python写爱心代码【爱心代码编程python可复制粘贴】

python写爱心代码【爱心代码编程python可复制粘贴】

python程序代码：heart.py

from math import cos, pi

import numpy as np

import cv2

import os, glob 

 

class HeartSignal:

    def __init__(self, curve=”heart”, title=”Love U”, frame_num=20, seed_points_num=2000, seed_num=None, highlight_rate=0.3,

                 background_img_dir=””, set_bg_imgs=False, bg_img_scale=0.2, bg_weight=0.3, curve_weight=0.7, frame_width=1080, frame_height=960, scale=10.1,

                 base_color=None, highlight_points_color_1=None, highlight_points_color_2=None, wait=100, n_star=5, m_star=2):

        super().__init__()

        self.curve = curve

        self.title = title

        self.highlight_points_color_2 = highlight_points_color_2

        self.highlight_points_color_1 = highlight_points_color_1

        self.highlight_rate = highlight_rate

        self.base_color = base_color

        self.n_star = n_star

        self.m_star = m_star

        self.curve_weight = curve_weight

        img_paths = glob.glob(background_img_dir + “/*”)

        self.bg_imgs = []

        self.set_bg_imgs = set_bg_imgs

        self.bg_weight = bg_weight

        if os.path.exists(background_img_dir) and len(img_paths) > 0 and set_bg_imgs:

            for img_path in img_paths:

                img = cv2.imread(img_path)

                self.bg_imgs.append(img)

            first_bg = self.bg_imgs[0]

            width = int(first_bg.shape[1] * bg_img_scale)

            height = int(first_bg.shape[0] * bg_img_scale)

            first_bg = cv2.resize(first_bg, (width, height), interpolation=cv2.INTER_AREA)

 

            # 对齐图片，自动裁切中间

            new_bg_imgs = [first_bg, ]

            for img in self.bg_imgs[1:]:

                width_close = abs(first_bg.shape[1] – img.shape[1]) < abs(first_bg.shape[0] – img.shape[0])

                if width_close:

                    # resize

                    height = int(first_bg.shape[1] / img.shape[1] * img.shape[0])

                    width = first_bg.shape[1]

                    img = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)

                    # crop and fill

                    if img.shape[0] > first_bg.shape[0]:

                        crop_num = img.shape[0] – first_bg.shape[0]

                        crop_top = crop_num //www.diyiyuanma.cn 2

                        crop_bottom = crop_num – crop_top

                        img = np.delete(img, range(crop_top), axis=0)

                        img = np.delete(img, range(img.shape[0] – crop_bottom, img.shape[0]), axis=0)

                    elif img.shape[0] < first_bg.shape[0]:

                        fill_num = first_bg.shape[0] – img.shape[0]

                        fill_top = fill_num www.lmtaolu.cn// 2

                        fill_bottom = fill_num – fill_top

                        img = np.concatenate([np.zeros([fill_top, width, 3]), img, np.zeros([fill_bottom, width, 3])], axis=0)

                else:

                    width = int(first_bg.shape[0] / img.shape[0] * img.shape[1])

                    height = first_bg.shape[0]

                    img = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)

                    # crop and fill

                    if img.shape[1] > first_bg.shape[1]:

                        crop_num = img.shape[1] – first_bg.shape[1]

                        crop_top = crop_num www.vbjcw.cn// 2

                        crop_bottom = crop_num – crop_top

                        img = np.delete(img, range(crop_top), axis=1)

                        img = np.delete(img, range(img.shape[1] – crop_bottom, img.shape[1]), axis=1)

                    elif img.shape[1] < first_bg.shape[1]:

                        fill_num = first_bg.shape[1] – img.shape[1]

