def generate_text(self, max_length): text = [] for i in range(max_length): print(self.topK_sampling(5))
时间: 2023-11-22 07:53:17 浏览: 35
这是一个生成文本的函数,它使用了 topK sampling 技术来生成文本。在每次循环中,它会调用 topK_sampling 函数,并将生成的文本添加到一个列表中。循环次数由 max_length 决定。具体实现可以参考下面的代码:
```
def generate_text(self, max_length):
text = []
for i in range(max_length):
generated = self.topK_sampling(5)
text.append(generated)
return " ".join(text)
```
这个函数将生成的文本列表转换成一个字符串,并用空格将其连接起来,最后返回生成的文本。
相关问题
编写一个python的类, 实现读取a.txt的内容, 生成文字瀑布流视频
当然,我可以帮你编写一个这样的类。以下是一个示例代码:
```python
import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont
class TextWaterfallVideoGenerator:
def __init__(self, text_file, output_file, font_path, font_size=32, text_color=(255, 255, 255), background_color=(0, 0, 0), fps=30):
self.text_file = text_file
self.output_file = output_file
self.font_path = font_path
self.font_size = font_size
self.text_color = text_color
self.background_color = background_color
self.fps = fps
def generate_video(self):
# 读取文本文件内容
with open(self.text_file, 'r', encoding='utf-8') as file:
lines = file.readlines()
# 计算视频尺寸
max_line_length = max(len(line) for line in lines)
video_width = max_line_length * self.font_size
video_height = len(lines) * self.font_size
# 创建视频写入器
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
video_writer = cv2.VideoWriter(self.output_file, fourcc, self.fps, (video_width, video_height))
# 创建画布
image = Image.new('RGB', (video_width, video_height), self.background_color)
draw = ImageDraw.Draw(image)
font = ImageFont.truetype(self.font_path, self.font_size)
frame_count = 0
for line in lines:
# 清空画布
draw.rectangle((0, 0, video_width, video_height), fill=self.background_color)
# 写入文本
draw.text((0, 0), line, font=font, fill=self.text_color)
# 将画布转为OpenCV图像
frame = np.array(image)
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
# 写入视频帧
for _ in range(self.fps):
video_writer.write(frame)
frame_count += 1
video_writer.release()
print(f"视频生成成功,总共生成了 {frame_count} 帧。")
```
使用方法示例:
```python
text_file = 'a.txt' # 替换为你的文本文件路径
output_file = 'output.mp4' # 替换为你的输出视频文件路径
font_path = 'font.ttf' # 替换为你的字体文件路径
generator = TextWaterfallVideoGenerator(text_file, output_file, font_path)
generator.generate_video()
```
请确保已安装必要的依赖库(cv2、numpy、Pillow)。此示例代码将文本文件中的每一行作为视频的一帧,生成一个文字瀑布流视频。你可以根据需要自定义字体、字体大小、文本颜色、背景颜色和帧率等参数。
无人机路径规划代码
以下是一个基于遗传算法的无人机路径规划的Python代码实现:
```python
import random
class Gene:
def __init__(self, x, y):
self.x = x
self.y = y
def __str__(self):
return f'({self.x}, {self.y})'
class Chromosome:
def __init__(self, genes):
self.genes = genes
self.fitness = 0
def __str__(self):
return ' -> '.join(str(g) for g in self.genes)
class GA:
def __init__(self, map, population_size, mutation_rate, elite_rate, crossover_rate, max_generation):
self.map = map
self.width = len(map[0])
self.height = len(map)
self.population_size = population_size
self.mutation_rate = mutation_rate
self.elite_rate = elite_rate
self.crossover_rate = crossover_rate
self.max_generation = max_generation
self.population = []
def generate_population(self):
for i in range(self.population_size):
genes = [Gene(random.randint(0, self.width-1), random.randint(0, self.height-1)) for j in range(self.width)]
chromosome = Chromosome(genes)
self.population.append(chromosome)
def evaluate_fitness(self):
for chromosome in self.population:
path_length = 0
for i in range(self.width-1):
x1, y1 = chromosome.genes[i].x, chromosome.genes[i].y
x2, y2 = chromosome.genes[i+1].x, chromosome.genes[i+1].y
path_length += abs(x1 - x2) + abs(y1 - y2)
chromosome.fitness = 1 / path_length
def select_elite(self):
elite_size = int(self.elite_rate * self.population_size)
elite = sorted(self.population, key=lambda c: c.fitness, reverse=True)[:elite_size]
return elite
def selection(self):
total_fitness = sum(c.fitness for c in self.population)
p = [c.fitness / total_fitness for c in self.population]
indices = random.choices(range(self.population_size), weights=p, k=self.population_size)
return [self.population[i] for i in indices]
def crossover(self, parent1, parent2):
if random.random() > self.crossover_rate:
return parent1, parent2
start = random.randint(0, self.width-1)
end = random.randint(start, self.width-1)
genes1 = [g for g in parent1.genes if g not in parent2.genes[start:end+1]]
genes2 = [g for g in parent2.genes if g not in parent1.genes[start:end+1]]
genes = genes1[:start] + parent2.genes[start:end+1] + genes1[start:]
child1 = Chromosome(genes)
genes = genes2[:start] + parent1.genes[start:end+1] + genes2[start:]
child2 = Chromosome(genes)
return child1, child2
def mutation(self, chromosome):
if random.random() > self.mutation_rate:
return chromosome
indices = random.sample(range(self.width), 2)
i, j = indices[0], indices[1]
chromosome.genes[i], chromosome.genes[j] = chromosome.genes[j], chromosome.genes[i]
return chromosome
def evolve(self):
self.generate_population()
for generation in range(self.max_generation):
self.evaluate_fitness()
elite = self.select_elite()
offspring = []
while len(offspring) < self.population_size - len(elite):
parents = random.sample(self.selection(), 2)
children = self.crossover(parents[0], parents[1])
offspring.extend(children)
offspring = [self.mutation(c) for c in offspring]
self.population = elite + offspring
best_chromosome = max(self.population, key=lambda c: c.fitness)
print(f'Generation {generation+1}: Best Path = {best_chromosome}, Fitness = {best_chromosome.fitness}')
```
这里的 `Gene` 类代表地图上的一个点,包含其坐标。 `Chromosome` 类代表一个无人机路径,由多个 `Gene` 组成。 `GA` 类中的 `generate_population` 方法用于初始化种群,`evaluate_fitness` 方法用于评估每个个体的适应度,`select_elite` 方法用于选择精英个体,`selection` 方法用于选择父代,`crossover` 方法用于交叉产生后代,`mutation` 方法用于变异产生新个体,`evolve` 方法实现了遗传算法的主要逻辑,包括选择、交叉、变异等步骤,通过不断迭代,得到最优的无人机路径。