m = cv2.estimateRigidTransform(prev_pts, curr_pts, fullAffine=False) # will only work with OpenCV-3 or less AttributeError: module 'cv2.cv2' has no attribute 'estimateRigidTransform'
时间: 2024-02-03 07:02:27 浏览: 20
这个错误通常是由于您使用的OpenCV版本不支持`estimateRigidTransform`函数或者您的导入方式不正确导致的。`estimateRigidTransform`是OpenCV 3及以下版本的函数,如果您使用的是OpenCV 4或更高版本,则需要使用`cv2.estimateAffinePartial2D`函数替代。
如果您确定您正在使用OpenCV 3及以下版本,则可能是由于您的导入方式不正确导致的。请确保您已正确导入cv2模块并使用正确的语法调用函数,例如:
```python
import cv2
m = cv2.estimateRigidTransform(prev_pts, curr_pts, fullAffine=False)
```
如果问题仍然存在,请检查您的OpenCV安装是否正确,并尝试重新安装或更新OpenCV。
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reward = 0 if self.prev_shaping is not None: reward = shaping - self.prev_shaping self.prev_shaping = shaping
这段代码是用于计算小人的奖励值的,其中包含以下内容:
1. reward = 0:先将奖励值设为0,如果满足一定条件,再根据条件计算出奖励值。
2. if self.prev_shaping is not None::如果之前的奖励值不为空,即小人之前已经做出过行动,那么根据当前状态和之前状态的差别来计算当前的奖励值。
3. reward = shaping - self.prev_shaping:根据当前状态shaping和之前状态的差别(即self.prev_shaping)来计算当前的奖励值reward。
4. self.prev_shaping = shaping:将当前状态的奖励值保存下来,以便在下一次计算奖励值时使用。
总的来说,这段代码是用于计算小人的奖励值的,以便对小人的行动进行评估和优化。奖励值的计算方式可以根据具体情况进行调整,以达到更好的效果。
import cv2 import numpy as np # 创建混合高斯模型 fgbg = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False) # 打开视频文件 cap = cv2.VideoCapture('t1.mp4') # 获取视频帧率、宽度和高度 fps = int(cap.get(cv2.CAP_PROP_FPS)) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 创建前景视频对象 fg_out = cv2.VideoWriter('foreground_video.avi', cv2.VideoWriter_fourcc(*'XVID'), fps, (width, height)) # 初始化上一帧 prev_frame = None # 循环遍历视频帧 while True: ret, frame = cap.read() if not ret: break # 高斯模型背景减除法 fgmask = fgbg.apply(frame) # 缩放比例 scale_percent = 50 # 计算缩放后的新尺寸 width = int(frame.shape[1] * scale_percent / 100) height = int(frame.shape[0] * scale_percent / 100) dim = (width, height) # 缩放图像 frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA) fgmask = cv2.resize(fgmask, dim, interpolation=cv2.INTER_AREA) # 形态学开运算去除噪点 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) opening = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel) # 寻找轮廓并计算周长 contours, hierarchy = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: perimeter = cv2.arcLength(cnt, True) if perimeter > 500: # 画出矩形框 x, y, w, h = cv2.boundingRect(cnt) cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) # 视频稳定 if prev_frame is not None: # 计算帧间差分 diff = cv2.absdiff(frame, prev_frame) # 计算运动向量 _, motion = cv2.optflow.calcOpticalFlowFarneback(prev_frame, frame, None, 0.5, 3, 15, 3, 5, 1.2, 0) # 平移每一帧 M = np.float32([[1, 0, motion[:,:,0].mean()], [0, 1, motion[:,:,1].mean()]]) frame = cv2.warpAffine(frame, M, (frame.shape[1], frame.shape[0])) diff = cv2.warpAffine(diff, M, (diff.shape[1], diff.shape[0])) # 显示帧间差分 cv2.imshow('diff', diff) # 更新上一帧 prev_frame = frame.copy() cv2.imshow('frame', frame) cv2.imshow('fgmask', fgmask) if cv2.waitKey(1) & 0xFF == ord('q'): break # 释放对象 cap.release() fg_out.release() cv2.destroyAllWindows()改为4.5.3版本的opencv能用的程序
import cv2 import numpy as np # 创建混合高斯模型 fgbg = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=50, detectShadows=False) # 打开视频文件 cap = cv2.VideoCapture('t1.mp4') # 获取视频帧率、宽度和高度 fps = int(cap.get(cv2.CAP_PROP_FPS)) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 创建前景视频对象 fourcc = cv2.VideoWriter_fourcc(*'XVID') fg_out = cv2.VideoWriter('foreground_video.avi', fourcc, fps, (width, height)) # 初始化上一帧 prev_frame = None # 循环遍历视频帧 while True: ret, frame = cap.read() if not ret: break # 高斯模型背景减除法 fgmask = fgbg.apply(frame) # 缩放比例 scale_percent = 50 # 计算缩放后的新尺寸 width = int(frame.shape[1] * scale_percent / 100) height = int(frame.shape[0] * scale_percent / 100) dim = (width, height) # 缩放图像 frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA) fgmask = cv2.resize(fgmask, dim, interpolation=cv2.INTER_AREA) # 形态学开运算去除噪点 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) opening = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel) # 寻找轮廓并计算周长 contours, hierarchy = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: perimeter = cv2.arcLength(cnt, True) if perimeter > 500: # 画出矩形框 x, y, w, h = cv2.boundingRect(cnt) cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) # 视频稳定 if prev_frame is not None: # 计算帧间差分 diff = cv2.absdiff(frame, prev_frame) # 计算运动向量 flow = cv2.calcOpticalFlowFarneback(prev_frame, frame, None, 0.5, 3, 15, 3, 5, 1.2, 0) # 平移每一帧 mean_flow = np.mean(flow, axis=(0, 1)) M = np.float32([[1, 0, mean_flow[0]], [0, 1, mean_flow[1]]]) frame = cv2.warpAffine(frame, M, (frame.shape[1], frame.shape[0])) diff = cv2.warpAffine(diff, M, (diff.shape[1], diff.shape[0])) # 显示帧间差分 cv2.imshow('diff', diff) # 更新上一帧 prev_frame = frame.copy() cv2.imshow('frame', frame) cv2.imshow('fgmask', fgmask) if cv2.waitKey(1) & 0xFF == ord('q'): break # 释放对象 cap.release() fg_out.release() cv2.destroyAllWindows()