def calibrate_cameras(config, cbrow=8, cbcol=6, calibrate=False, alpha=0.4):
"""This function extracts the corners points from the calibration images, calibrates the camera and stores the calibration files in the project folder (defined in the config file).
Make sure you have around 20-60 pairs of calibration images. The function should be used iteratively to select the right set of calibration images.
A pair of calibration image is considered "correct", if the corners are detected correctly in both the images. It may happen that during the first run of this function,
the extracted corners are incorrect or the order of detected corners does not align for the corresponding views (i.e. camera-1 and camera-2 images).
In such a case, remove those pairs of images and re-run this function. Once the right number of calibration images are selected,
use the parameter ``calibrate=True`` to calibrate the cameras.
Parameters
----------
config : string
Full path of the config.yaml file as a string.
cbrow : int
Integer specifying the number of rows in the calibration image.
cbcol : int
Integer specifying the number of columns in the calibration image.
calibrate : bool
If this is set to True, the cameras are calibrated with the current set of calibration images. The default is ``False``
Set it to True, only after checking the results of the corner detection method and removing dysfunctional images!
alpha: float
Floating point number between 0 and 1 specifying the free scaling parameter. When alpha = 0, the rectified images with only valid pixels are stored
i.e. the rectified images are zoomed in. When alpha = 1, all the pixels from the original images are retained.
For more details: https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html
Example
--------
Linux/MacOs/Windows
>>> deeplabcutcore.calibrate_camera(config)
Once the right set of calibration images are selected,
>>> deeplabcutcore.calibrate_camera(config,calibrate=True)
"""
# Termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# Prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((cbrow * cbcol, 3), np.float32)
objp[:, :2] = np.mgrid[0:cbcol, 0:cbrow].T.reshape(-1, 2)
# Read the config file
cfg_3d = auxiliaryfunctions.read_config(config)
img_path, path_corners, path_camera_matrix, path_undistort = auxiliaryfunctions_3d.Foldernames3Dproject(
cfg_3d
)
images = glob.glob(os.path.join(img_path, "*.jpg"))
cam_names = cfg_3d["camera_names"]
# update the variable snapshot* in config file according to the name of the cameras
try:
for i in range(len(cam_names)):
cfg_3d[str("config_file_" + cam_names[i])] = cfg_3d.pop(
str("config_file_camera-" + str(i + 1))
)
for i in range(len(cam_names)):
cfg_3d[str("shuffle_" + cam_names[i])] = cfg_3d.pop(
str("shuffle_camera-" + str(i + 1))
)
except:
pass
project_path = cfg_3d["project_path"]
projconfigfile = os.path.join(str(project_path), "config.yaml")
auxiliaryfunctions.write_config_3d(projconfigfile, cfg_3d)
# Initialize the dictionary
img_shape = {}
objpoints = {} # 3d point in real world space
imgpoints = {} # 2d points in image plane.
dist_pickle = {}
stereo_params = {}
for cam in cam_names:
objpoints.setdefault(cam, [])
imgpoints.setdefault(cam, [])
dist_pickle.setdefault(cam, [])
# Sort the images.
images.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
if len(images) == 0:
raise Exception(
"No calibration images found. Make sure the calibration images are saved as .jpg and with prefix as the camera name as specified in the config.yaml file."
)
for fname in images:
for cam in cam_names:
if cam in fname:
filename = Path(fname).stem
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(
gray, (cbcol, cbrow), None
) # (8,6) pattern (dimensions = common points of black squares)
# If found, add object points, image points (after refining them)
if ret == True:
img_shape[cam] = gray.shape[::-1]
objpoints[cam].append(objp)
corners = cv2.cornerSubPix(
gray, corners, (11, 11), (-1, -1), criteria
)
imgpoints[cam].append(corners)
# Draw the corners and store the images
img = cv2.drawChessboardCorners(img, (cbcol, cbrow), corners, ret)
cv2.imwrite(
os.path.join(str(path_corners), filename + "_corner.jpg"), img
)
else:
print("Corners not found for the image %s" % Path(fname).name)
try:
h, w = img.shape[:2]
except:
raise Exception(
"It seems that the name of calibration images does not match with the camera names in the config file. Please make sure that the calibration images are named with camera names as specified in the config.yaml file."