                        fill_top = fill_num // 2

                        fill_bottom = fill_num – fill_top

                        img = np.concatenate([np.zeros([fill_top, width, 3]), img, np.zeros([fill_bottom, width, 3])], axis=1)

                new_bg_imgs.append(img)

            self.bg_imgs = new_bg_imgs

            assert all(img.shape[0] == first_bg.shape[0] and img.shape[1] == first_bg.shape[1] for img in self.bg_imgs), “背景图片宽和高不一致”

            self.frame_width = self.bg_imgs[0].shape[1]

            self.frame_height = self.bg_imgs[0].shape[0]

        else:

            self.frame_width = frame_width  # 窗口宽度

            self.frame_height = frame_height  # 窗口高度

        self.center_x = self.frame_width / 2

        self.center_y = self.frame_height / 2

        self.main_curve_width = -1

        self.main_curve_height = -1

 

        self.frame_points = []  # 每帧动态点坐标

        self.frame_num = frame_num  # 帧数

        self.seed_num = seed_num  # 伪随机种子，设置以后除光晕外粒子相对位置不动（减少内部闪烁感）

        self.seed_points_num = seed_points_num  # 主图粒子数

        self.scale = scale  # 缩放比例

        self.wait = wait

 

    def curve_function(self, curve):

        curve_dict = {

            “heart”: self.heart_function,

            “butterfly”: self.butterfly_function,

            “star”: self.star_function,

        }

        return curve_dict[curve]

 

    def heart_function(self, t, frame_idx=0, scale=5.20):

        “””

        图形方程

        :param frame_idx: 帧的索引，根据帧数变换心形

        :param scale: 放大比例

        :param t: 参数

        :return: 坐标

        “””

        trans = 3 – (1 + self.periodic_func(frame_idx, self.frame_num)) * 0.5  # 改变心形饱满度度的参数

 

        x = 15 * (np.sin(t) ** 3)

        t = np.where((pi < t) & (t 0部分的图形到3、4象限

        y = -(14 * np.cos(t) – 4 * np.cos(2 * t) – 2 * np.cos(3 * t) – np.cos(trans * t))

 

        ign_area = 0.15

        center_ids = np.where((x > -ign_area) & (x < ign_area))

        if np.random.random() > 0.32:

            x, y = np.delete(x, center_ids), np.delete(y, center_ids)  # 删除稠密部分的扩散，为了美观

 

        # 放大

        x *= scale

        y *= scale

 

        # 移到画布中央

        x += self.center_x

        y += self.center_y

 

        # 原心形方程

        # x = 15 * (sin(t) ** 3)

        # y = -(14 * cos(t) – 4 * cos(2 * t) – 2 * cos(3 * t) – cos(3 * t))

        return x.astype(int), y.astype(int)

 

    def butterfly_function(self, t, frame_idx=0, scale=5.2):

        “””

        图形函数

        :param frame_idx:

        :param scale: 放大比例

        :param t: 参数

        :return: 坐标

        “””

        # 基础函数

        # t = t * pi

        p = np.exp(np.sin(t)) – 2.5 * np.cos(4 * t) + np.sin(t) ** 5

        x = 5 * p * np.cos(t)

        y = – 5 * p * np.sin(t)

 

        # 放大

        x *= scale

        y *= scale

 

        # 移到画布中央

        x += self.center_x

        y += self.center_y

 

        return x.astype(int), y.astype(int)

 

    def star_function(self, t, frame_idx=0, scale=5.2):

        n = self.n_star / self.m_star

        p = np.cos(pi / n) / np.cos(pi / n – (t % (2 * pi / n)))

 

        x = 15 * p * np.cos(t)

        y = 15 * p * np.sin(t)

 

        # 放大

        x *= scale

        y *= scale

 

        # 移到画布中央

        x += self.center_x

        y += self.center_y

 

        return x.astype(int), y.astype(int)

 

    def shrink(self, x, y, ratio, offset=1, p=0.5, dist_func=”uniform”):

        “””

        带随机位移的抖动

        :param x: 原x

        :param y: 原y

        :param ratio: 缩放比例

        :param p:

        :param offset:

        :return: 转换后的x,y坐标

        “””

        x_ = (x – self.center_x)

        y_ = (y – self.center_y)

        force = 1 / ((x_ ** 2 + y_ ** 2) ** p + 1e-30)

 

        dx = ratio * force * x_

        dy = ratio * force * y_

 

        def d_offset(x):

            if dist_func == “uniform”:

                return x + np.random.uniform(-offset, offset, size=x.shape)

            elif dist_func == “norm”:

                return x + offset * np.random.normal(0, 1, size=x.shape)

 

        dx, dy = d_offset(dx), d_offset(dy)

 

        return x – dx, y – dy

 

    def scatter(self, x, y, alpha=0.75, beta=0.15):

        “””

        随机内部扩散的坐标变换

        :param alpha: 扩散因子 – 松散

        :param x: 原x

        :param y: 原y

        :param beta: 扩散因子 – 距离

        :return: x,y 新坐标

        “””

 

        ratio_x = – beta * np.log(np.random.random(x.shape) * alpha)

        ratio_y = – beta * np.log(np.random.random(y.shape) * alpha)

        dx = ratio_x * (x – self.center_x)

        dy = ratio_y * (y – self.center_y)

 

        return x – dx, y – dy

 

    def periodic_func(self, x, x_num):

        “””

        跳动周期曲线

        :param p: 参数

        :return: y

        “””

 

        # 可以尝试换其他的动态函数，达到更有力量的效果（贝塞尔？）

        def ori_func(t):

            return cos(t)

 

        func_period = 2 * pi

        return ori_func(x / x_num * func_period)

 

    def gen_points(self, points_num, frame_idx, shape_func):

        # 用周期函数计算得到一个因子，用到所有组成部件上，使得各个部分的变化周期一致

        cy = self.periodic_func(frame_idx, self.frame_num)

        ratio = 10 * cy

 

        # 图形

        period = 2 * pi * self.m_star if self.curve == “star” else 2 * pi

        seed_points = np.linspace(0, period, points_num)

        seed_x, seed_y = shape_func(seed_points, frame_idx, scale=self.scale)

        x, y = self.shrink(seed_x, seed_y, ratio, offset=2)

        curve_width, curve_height = int(x.max() – x.min()), int(y.max() – y.min())

        self.main_curve_width = max(self.main_curve_width, curve_width)

        self.main_curve_height = max(self.main_curve_height, curve_height)

        point_size = np.random.choice([1, 2], x.shape, replace=True, p=[0.5, 0.5])

        tag = np.ones_like(x)

 

        def delete_points(x_, y_, ign_area, ign_prop):

            ign_area = ign_area

            center_ids = np.where((x_ > self.center_x – ign_area) & (x_ < self.center_x + ign_area))

            center_ids = center_ids[0]

            np.random.shuffle(center_ids)

            del_num = round(len(center_ids) * ign_prop)

            del_ids = center_ids[:del_num]

            x_, y_ = np.delete(x_, del_ids), np.delete(y_, del_ids)  # 删除稠密部分的扩散，为了美观

            return x_, y_

 

        # 多层次扩散

        for idx, beta in enumerate(np.linspace(0.05, 0.2, 6)):

            alpha = 1 – beta

            x_, y_ = self.scatter(seed_x, seed_y, alpha, beta)

            x_, y_ = self.shrink(x_, y_, ratio, offset=round(beta * 15))

            x = np.concatenate((x, x_), 0)

            y = np.concatenate((y, y_), 0)

            p_size = np.random.choice([1, 2], x_.shape, replace=True, p=[0.55 + beta, 0.45 – beta])

            point_size = np.concatenate((point_size, p_size), 0)

            tag_ = np.ones_like(x_) * 2

            tag = np.concatenate((tag, tag_), 0)

 

        # 光晕

        halo_ratio = int(7 + 2 * abs(cy))  # 收缩比例随周期变化

 

