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 < 2 * pi), 2 * pi - t, t)  # 翻转x > 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]

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