)
# Perform calibration for each cameras and store the matrices as a pickle file
if calibrate == True:
# Calibrating each camera
for cam in cam_names:
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(
objpoints[cam], imgpoints[cam], img_shape[cam], None, None
)
# Save the camera calibration result for later use (we won't use rvecs / tvecs)
dist_pickle[cam] = {
"mtx": mtx,
"dist": dist,
"objpoints": objpoints[cam],
"imgpoints": imgpoints[cam],
}
pickle.dump(
dist_pickle,
open(
os.path.join(path_camera_matrix, cam + "_intrinsic_params.pickle"),
"wb",
),
)
print(
"Saving intrinsic camera calibration matrices for %s as a pickle file in %s"
% (cam, os.path.join(path_camera_matrix))
)
# Compute mean re-projection errors for individual cameras
mean_error = 0
for i in range(len(objpoints[cam])):
imgpoints_proj, _ = cv2.projectPoints(
objpoints[cam][i], rvecs[i], tvecs[i], mtx, dist
)
error = cv2.norm(imgpoints[cam][i], imgpoints_proj, cv2.NORM_L2) / len(
imgpoints_proj
)
mean_error += error
print(
"Mean re-projection error for %s images: %.3f pixels "
% (cam, mean_error / len(objpoints[cam]))
)
# Compute stereo calibration for each pair of cameras
camera_pair = [[cam_names[0], cam_names[1]]]
for pair in camera_pair:
print("Computing stereo calibration for " % pair)
retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F = cv2.stereoCalibrate(
objpoints[pair[0]],
imgpoints[pair[0]],
imgpoints[pair[1]],
dist_pickle[pair[0]]["mtx"],
dist_pickle[pair[0]]["dist"],
dist_pickle[pair[1]]["mtx"],
dist_pickle[pair[1]]["dist"],
(h, w),
flags=cv2.CALIB_FIX_INTRINSIC,
)
# Stereo Rectification
rectify_scale = (
alpha
) # Free scaling parameter check this https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html#fisheye-stereorectify
R1, R2, P1, P2, Q, roi1, roi2 = cv2.stereoRectify(
cameraMatrix1,
distCoeffs1,
cameraMatrix2,
distCoeffs2,
(h, w),
R,
T,
alpha=rectify_scale,
)
stereo_params[pair[0] + "-" + pair[1]] = {
"cameraMatrix1": cameraMatrix1,
"cameraMatrix2": cameraMatrix2,
"distCoeffs1": distCoeffs1,
"distCoeffs2": distCoeffs2,
"R": R,
"T": T,
"E": E,
"F": F,
"R1": R1,
"R2": R2,
"P1": P1,
"P2": P2,
"roi1": roi1,
"roi2": roi2,
"Q": Q,
"image_shape": [img_shape[pair[0]], img_shape[pair[1]]],
}
print(
"Saving the stereo parameters for every pair of cameras as a pickle file in %s"
% str(os.path.join(path_camera_matrix))
)
auxiliaryfunctions.write_pickle(
os.path.join(path_camera_matrix, "stereo_params.pickle"), stereo_params
)
print(
"Camera calibration done! Use the function ``check_undistortion`` to check the check the calibration"
)
else:
print(
"Corners extracted! You may check for the extracted corners in the directory %s and remove the pair of images where the corners are incorrectly detected. If all the corners are detected correctly with right order, then re-run the same function and use the flag ``calibrate=True``, to calbrate the camera."
% str(path_corners)
)
def undistort_points(config, dataframe, camera_pair, destfolder):
cfg_3d = auxiliaryfunctions.read_config(config)
img_path, path_corners, path_camera_matrix, path_undistort = auxiliaryfunctions_3d.Foldernames3Dproject(
cfg_3d)
'''
path_undistort = destfolder
filename_cam1 = Path(dataframe[0]).stem
filename_cam2 = Path(dataframe[1]).stem
#currently no interm. saving of this due to high speed.
