不会吧？不会吧？不会吧？不会有人忘记我还会写图像处理的代码吧？别说了，我知道你忘了，没关系，我会在这篇文章写一些很简短的代码实现常见的图像处理工作（别问为啥写不长，能力有限，20行开外就不受控制了）。
import cv2def extract_frames(video_path, interval):# 打开视频文件cap = cv2.VideoCapture(video_path)# 计算视频总帧数和帧率frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))fps = cap.get(cv2.CAP_PROP_FPS)# 计算抽帧间隔interval_frames = int(interval * fps)# 初始化帧计数器和关键帧列表count = 0frames = []# 逐帧遍历视频while True:frame = cap.read()if not ret:breakcount += 1# 如果是关键帧，将其添加到关键帧列表中if count % interval_frames == 0:frames.append(frame)# 关闭视频文件cap.release()return frames
frames = extract_frames('video.mp4', 1) # 抽取间隔为1秒的关键帧for frame in frames:cv2.imshow('frame', frame)cv2.waitKey(0)cv2.destroyAllWindows()
import cv2import numpy as npdef extract_depth_edges(depth_img):# 计算Sobel算子的卷积核sobelx = cv2.Sobel(depth_img, cv2.CV_64F, 1, 0, ksize=3)sobely = cv2.Sobel(depth_img, cv2.CV_64F, 0, 1, ksize=3)# 计算梯度幅值和方向grad_mag = np.sqrt(sobelx ** 2 + sobely ** 2)grad_dir = np.arctan2(sobely, sobelx)# 将梯度幅值归一化到0-255之间grad_mag_norm = cv2.normalize(grad_mag, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)# 将梯度方向转换为角度grad_dir_deg = (grad_dir * 180 / np.pi) % 180# 应用非极大值抑制grad_mag_nms = cv2.Canny(grad_mag_norm, 100, 200)return grad_mag_nms
depth_img = cv2.imread('depth_image.png', cv2.IMREAD_GRAYSCALE)edges = extract_depth_edges(depth_img)cv2.imshow('depth_edges', edges)cv2.waitKey(0)cv2.destroyAllWindows()
import numpy as np# 假设我们已经提取了深度图像的边缘信息，存储在名为edge_img的NumPy数组中# 将边缘值缩放到0到1之间edge_img = edge_img / 255.0# 将边缘信息转换为字符串格式edge_str = np.array2string(edge_img, separator=',', formatter={'float_kind':lambda x: "%.5f" % x})# 将字符串写入txt文件with open('edge_info.txt', 'w') as f:f.write(edge_str)
import cv2import numpy as npdef extract_edge(frame, threshold):# 使用高斯模糊平滑图像blurred = cv2.GaussianBlur(frame, (3, 3), 0)# 转换为灰度图像gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)# 使用Canny算法提取边缘edges = cv2.Canny(gray, threshold, threshold * 2)return edgesdef save_edges_to_txt(edges, filename):# 将边缘值缩放到0到1之间edges = edges / 255.0# 将边缘信息转换为字符串格式edge_str = np.array2string(edges, separator=',', formatter={'float_kind':lambda x: "%.5f" % x})# 将字符串写入txt文件with open(filename, 'w') as f:f.write(edge_str)# 读取深度图像depth_img = cv2.imread('depth_img.png')# 指定抽帧间隔interval = 10# 提取深度图像边缘edges = extract_edge(depth_img, 50)# 抽帧，保留每隔interval个像素sampled_edges = edges[::interval, ::interval]# 将边缘信息保存到txt文件中save_edges_to_txt(sampled_edges, 'edge_info.txt')
import cv2import numpy as npdef load_edges_from_txt(filename, shape):# 从txt文件中读取边缘信息edge_str = np.loadtxt(filename, delimiter=',')# 创建全零数组edges = np.zeros(shape)# 将边缘信息复制到全零数组的对应位置上np.put(edges, np.arange(shape[0]*shape[1]), edge_str)# 对全零数组进行插值操作edges = cv2.resize(edges, (shape[1], shape[0]))# 对插值后的边缘图像进行二值化处理ret, edges = cv2.threshold(edges, 0, 255, cv2.THRESH_BINARY)return edges# 读取深度图像depth_img = cv2.imread('depth_img.png')# 获取深度图像大小height, width = depth_img.shape[:2]# 从txt文件中加载边缘信息，并恢复成图像edges = load_edges_from_txt('edge_info.txt', (height//10, width//10))# 显示原始深度图像和恢复的边缘图像cv2.imshow('depth_img', depth_img)cv2.imshow('edges', edges)cv2.waitKey(0)cv2.destroyAllWindows()
import cv2# 读取原始图像img = cv2.imread('original_image.png')# 缩放图像new_img = cv2.resize(img, (10, 10), interpolation=cv2.INTER_AREA)# 显示原始图像和缩放后的图像cv2.imshow('original', img)cv2.imshow('new', new_img)cv2.waitKey(0)cv2.destroyAllWindows()
