我使用的代码利用卡尔曼滤波器来稳定视频，但我无法录制要保存的视频。我考虑过将所有帧保存在文件中并稍后创建视频。
如果有人有更好的想法，或者如何修改代码以便可以使用cv2.videoWriter。
答案 0 :(得分：0)
import cv2
import numpy as np
from matplotlib import pyplot as plt
ini = time.time()
class FrameInfo:
def __init__(self):
self.img = None
self.img_gray = None
self.features = []
self.number = 0
self.trajectory = Trajectory()
@property
def width(self):
return self.img_gray.shape[1]
@property
def height(self):
return self.img_gray.shape[0]
@property
def width_height(self):
return self.img_gray.shape[::-1]
videoInPath = "../data/MVI_5408.avi"
camera = cv2.VideoCapture(videoInPath)
videoOutPath = videoInPath +'sab.avi'
N_FRAMES = int(camera.get(cv2.CAP_PROP_FRAME_COUNT))
FPS = camera.get(cv2.CAP_PROP_FPS)
VID_WIDTH = int(camera.get(cv2.CAP_PROP_FRAME_WIDTH))
VID_HEIGHT = int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))
print "N_FRAMES: " + str(N_FRAMES)
print "FPS: " + str(FPS)
frame = None
prev_frame = None
trajectory = Trajectory(0, 0, 0)
org_trajectories = []
stb_trajectories = []
crop = 40
crop_rate = crop / 20
filter = KalmanFilter(Trajectory(4e-2, 4e-2, 4e-2), Trajectory(crop_rate,
crop_rate, crop_rate), Trajectory(1, 1, 1))
#surf = cv2.SURF(4000)
prev_trans = None
frame_number = 0
frame_width = int(1336.0 / 2)
frame_height = int(768.0 / 2)
def resize(img):
return cv2.resize(img, (frame_width, frame_height),
interpolation=cv2.INTER_LANCZOS4)
lk_params = dict(winSize=(19, 19),
maxLevel=2,
criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
feature_params = dict(maxCorners=100,
qualityLevel=0.01,
minDistance=8,
blockSize=19)
crop_rate = 0.9
limits = [int(frame_width * (1 - crop_rate)), int(frame_height * (1 -
crop_rate)), 0.05]
output_video = cv2.VideoWriter(videoOutPath,
cv2.VideoWriter_fourcc(*'XVID'), # -1, #
int(video.get(cv2.cv.CV_CAP_PROP_FOURCC)),
FPS,
(frame_width - 2*limits[0], frame_height - 2*limits[1])
)
feature_cont = 0
flow_cont = 0
ransac_cont = 0
kalman_cont = 0
features_quant = []
percent = 0
inliers_quant = []
ouliers_quant = []
compensate_count = 0
for k in range(N_FRAMES-1):
# Capture frame_img-by-frame_img
ret, frame_img = camera.read()
if frame_img is None:
break
if frame is not None:
prev_frame = frame
frame_number += 1
if frame_number < 220:
continue
frame = FrameInfo()
frame.number = frame_number
frame.img = frame_img
frame.img = cv2.resize(frame_img, (0, 0), fx=(1336.0 / frame_img.shape[1]) / 2.0,
fy=(768.0 / frame_img.shape[0]) / 2.0,
interpolation=cv2.INTER_LANCZOS4)
frame.img_gray = cv2.cvtColor(frame.img, cv2.COLOR_BGR2GRAY)
feature_time_ini = time.time()
frame.features = cv2.goodFeaturesToTrack(frame.img_gray, **feature_params)
#frame.features = sift.detect(frame.img_gray,None)
features_quant.append(len(frame.features))
if prev_frame is None:
continue
feature_time_fim = time.time()
feature_cont += feature_time_fim - feature_time_ini
feature_time_ini = 0
feature_time_fim = 0
flow_time_ini = time.time()
new_features, _, _ = cv2.calcOpticalFlowPyrLK(prev_frame.img, frame.img, prev_frame.features, None, **lk_params)
new_features_for_validation, _, _ = cv2.calcOpticalFlowPyrLK(frame.img, prev_frame.img, new_features, None,
**lk_params)
flow_time_fim = time.time()
flow_cont += flow_time_fim - flow_time_ini
flow_time_ini = 0
flow_time_fim = 0
d = abs(prev_frame.features - new_features_for_validation).reshape(-1, 2).max(-1)
good = d < 1
# Select good_features points
good_new = np.array([x for x, s in zip(new_features, good) if s], dtype=np.float32)
good_old = np.array([x for x, s in zip(prev_frame.features, good) if s], dtype=np.float32)
# trans = cv2.estimateRigidTransform(good_old, good_new, fullAffine=False)
ransac_time_ini = time.time()
trans, inliers_indices,outliers_indices = RansacMotionEstimator(40, 1.0).estimate(good_old, good_new)
ransac_time_fim = time.time()
ransac_cont += ransac_time_fim - ransac_time_ini
ransac_time_ini = 0
ransac_time_fim = 0
inliers_quant.append(len(inliers_indices))
ouliers_quant.append(len(outliers_indices))
if trans is None and prev_trans is None:
print ("wuf? trans is None and prev_trans is none too")
continue
if trans is None:
trans = prev_trans
print ("wut? trans is None")
delta = Trajectory(get_x(trans), get_y(trans), get_rad_angle(trans))
trajectory += delta
kalman_time_ini = time.time()
filter.put(trajectory)
diff = filter.get() - trajectory
new_delta = delta + diff
kalman_time_fim = time.time()
org_trajectories.append(trajectory)
stb_trajectories.append(filter.get())
kalman_cont += kalman_time_fim - kalman_time_ini
kalman_time_ini = 0
kalman_time_fim = 0
fill_mat(trans, new_delta.x, new_delta.y, new_delta.angle)
compensate_time_ini = time.time()
#out = cv2.warpAffine(frame.img, trans, frame.width_height, flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_CONSTANT)
#out = cv2.warpAffine(frame_img, trans, frame.width_height, flags=cv2.INTER_LANCZOS4, borderMode=cv2.BORDER_REFLECT)
#out = cv2.warpAffine(frame_img, trans, frame.width_height)
out = cv2.warpAffine(frame_img, trans, frame.width_height, flags=cv2.INTER_CUBIC,borderMode=cv2.BORDER_REPLICATE)
compensate_time_fim = time.time()
compensate_count += compensate_time_fim - compensate_time_ini
compensate_time_ini = 0
compensate_time_fim = 0
#out = cv2.warpAffine(frame_img, trans, frame.width_height, flags=cv2.INTER_LANCZOS4, borderMode=cv2.BORDER_REFLECT)
#out = frame.img.copy()
prev_trans = trans.copy()
output_video.write(out)
camera.release()
output_video.release()
cv2.destroyAllWindows()