# check if the undistorted files are already present
if os.path.exists(os.path.join(path_undistort,filename_cam1 + '_undistort.h5')) and os.path.exists(os.path.join(path_undistort,filename_cam2 + '_undistort.h5')):
print("The undistorted files are already present at %s" % os.path.join(path_undistort,filename_cam1))
dataFrame_cam1_undistort = pd.read_hdf(os.path.join(path_undistort,filename_cam1 + '_undistort.h5'))
dataFrame_cam2_undistort = pd.read_hdf(os.path.join(path_undistort,filename_cam2 + '_undistort.h5'))
else:
'''
if True:
# Create an empty dataFrame to store the undistorted 2d coordinates and likelihood
dataframe_cam1 = pd.read_hdf(dataframe[0])
dataframe_cam2 = pd.read_hdf(dataframe[1])
scorer_cam1 = dataframe_cam1.columns.get_level_values(0)[0]
scorer_cam2 = dataframe_cam2.columns.get_level_values(0)[0]
stereo_file = auxiliaryfunctions.read_pickle(
os.path.join(path_camera_matrix, 'stereo_params.pickle'))
path_stereo_file = os.path.join(path_camera_matrix,
'stereo_params.pickle')
stereo_file = auxiliaryfunctions.read_pickle(path_stereo_file)
mtx_l = stereo_file[camera_pair]['cameraMatrix1']
dist_l = stereo_file[camera_pair]['distCoeffs1']
mtx_r = stereo_file[camera_pair]['cameraMatrix2']
dist_r = stereo_file[camera_pair]['distCoeffs2']
R1 = stereo_file[camera_pair]['R1']
P1 = stereo_file[camera_pair]['P1']
R2 = stereo_file[camera_pair]['R2']
P2 = stereo_file[camera_pair]['P2']
# Create an empty dataFrame to store the undistorted 2d coordinates and likelihood
dataFrame_cam1_undistort, scorer_cam1, bodyparts = auxiliaryfunctions_3d.create_empty_df(
dataframe_cam1, scorer_cam1, flag='2d')
dataFrame_cam2_undistort, scorer_cam2, bodyparts = auxiliaryfunctions_3d.create_empty_df(
dataframe_cam2, scorer_cam2, flag='2d')
for bpindex, bp in tqdm(enumerate(bodyparts)):
# Undistorting the points from cam1 camera
points_cam1 = np.array([
dataframe_cam1[scorer_cam1][bp]['x'].values[:],
dataframe_cam1[scorer_cam1][bp]['y'].values[:]
])
points_cam1 = points_cam1.T
points_cam1 = np.expand_dims(points_cam1, axis=1)
points_cam1_remapped = cv2.undistortPoints(src=points_cam1,
cameraMatrix=mtx_l,
distCoeffs=dist_l,
P=P1,
R=R1)
dataFrame_cam1_undistort.iloc[:][scorer_cam1, bp,
'x'] = points_cam1_remapped[:, 0,
0]
dataFrame_cam1_undistort.iloc[:][scorer_cam1, bp,
'y'] = points_cam1_remapped[:, 0,
1]
dataFrame_cam1_undistort.iloc[:][
scorer_cam1, bp, 'likelihood'] = dataframe_cam1[scorer_cam1][
bp]['likelihood'].values[:]
# Undistorting the points from cam2 camera
points_cam2 = np.array([
dataframe_cam2[scorer_cam2][bp]['x'].values[:],
dataframe_cam2[scorer_cam2][bp]['y'].values[:]
])
points_cam2 = points_cam2.T
points_cam2 = np.expand_dims(points_cam2, axis=1)
points_cam2_remapped = cv2.undistortPoints(src=points_cam2,
cameraMatrix=mtx_r,
distCoeffs=dist_r,
P=P2,
R=R2)
dataFrame_cam2_undistort.iloc[:][scorer_cam2, bp,
'x'] = points_cam2_remapped[:, 0,
0]
dataFrame_cam2_undistort.iloc[:][scorer_cam2, bp,
'y'] = points_cam2_remapped[:, 0,
1]
dataFrame_cam2_undistort.iloc[:][
scorer_cam2, bp, 'likelihood'] = dataframe_cam2[scorer_cam2][
bp]['likelihood'].values[:]
# Save the undistorted files
dataFrame_cam1_undistort.sort_index(inplace=True)
dataFrame_cam2_undistort.sort_index(inplace=True)
return (dataFrame_cam1_undistort, dataFrame_cam2_undistort,
stereo_file[camera_pair], path_stereo_file)
def check_undistortion(config, cbrow=8, cbcol=6, plot=True):
"""
This function undistorts the calibration images based on the camera matrices and stores them in the project folder(defined in the config file)
to visually check if the camera matrices are correct.
