脸，我可以为您提供以下代码参考：

import dlib
import cv2
import numpy as np

# 加载模型
predictor_path = 'shape_predictor_68_face_landmarks.dat'
face_swap = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)

# 加载两张图片
img1 = cv2.imread('img1.jpg')
img2 = cv2.imread('img2.jpg')

# 人脸关键点检测与特征点提取
faces1 = face_swap(img1, 1)
faces2 = face_swap(img2, 1)
face1_points = []
face2_points = []
for face in faces1:
    shape = predictor(img1, face)
    for i in range(68):
        face1_points.append((shape.part(i).x, shape.part(i).y))
for face in faces2:
    shape = predictor(img2, face)
    for i in range(68):
        face2_points.append((shape.part(i).x, shape.part(i).y))

# 生成三角剖分
face_swap_triangulation = dlib.delaunay_triangulation(face1_points)
triangles = []
for i in range(face_swap_triangulation.shape[0]):
    tr = []
    for j in range(3):
        tr.append(face_swap_triangulation[i][j])
    triangles.append(tr)

# 仿射变换调整图片
def apply_affine_transform(src, src_tri, dst_tri, size):
    warp_mat = cv2.getAffineTransform(np.float32(src_tri), np.float32(dst_tri))
    dst = cv2.warpAffine(src, warp_mat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
    return dst

# 泊松融合
def apply_mask(src, mask):
    thresh = cv2.threshold(mask, 1, 255, cv2.THRESH_BINARY)[1]
    mask = cv2.merge([thresh, thresh, thresh])
    return cv2.bitwise_and(src, mask)

def apply_poisson(src, src_tri, dst_tri, dst, size):
    warp_mat = cv2.getAffineTransform(np.float32(src_tri), np.float32(dst_tri))
    src_warp = cv2.warpAffine(src, warp_mat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
    mask = np.zeros((size[1], size[0], 3), dtype=np.float32)
    cv2.fillConvexPoly(mask, np.int32(dst_tri), (1.0, 1.0, 1.0), 16, 0)
    mask = (mask * 255).astype(np.uint8)
    cv2.imshow("mask", mask)
    cv2.waitKey(1000)
    r = cv2.boundingRect(np.float32([dst_tri]))
    center = ((r[0]+int(r[2]/2), r[1]+int(r[3]/2)))
    output = cv2.seamlessClone(np.uint8(src_warp), dst, mask, center, cv2.NORMAL_CLONE)
    return output

# 换脸主程序入口
def face_swap_image(img1, img2):
    faces1 = face_swap(img1, 1)
    faces2 = face_swap(img2, 1)
    face1_points = []
    face2_points = []
    for face in faces1:
        shape = predictor(img1, face)
        for i in range(68):
            face1_points.append((shape.part(i).x, shape.part(i).y))
    for face in faces2:
        shape = predictor(img2, face)
        for i in range(68):
            face2_points.append((shape.part(i).x, shape.part(i).y))
 
    if len(faces1) > 0 and len(faces2) > 0:
        # 调整图片大小，使得两张图片尺寸一致
        height, width, channels = img2.shape
        img1_resized = cv2.resize(img1, (width, height))
 
        # 人脸关键点检测与特征点提取
        faces1 = face_swap(img1_resized, 1)
        faces2 = face_swap(img2, 1)
        face1_points = []
        face2_points = []
        for face in faces1:
            shape = predictor(img1_resized, face)
            for i in range(68):
                face1_points.append((shape.part(i).x, shape.part(i).y))
        for face in faces2:
            shape = predictor(img2, face)
            for i in range(68):
                face2_points.append((shape.part(i).x, shape.part(i).y))
 
        # 生成三角剖分
        img2_new_face = np.zeros_like(img2)
        for triangle in triangles:
            # 获取面部特征点序号
            x1, y1 = face1_points[triangle[0]]
            x2, y2 = face1_points[triangle[1]]
            x3, y3 = face1_points[triangle[2]]
            src = np.float32([[x1, y1], [x2, y2], [x3, y3]])
 
            # 获取新脸的面部特征点
            x1, y1 = face2_points[triangle[0]]
            x2, y2 = face2_points[triangle[1]]
            x3, y3 = face2_points[triangle[2]]
            dst = np.float32([[x1, y1], [x2, y2], [x3, y3]])
 
            # 仿射变换调整图片
            warped_triangle = apply_affine_transform(img1_resized, src, dst, (width, height))
 
            # 泊松融合
            mask = np.zeros_like(img2_new_face)
            cv2.fillConvexPoly(mask, np.int32(dst), (255, 255, 255))
            warped_triangle_masked = apply_mask(warped_triangle, mask)
            img2_new_face = cv2.add(img2_new_face, warped_triangle_masked)
 
        # 边缘融合
        r = cv2.boundingRect(np.float32([face2_points]))
        center = ((r[0]+int(r[2]/2), r[1]+int(r[3]/2)))
        output = apply_poisson(img2_new_face, face2_points, np.array(triangles), img2, img2.shape[0:2])
 
        # 返回融合后的图片
        return output
    else:
        # 未检测到人脸，直接返回原图
        return img2
        
img1 = cv2.imread('img1.jpg')
img2 = cv2.imread('img2.jpg')
output = face_swap_image(img1, img2)
cv2.imshow("Face Swap Output", output)
cv2.waitKey(0)
cv2.destroyAllWindows()

这段代码使用了Python的Dlib库进行人脸关键点检测与特征点提取，并对两张图片进行三角剖分、仿射变换调整和泊松融合等操作，实现了换脸效果。

希望能够对您有所帮助，如果您还有任何问题，请随时询问我。