import cv2
def extract_frames(video_path, interval):    # 打開視頻文件    cap = cv2.VideoCapture(video_path)    # 計算視頻總幀數(shù)和幀率    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))    fps = cap.get(cv2.CAP_PROP_FPS)    # 計算抽幀間隔    interval_frames = int(interval * fps)    # 初始化幀計數(shù)器和關鍵幀列表    count = 0    frames = []    # 逐幀遍歷視頻    while True:        ret, frame = cap.read()        if not ret:            break        count += 1        # 如果是關鍵幀，將其添加到關鍵幀列表中        if count % interval_frames == 0:            frames.append(frame)    # 關閉視頻文件    cap.release()    return frames

frames = extract_frames('video.mp4', 1)  # 抽取間隔為1秒的關鍵幀for frame in frames:    cv2.imshow('frame', frame)    cv2.waitKey(0)cv2.destroyAllWindows()

import cv2import numpy as np
def extract_depth_edges(depth_img):    # 計算Sobel算子的卷積核    sobelx = cv2.Sobel(depth_img, cv2.CV_64F, 1, 0, ksize=3)    sobely = cv2.Sobel(depth_img, cv2.CV_64F, 0, 1, ksize=3)    # 計算梯度幅值和方向    grad_mag = np.sqrt(sobelx ** 2 + sobely ** 2)    grad_dir = np.arctan2(sobely, sobelx)    # 將梯度幅值歸一化到0-255之間    grad_mag_norm = cv2.normalize(grad_mag, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)    # 將梯度方向轉換為角度    grad_dir_deg = (grad_dir * 180 / np.pi) % 180    # 應用非極大值抑制    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
# 假設我們已經(jīng)提取了深度圖像的邊緣信息,，存儲在名為edge_img的NumPy數(shù)組中
# 將邊緣值縮放到0到1之間edge_img = edge_img / 255.0
# 將邊緣信息轉換為字符串格式edge_str = np.array2string(edge_img, separator=',', formatter={'float_kind':lambda x: "%.5f" % x})
# 將字符串寫入txt文件with open('edge_info.txt', 'w') as f:    f.write(edge_str)

import cv2import numpy as np
def extract_edge(frame, threshold):    # 使用高斯模糊平滑圖像    blurred = cv2.GaussianBlur(frame, (3, 3), 0)    # 轉換為灰度圖像    gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)    # 使用Canny算法提取邊緣    edges = cv2.Canny(gray, threshold, threshold * 2)    return edges
def save_edges_to_txt(edges, filename):    # 將邊緣值縮放到0到1之間    edges = edges / 255.0    # 將邊緣信息轉換為字符串格式    edge_str = np.array2string(edges, separator=',', formatter={'float_kind':lambda x: "%.5f" % x})    # 將字符串寫入txt文件    with open(filename, 'w') as f:        f.write(edge_str)
# 讀取深度圖像depth_img = cv2.imread('depth_img.png')# 指定抽幀間隔interval = 10# 提取深度圖像邊緣edges = extract_edge(depth_img, 50)# 抽幀,，保留每隔interval個像素sampled_edges = edges[::interval, ::interval]# 將邊緣信息保存到txt文件中save_edges_to_txt(sampled_edges, 'edge_info.txt')

import cv2import numpy as np
def load_edges_from_txt(filename, shape):    # 從txt文件中讀取邊緣信息    edge_str = np.loadtxt(filename, delimiter=',')    # 創(chuàng)建全零數(shù)組    edges = np.zeros(shape)    # 將邊緣信息復制到全零數(shù)組的對應位置上    np.put(edges, np.arange(shape[0]*shape[1]), edge_str)    # 對全零數(shù)組進行插值操作    edges = cv2.resize(edges, (shape[1], shape[0]))    # 對插值后的邊緣圖像進行二值化處理    ret, edges = cv2.threshold(edges, 0, 255, cv2.THRESH_BINARY)    return edges
# 讀取深度圖像depth_img = cv2.imread('depth_img.png')# 獲取深度圖像大小height, width = depth_img.shape[:2]# 從txt文件中加載邊緣信息,，并恢復成圖像edges = load_edges_from_txt('edge_info.txt', (height//10, width//10))# 顯示原始深度圖像和恢復的邊緣圖像cv2.imshow('depth_img', depth_img)cv2.imshow('edges', edges)cv2.waitKey(0)cv2.destroyAllWindows()

import cv2
# 讀取原始圖像img = cv2.imread('original_image.png')# 縮放圖像new_img = cv2.resize(img, (10, 10), interpolation=cv2.INTER_AREA)# 顯示原始圖像和縮放后的圖像cv2.imshow('original', img)cv2.imshow('new', new_img)cv2.waitKey(0)cv2.destroyAllWindows()