Parameters
----------
config : string
Full path of the config.yaml file as a string.
cbrow : int
Int specifying the number of rows in the calibration image.
cbcol : int
Int specifying the number of columns in the calibration image.
plot : bool
If this is set to True, the results of undistortion are saved as plots. The default is ``True``; if provided it must be either ``True`` or ``False``.
Example
--------
Linux/MacOs/Windows
>>> deeplabcutcore.check_undistortion(config, cbrow = 8,cbcol = 6)
"""
# Read the config file
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
cfg_3d = auxiliaryfunctions.read_config(config)
img_path, path_corners, path_camera_matrix, path_undistort = auxiliaryfunctions_3d.Foldernames3Dproject(
cfg_3d
)
# colormap = plt.get_cmap(cfg_3d['colormap'])
markerSize = cfg_3d["dotsize"]
alphaValue = cfg_3d["alphaValue"]
markerType = cfg_3d["markerType"]
markerColor = cfg_3d["markerColor"]
cam_names = cfg_3d["camera_names"]
images = glob.glob(os.path.join(img_path, "*.jpg"))
# Sort the images
images.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
"""
for fname in images:
for cam in cam_names:
if cam in fname:
filename = Path(fname).stem
ext = Path(fname).suffix
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
"""
camera_pair = [[cam_names[0], cam_names[1]]]
stereo_params = auxiliaryfunctions.read_pickle(
os.path.join(path_camera_matrix, "stereo_params.pickle")
)
for pair in camera_pair:
map1_x, map1_y = cv2.initUndistortRectifyMap(
stereo_params[pair[0] + "-" + pair[1]]["cameraMatrix1"],
stereo_params[pair[0] + "-" + pair[1]]["distCoeffs1"],
stereo_params[pair[0] + "-" + pair[1]]["R1"],
stereo_params[pair[0] + "-" + pair[1]]["P1"],
(stereo_params[pair[0] + "-" + pair[1]]["image_shape"][0]),
cv2.CV_16SC2,
)
map2_x, map2_y = cv2.initUndistortRectifyMap(
stereo_params[pair[0] + "-" + pair[1]]["cameraMatrix2"],
stereo_params[pair[0] + "-" + pair[1]]["distCoeffs2"],
stereo_params[pair[0] + "-" + pair[1]]["R2"],
stereo_params[pair[0] + "-" + pair[1]]["P2"],
(stereo_params[pair[0] + "-" + pair[1]]["image_shape"][1]),
cv2.CV_16SC2,
)
cam1_undistort = []
cam2_undistort = []
for fname in images:
if pair[0] in fname:
filename = Path(fname).stem
img1 = cv2.imread(fname)
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
h, w = img1.shape[:2]
_, corners1 = cv2.findChessboardCorners(gray1, (cbcol, cbrow), None)
corners_origin1 = cv2.cornerSubPix(
gray1, corners1, (11, 11), (-1, -1), criteria
)
# Remapping dataFrame_camera1_undistort
im_remapped1 = cv2.remap(img1, map1_x, map1_y, cv2.INTER_LANCZOS4)
imgpoints_proj_undistort = cv2.undistortPoints(
src=corners_origin1,
cameraMatrix=stereo_params[pair[0] + "-" + pair[1]][
"cameraMatrix1"
],
distCoeffs=stereo_params[pair[0] + "-" + pair[1]]["distCoeffs1"],
P=stereo_params[pair[0] + "-" + pair[1]]["P1"],
R=stereo_params[pair[0] + "-" + pair[1]]["R1"],
)
cam1_undistort.append(imgpoints_proj_undistort)
cv2.imwrite(
os.path.join(str(path_undistort), filename + "_undistort.jpg"),
im_remapped1,
)
imgpoints_proj_undistort = []
elif pair[1] in fname:
filename = Path(fname).stem
img2 = cv2.imread(fname)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
h, w = img2.shape[:2]
_, corners2 = cv2.findChessboardCorners(gray2, (cbcol, cbrow), None)
corners_origin2 = cv2.cornerSubPix(
gray2, corners2, (11, 11), (-1, -1), criteria
)
# Remapping
im_remapped2 = cv2.remap(img2, map2_x, map2_y, cv2.INTER_LANCZOS4)
imgpoints_proj_undistort2 = cv2.undistortPoints(
src=corners_origin2,
cameraMatrix=stereo_params[pair[0] + "-" + pair[1]][
"cameraMatrix2"
],
distCoeffs=stereo_params[pair[0] + "-" + pair[1]]["distCoeffs2"],
P=stereo_params[pair[0] + "-" + pair[1]]["P2"],
R=stereo_params[pair[0] + "-" + pair[1]]["R2"],
)
cam2_undistort.append(imgpoints_proj_undistort2)
