使用opencv调用TV_L1算法提取光流

提取视频的光流并使用灰度图可视化

import cv2
import numpy as np
import pickle
import osdef compute_tvl1_optical_flow(video_path):# 创建视频捕获对象cap = cv2.VideoCapture(video_path)ret, frame1 = cap.read()if not ret:print("Failed to read video")return None  # 返回None，表示无法读取视频# 将第一帧转换为灰度prvs = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)# 创建TVL1光流对象optical_flow = cv2.optflow.DualTVL1OpticalFlow_create()# 初始化用于存储光流数据的列表flows = []# 读取视频帧并计算光流while True:ret, frame2 = cap.read()if not ret:break# 将当前帧转换为灰度next = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)# 计算光流flow = optical_flow.calc(prvs, next, None)# 将光流数据添加到列表中flows.append(flow)# 更新前一帧prvs = nextcap.release()# 返回光流数据列表return flowsdef save_optical_flows(flows, filepath):with open(filepath, 'wb') as file:pickle.dump(flows, file)def draw_gray(flow, scale=1, gamma=0.1):# 计算每个像素点的速度大小magnitude = np.sqrt(flow[:, :, 0] ** 2 + flow[:, :, 1] ** 2)# 应用gamma校正来增强低亮度区域的可视化magnitude = np.power(magnitude, gamma)# 将速度大小归一化到0-255的范围normalized_mag = cv2.normalize(magnitude, None, 0, 255, cv2.NORM_MINMAX)# 将浮点数转换为8-bit整数，并调整亮度gray_img = np.uint8(normalized_mag * scale)return gray_img# 使用函数
video_path = '/home/yunchuan/video_features-master/sample/v_ZNVhz7ctTq0.mp4'
output_dir = '/home/yunchuan/video_features-master/sample/flow_images/'
if not os.path.exists(output_dir):os.makedirs(output_dir)flows = compute_tvl1_optical_flow(video_path)if flows is not None:print("Successfully computed optical flows.")save_optical_flows(flows, '/home/yunchuan/video_features-master/sample/flows.pkl')  # 保存光流数据for i, flow in enumerate(flows):img = draw_gray(flow)flow_image_path = os.path.join(output_dir, f'gray_flow_{i}.png')cv2.imwrite(flow_image_path, img)  # 保存光流图像cv2.destroyAllWindows()
else:print("Failed to compute optical flows.")

计算两张图片之间的光流并使用灰度图可视化

import cv2
import numpy as np
import osdef calculate_optical_flow(image1_path, image2_path):# 读取两张图片frame1 = cv2.imread(image1_path)frame2 = cv2.imread(image2_path)if frame1 is None or frame2 is None:print("Error loading images!")return None# 检查并调整两张图片的尺寸，确保它们大小相同h1, w1 = frame1.shape[:2]h2, w2 = frame2.shape[:2]h = min(h1, h2)w = min(w1, w2)h=180w=280# 将图片大小调整为最小的共同尺寸frame1 = cv2.resize(frame1, (w, h))frame2 = cv2.resize(frame2, (w, h))# 将图片转换为灰度prvs = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)next = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)# 创建TVL1光流对象optical_flow = cv2.optflow.DualTVL1OpticalFlow_create()# 计算两张图片之间的光流flow = optical_flow.calc(prvs, next, None)return flowdef draw_gray(flow, scale=1, gamma=0.1):# 计算每个像素点的速度大小magnitude = np.sqrt(flow[:, :, 0] ** 2 + flow[:, :, 1] ** 2)# 应用gamma校正来增强低亮度区域的可视化magnitude = np.power(magnitude, gamma)# 将速度大小归一化到0-255的范围normalized_mag = cv2.normalize(magnitude, None, 0, 255, cv2.NORM_MINMAX)# 将浮点数转换为8-bit整数，并调整亮度gray_img = np.uint8(normalized_mag * scale)return gray_img# 指定两张图片的路径
image1_path = '/home/yunchuan/video_features-master/sample_images/image1.jpg'
image2_path = '/home/yunchuan/video_features-master/sample_images/image2.jpg'# 输出路径
output_dir = '/home/yunchuan/video_features-master/sample_images/'
if not os.path.exists(output_dir):os.makedirs(output_dir)# 计算光流
flow = calculate_optical_flow(image1_path, image2_path)
if flow is not None:# 可视化光流为灰度图gray_flow_image = draw_gray(flow)gray_flow_image_path = os.path.join(output_dir, 'optical_flow_visualization.png')cv2.imwrite(gray_flow_image_path, gray_flow_image)print(f"Optical flow visualization saved to {gray_flow_image_path}")# 保存原始光流数据raw_flow_path = os.path.join(output_dir, 'raw_optical_flow.npy')np.save(raw_flow_path, flow)print(f"Raw optical flow data saved to {raw_flow_path}")
else:print("Failed to compute optical flow.")