        # 基础光晕

        x_, y_ = shape_func(seed_points, frame_idx, scale=self.scale + 0.9)

        x_1, y_1 = self.shrink(x_, y_, halo_ratio, offset=18, dist_func=”uniform”)

        x_1, y_1 = delete_points(x_1, y_1, 20, 0.5)

        x = np.concatenate((x, x_1), 0)

        y = np.concatenate((y, y_1), 0)

 

        # 炸裂感光晕

        halo_number = int(points_num * 0.6 + points_num * abs(cy))  # 光晕点数也周期变化

        seed_points = np.random.uniform(0, 2 * pi, halo_number)

        x_, y_ = shape_func(seed_points, frame_idx, scale=self.scale + 0.9)

        x_2, y_2 = self.shrink(x_, y_, halo_ratio, offset=int(6 + 15 * abs(cy)), dist_func=”norm”)

        x_2, y_2 = delete_points(x_2, y_2, 20, 0.5)

        x = np.concatenate((x, x_2), 0)

        y = np.concatenate((y, y_2), 0)

 

        # 膨胀光晕

        x_3, y_3 = shape_func(np.linspace(0, 2 * pi, int(points_num * .4)),

                                             frame_idx, scale=self.scale + 0.2)

        x_3, y_3 = self.shrink(x_3, y_3, ratio * 2, offset=6)

        x = np.concatenate((x, x_3), 0)

        y = np.concatenate((y, y_3), 0)

 

        halo_len = x_1.shape[0] + x_2.shape[0] + x_3.shape[0]

        p_size = np.random.choice([1, 2, 3], halo_len, replace=True, p=[0.7, 0.2, 0.1])

        point_size = np.concatenate((point_size, p_size), 0)

        tag_ = np.ones(halo_len) * 2 * 3

        tag = np.concatenate((tag, tag_), 0)

 

        x_y = np.around(np.stack([x, y], axis=1), 0)

        x, y = x_y[:, 0], x_y[:, 1]

        return x, y, point_size, tag

 

    def get_frames(self, shape_func):

        for frame_idx in range(self.frame_num):

            np.random.seed(self.seed_num)

            self.frame_points.append(self.gen_points(self.seed_points_num, frame_idx, shape_func))

 

        frames = []

 

        def add_points(frame, x, y, size, tag):

            highlight1 = np.array(self.highlight_points_color_1, dtype=’uint8′)

            highlight2 = np.array(self.highlight_points_color_2, dtype=’uint8′)

            base_col = np.array(self.base_color, dtype=’uint8′)

 

            x, y = x.astype(int), y.astype(int)

            frame[y, x] = base_col

 

            size_2 = np.int64(size == 2)

            frame[y, x + size_2] = base_col

            frame[y + size_2, x] = base_col

 

            size_3 = np.int64(size == 3)

            frame[y + size_3, x] = base_col

            frame[y – size_3, x] = base_col

            frame[y, x + size_3] = base_col

            frame[y, x – size_3] = base_col

            frame[y + size_3, x + size_3] = base_col

            frame[y – size_3, x – size_3] = base_col

            # frame[y – size_3, x + size_3] = color

            # frame[y + size_3, x – size_3] = color

 

            # 高光

            random_sample = np.random.choice([1, 0], size=tag.shape, p=[self.highlight_rate, 1 – self.highlight_rate])

 

            # tag2_size1 = np.int64((tag <= 2) & (size == 1) & (random_sample == 1))

            # frame[y * tag2_size1, x * tag2_size1] = highlight2

 

            tag2_size2 = np.int64((tag <= 2) & (size == 2) & (random_sample == 1))

            frame[y * tag2_size2, x * tag2_size2] = highlight1

            # frame[y * tag2_size2, (x + 1) * tag2_size2] = highlight2

            # frame[(y + 1) * tag2_size2, x * tag2_size2] = highlight2

            frame[(y + 1) * tag2_size2, (x + 1) * tag2_size2] = highlight2

 

        for x, y, size, tag in self.frame_points:

            frame = np.zeros([self.frame_height, self.frame_width, 3], dtype=”uint8″)

            add_points(frame, x, y, size, tag)

            frames.append(frame)

