零基础入门：Computer Science Circles带你轻松学Python编程

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"Python_Computer Science Circles" 是一个面向零编程基础人群的在线学习平台，由滑铁卢大学数学与计算机教育中心开发，提供免费的Python编程教育资源。该网站设计初衷是为了让任何人都能够轻松入门编程，无需在本地安装任何软件，利用其半互动式的教学方式教授基础知识。 该课程的特点包括： 1. 友好入门：针对完全新手，无需编程背景，任何人都可以访问并开始学习。课程采用逐步引导的方式，通过一系列清晰的教程和实践练习，帮助用户建立基础概念。 2. 免费与账户管理：网站是免费的，用户可以创建个人账户，所有学习进度会被自动保存，方便跟踪和回溯学习过程。中文界面也提供了便利，点击“zh”按钮即可切换到适合中国用户的界面。 3. 互动学习体验：网站设计有控制台和可视化工具，学习者不仅能在课程中进行练习，还能在独立环境中运用这些工具编写和运行代码。控制台用于实时反馈，可视化工具则有助于理解和掌握编程逻辑。 4. 多语言支持：虽然推荐从英文版开始学习，因为编程领域英语是主导，但网站同时提供中英文版本，便于不同语言背景的学习者适应。 5. 学习进度展示：通过个人菜单，用户可以看到自己的学习进度，包括已完成的练习和获得的成就，如图所示。 6. 视觉化教学：提供的动态图像和步骤说明有助于直观理解编程概念，使复杂的过程变得简单易懂。 "Computer Science Circles" 是一个非常适合初学者的Python学习平台，通过实践和逐步深入的教学方法，为无编程基础的人提供了一个系统且易于跟进的学习路径。无论是为了个人兴趣还是职业发展，这个资源都是一个值得尝试的起点。

circlematlab.zip_CircleFinder_circles

2022-07-13 上传
CircleFinder finds circles in an RGB or grayscale Useage example: [r , c , rad] = circlefinder(im) finds circles with the default settings. See also: RGBCircle nonmaxsup1d houghtransform

text.zip_opencv_python_python opencv

2022-07-15 上传
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param1=50, param2=30, minRadius=0, maxRadius=0) ``` `HoughCircles()`函数返回一个包含潜在圆心坐标和半径的数组。参数需要根据实际情况调整。 ...

circles.rar_circles_stability circles_稳定性判断圆_稳定性圆、增益圆、噪声圆、_等增益圆

2022-07-14 上传
标题提到的"circles.rar_circles_stability circles_稳定性判断圆_稳定性圆、增益圆、噪声圆、_等增益圆"是一个关于电路稳定性的专题，涉及到的是电路理论中的“圆图法”，这是一种分析线性时不变系统（LTI）稳定性...

可以把这段代码精简吗import sensor, image, time, lcd from pyb import UART sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time = 2000) lcd.init() sensor.set_auto_gain(False) sensor.set_auto_whitebal(False) clock = time.clock() uart = UART(3, 19200, timeout_char=2000) while(True): clock.tick() img = sensor.snapshot().lens_corr(1.8) lcd.display(img) for c in img.find_circles(threshold = 3500, x_margin = 10, y_margin = 10, r_margin = 10, r_min = 2, r_max = 100, r_step = 2): area = (c.x()-c.r(), c.y()-c.r(), 2c.r(), 2c.r()) statistics = img.get_statistics(roi=area) print(statistics) if 0<statistics.l_mode()<100 and 0<statistics.a_mode()<127 and 0<statistics.b_mode()<127: img.draw_circle(c.x(), c.y(), c.r(), color = (255, 0, 0)) uart.write("find red cirl!\n") time.sleep_ms(2000) else: img.draw_rectangle(area, color = (255, 255, 255)) uart.write("nothing!\n") time.sleep_ms(2000) print("FPS %f" % clock.fps())

