发布时间：2025-06-24 18:42:57  作者：北方职教升学中心  阅读量：874


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#  - 140base_color: # 主色  默认玫瑰粉  - 228  - 100  - 100highlight_points_color_1: # 高光粒子色1 默认淡紫色  - 180  - 87  - 200highlight_points_color_2: # 高光粒子色2 默认淡粉色  - 228  - 140  - 140period_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_numseed_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, starn_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:2m_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 3title: "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]

本代码是搬运github上的：

网址如下：

https://github.com/131250208/FunnyToys/blob/main/heart.py

演示：

 

python程序代码：heart.py

from math import cos, piimport numpy as npimport cv2import os, globclass 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 // 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 // 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 // 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 < 2 * pi), 2 * pi - t, t)  # 翻转x > 0部分的图形到3、
上一篇：SQL在云计算中的新角色：重新定义数据分析
下一篇：2024年贝壳金融推荐目的地-放松身心城市漫游