 

        return frames

 

    def draw(self, times=10):

        frames = self.get_frames(self.curve_function(self.curve))

 

        for i in range(times):

            for frame in frames:

                frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)

                if len(self.bg_imgs) > 0 and self.set_bg_imgs:

                    frame = cv2.addWeighted(self.bg_imgs[i % len(self.bg_imgs)], self.bg_weight, frame, self.curve_weight, 0)

                cv2.imshow(self.title, frame)

                cv2.waitKey(self.wait)

 

 

if __name__ == ‘__main__’:

    import yaml

    settings = yaml.load(open(“./settings.yaml”, “r”, encoding=”utf-8″), Loader=yaml.FullLoader)

    if settings[“wait”] == -1:

        settings[“wait”] = int(settings[“period_time”] / settings[“frame_num”])

    del settings[“period_time”]

    times = settings[“times”]

    del settings[“times”]

    heart = HeartSignal(seed_num=5201314, **settings)

    heart.draw(times)

其中也要到这个py文件的相同的文件夹里引入settings.yaml文件：

# 颜色：RGB三原色数值 0~255

# 设置高光时，尽量选择接近主色的颜色，看起来会和谐一点

 

# 视频里的蓝色调

#base_color: # 主色  默认玫瑰粉

#  – 30

#  – 100

#  – 100

#highlight_points_color_1: # 高光粒子色1 默认淡紫色

#  – 150

#  – 120

#  – 220

#highlight_points_color_2: # 高光粒子色2 默认淡粉色

#  – 128

#  – 140

#  – 140

 

base_color: # 主色  默认玫瑰粉

  – 228

  – 100

  – 100

highlight_points_color_1: # 高光粒子色1 默认淡紫色

  – 180

  – 87

  – 200

highlight_points_color_2: # 高光粒子色2 默认淡粉色

  – 228

  – 140

  – 140

 

period_time: 1000 * 2  # 周期时间，默认1.5s一个周期

times: 5 # 播放周期数，一个周期跳动1次

frame_num: 24  # 一个周期的生成帧数

wait: 60  # 每一帧停留时间, 设置太短可能造成闪屏，设置 -1 自动设置为 period_time / frame_num

seed_points_num: 2000  # 构成主图的种子粒子数，总粒子数是这个的8倍左右（包括散点和光晕）

highlight_rate: 0.2 # 高光粒子的比例

frame_width: 720  # 窗口宽度，单位像素，设置背景图片后失效

frame_height: 640  # 窗口高度，单位像素，设置背景图片后失效

scale: 9.1  # 主图缩放比例

curve: “butterfly”  # 图案类型：heart, butterfly, star

n_star: 7 # n-角型/星，如果curve设置成star才会生效，五角星：n-star:5, m-star:2

m_star: 3 # curve设置成star才会生效，n-角形 m-star都是1，n-角星 m-star大于1，比如 七角星：n-star:7, m-star:2 或 3

title: “Love Li Xun”  # 仅支持字母，中文乱码

background_img_dir: “src/center_imgs” # 这个目录放置背景图片，建议像素在400 X 400以上，否则可能报错，如果图片实在小，可以调整上面scale把爱心缩小

set_bg_imgs: false # true或false，设置false用默认黑背景

bg_img_scale: 0.6 # 0 – 1，背景图片缩放比例

bg_weight: 0.4 # 0 – 1，背景图片权重，可看做透明度吧

curve_weight: 1 # 同上

 

# ======================== 推荐参数: 直接复制数值替换上面对应参数 ==================================

# 蝴蝶，报错很可能是蝴蝶缩放大小超出窗口宽和高

# curve: “butterfly”

# frame_width: 800

# frame_height: 720

# scale: 60

# base_color: [100, 100, 228]

# highlight_points_color_1: [180, 87, 200]

# highlight_points_color_2: [228, 140, 140]