2023-06-12 上传
```python import sensor, image, time, lcd from pyb import UART sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time=2000) lcd.init() sensor....

可以把这段代码精简吗import sensor, image, time from pyb import UART sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time = 2000) sensor.set_auto_gain(False) # must be turned off for color tracking sensor.set_auto_whitebal(False) # must be turned off for color tracking clock = time.clock() uart = UART(3, 19200, timeout_char=2000) while(True): clock.tick() img = sensor.snapshot().lens_corr(1.8) for c in img.find_circles(threshold = 3500, x_margin = 10, y_margin = 10, r_margin = 10, r_min = 2, r_max = 100, r_step = 2): area = (c.x()-c.r(), c.y()-c.r(), 2*c.r(), 2*c.r()) #area为识别到的圆的区域，即圆的外接矩形框 statistics = img.get_statistics(roi=area)#像素颜色统计 print(statistics) #(0,100,0,120,0,120)是红色的阈值，所以当区域内的众数（也就是最多的颜色），范围在这个阈值内，就说明是红色的圆。 #l_mode()，a_mode()，b_mode()是L通道，A通道，B通道的众数。 if 0<statistics.l_mode()<100 and 0<statistics.a_mode()<127 and 0<statistics.b_mode()<127:#if the circle is red img.draw_circle(c.x(), c.y(), c.r(), color = (255, 0, 0))#识别到的红色圆形用红色的圆框出来 uart.write("Hello esp32!\r") time.sleep_ms(2000) else: img.draw_rectangle(area, color = (255, 255, 255)) #将非红色的圆用白色的矩形框出来 print("FPS %f" % clock.fps())

2023-06-12 上传
```python import sensor, image, time from pyb import UART sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time=2000) sensor.set_auto_gain...

import sensor, image, time sensor.reset() sensor.set_pixformat(sensor.RGB565) # 灰度更快 sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time = 2000) clock = time.clock() while(True): clock.tick() #lens_corr(1.8)畸变矫正 img = sensor.snapshot().lens_corr(1.8) for c in img.find_circles(threshold = 3500, x_margin = 10, y_margin = 10, r_margin = 10,r_min = 2, r_max = 100, r_step = 2): img.draw_circle(c.x(), c.y(), c.r(), color = (255, 0, 0)) print(c) clock.tick() img = sensor.snapshot() for r in img.find_rects(threshold = 10000): img.draw_rectangle(r.rect(), color = (255, 0, 0)) for p in r.corners(): img.draw_circle(p[0], p[1], 5, color = (0, 255, 0)) print(r) print("FPS %f" % clock.fps()) print("FPS %f" % clock.fps()) for l in img.find_line_segments(merge_distance = 10, max_theta_diff = 10): img.draw_line(l.line(), color = (255, 0, 0)) sum += l.theta() sum -= 180 if sum<110 and sum>1: print('三角形') num_segment=1

2023-07-15 上传
```python import sensor import image import time sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time=2000) clock = time.clock() while True: ...

openMV用UART库将以下代码通过串口发送给arduino: import sensor import image import time sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time=2000) clock = time.clock() while True: clock.tick() img = sensor.snapshot().lens_corr(1.8) #检测圆形 for c in img.find_circles(threshold=3500, x_margin=10, y_margin=10, r_margin=10, r_min=2, r_max=100, r_step=2): img.draw_circle(c.x(), c.y(), c.r(), color=(255, 0, 0)) print(c) img = sensor.snapshot() #检测矩形 for r in img.find_rects(threshold=10000): img.draw_rectangle(r.rect(), color=(255, 0, 0)) for p in r.corners(): img.draw_circle(p[0], p[1], 5, color=(0, 255, 0)) print(r) #检测三角形 sum_theta = 0 count = 0 for l in img.find_line_segments(merge_distance=10, max_theta_diff=10): img.draw_line(l.line(), color=(255, 0, 0)) sum_theta += l.theta() count += 1 avg_theta = sum_theta / count if count > 0 else 0 if 1 < avg_theta < 110: print('三角形') print("FPS %f" % clock.fps())

2023-07-16 上传
```python import serial # 打开串口 ser = serial.Serial('/dev/ttyUSB0', 9600) # 根据实际情况修改串口号和波特率 # 发送代码 code = ''' import sensor import image import time sensor.reset() sensor.set_...

将以下openMV代码和Arduino mega2560 实现串口通信 i import sensor import image import time sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QQVGA) sensor.skip_frames(time=2000) clock = time.clock() while True: clock.tick() img = sensor.snapshot().lens_corr(1.8) # 检测圆形 for c in img.find_circles(threshold=3500, x_margin=10, y_margin=10, r_margin=10, r_min=2, r_max=100, r_step=2): img.draw_circle(c.x(), c.y(), c.r(), color=(255, 0, 0)) print(c) img = sensor.snapshot() # 检测矩形 for r in img.find_rects(threshold=10000): img.draw_rectangle(r.rect(), color=(255, 0, 0)) for p in r.corners(): img.draw_circle(p[0], p[1], 5, color=(0, 255, 0)) print(r) # 检测三角形 sum_theta = 0 count = 0 for l in img.find_line_segments(merge_distance=10, max_theta_diff=10): img.draw_line(l.line(), color=(255, 0, 0)) sum_theta += l.theta() count += 1 avg_theta = sum_theta / count if count > 0 else 0 if 1 < avg_theta < 110: print('三角形') print("FPS %f" % clock.fps())

2023-07-16 上传
for c in img.find_circles(threshold=3500, x_margin=10, y_margin=10, r_margin=10, r_min=2, r_max=100, r_step=2): img.draw_circle(c.x(), c.y(), c.r(), color=(255, 0, 0)) print(c) img = sensor....

修改此代码使其可重复运行import pygame import sys from pygame.locals import * from robomaster import * import cv2 import numpy as np focal_length = 750 # 焦距 known_radius = 2 # 已知球的半径 def calculate_distance(focal_length, known_radius, perceived_radius): distance = (known_radius * focal_length) / perceived_radius return distance def show_video(ep_robot, screen): 获取机器人第一视角图像帧 img = ep_robot.camera.read_cv2_image(strategy="newest") 转换图像格式，转换为pygame的surface对象 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = cv2.transpose(img) # 行列互换 img = pygame.surfarray.make_surface(img) screen.blit(img, (0, 0)) # 绘制图像 def detect_white_circle(ep_robot): 获取机器人第一视角图像帧 img = ep_robot.camera.read_cv2_image(strategy="newest") 转换为灰度图像 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 进行中值滤波处理 gray = cv2.medianBlur(gray, 5) 检测圆形轮廓 circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 50, param1=160, param2=40, minRadius=5, maxRadius=60) if circles is not None: circles = np.uint16(np.around(circles)) for circle in circles[0, :]: center = (circle[0], circle[1]) known_radius = circle 在图像上绘制圆形轮廓 cv2.circle(img, center, known_radius, (0, 255, 0), 2) 显示图像 distance = calculate_distance(focal_length, known_radius, known_radius) 在图像上绘制圆和距离 cv2.circle(img, center, known_radius, (0, 255, 0), 2) cv2.putText(img, f"Distance: {distance:.2f} cm", (center[0] - known_radius, center[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.imshow("White Circle Detection", img) cv2.waitKey(1) def main(): pygame.init() screen_size = width, height = 1280, 720 screen = pygame.display.set_mode(screen_size) ep_robot = robot.Robot() ep_robot.initialize(conn_type='ap') version = ep_robot.get_version() print("Robot version: {0}".format(version)) ep_robot.camera.start_video_stream(display=False) pygame.time.wait(100) clock = pygame.time.Clock() while True: clock.tick(5) # 将帧数设置为25帧 for event in pygame.event.get(): if event.type == QUIT: ep_robot.close() pygame.quit() sys.exit() show_video(ep_robot, screen) detect_white_circle(ep_robot) if name == 'main': main()

2023-07-13 上传
修改后的代码如下所示： ... circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 50, param1=160, param2=40, minRadius=5, maxRadius=60) if circles is not None: circles = np.uint16(np.around(circles

sklearn.datasets.make_circles

2023-03-16 上传
sklearn.datasets.make_circles是一个函数，用于生成一个圆形的数据集。该函数可以生成两个同心圆的数据集，其中内圆和外圆之间有一定的距离。生成的数据集可以用于分类问题或聚类问题的训练和测试。函数的参数包括...
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