cv2.imwrite(
os.path.join(str(path_undistort), filename + "_undistort.jpg"),
im_remapped2,
)
imgpoints_proj_undistort2 = []
cam1_undistort = np.array(cam1_undistort)
cam2_undistort = np.array(cam2_undistort)
print("All images are undistorted and stored in %s" % str(path_undistort))
print(
"Use the function ``triangulate`` to undistort the dataframes and compute the triangulation"
)
if plot == True:
f1, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
f1.suptitle(
str("Original Image: Views from " + pair[0] + " and " + pair[1]),
fontsize=25,
)
# Display images in RGB
ax1.imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))
ax2.imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
norm = mcolors.Normalize(vmin=0.0, vmax=cam1_undistort.shape[1])
plt.savefig(os.path.join(str(path_undistort), "Original_Image.png"))
# Plot the undistorted corner points
f2, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
f2.suptitle(
"Undistorted corner points on camera-1 and camera-2", fontsize=25
)
ax1.imshow(cv2.cvtColor(im_remapped1, cv2.COLOR_BGR2RGB))
ax2.imshow(cv2.cvtColor(im_remapped2, cv2.COLOR_BGR2RGB))
for i in range(0, cam1_undistort.shape[1]):
ax1.scatter(
[cam1_undistort[-1][i, 0, 0]],
[cam1_undistort[-1][i, 0, 1]],
marker=markerType,
s=markerSize,
color=markerColor,
alpha=alphaValue,
)
ax2.scatter(
[cam2_undistort[-1][i, 0, 0]],
[cam2_undistort[-1][i, 0, 1]],
marker=markerType,
s=markerSize,
color=markerColor,
alpha=alphaValue,
)
plt.savefig(os.path.join(str(path_undistort), "undistorted_points.png"))
# Triangulate
triangulate = auxiliaryfunctions_3d.compute_triangulation_calibration_images(
stereo_params[pair[0] + "-" + pair[1]],
cam1_undistort,
cam2_undistort,
path_undistort,
cfg_3d,
plot=True,
)
auxiliaryfunctions.write_pickle("triangulate.pickle", triangulate)
def triangulate(config,
video_path,
videotype='avi',
filterpredictions=True,
filtertype='median',
gputouse=None,
destfolder=None,
save_as_csv=False):
"""
This function triangulates the detected DLC-keypoints from the two camera views
using the camera matrices (derived from calibration) to calculate 3D predictions.
Parameters
----------
config : string
Full path of the config.yaml file as a string.
video_path : string/list of list
Full path of the directory where videos are saved. If the user wants to analyze
only a pair of videos, the user needs to pass them as a list of list of videos,
i.e. [['video1-camera-1.avi','video1-camera-2.avi']]
videotype: string, optional
Checks for the extension of the video in case the input to the video is a directory.\n
Only videos with this extension are analyzed. The default is ``.avi``
filterpredictions: Bool, optional
Filter the predictions with filter specified by "filtertype". If specified it
should be either ``True`` or ``False``.
filtertype: string
Select which filter, 'arima' or 'median' filter (currently supported).
gputouse: int, optional. Natural number indicating the number of your GPU (see number in nvidia-smi).
If you do not have a GPU put None.
See: https://nvidia.custhelp.com/app/answers/detail/a_id/3751/~/useful-nvidia-smi-queries
destfolder: string, optional
Specifies the destination folder for analysis data (default is the path of the video)
save_as_csv: bool, optional
Saves the predictions in a .csv file. The default is ``False``
Example
-------
Linux/MacOS
To analyze all the videos in the directory:
>>> deeplabcutcore.triangulate(config,'/data/project1/videos/')
To analyze only a few pairs of videos:
>>> deeplabcutcore.triangulate(config,[['/data/project1/videos/video1-camera-1.avi','/data/project1/videos/video1-camera-2.avi'],['/data/project1/videos/video2-camera-1.avi','/data/project1/videos/video2-camera-2.avi']])
Windows
To analyze all the videos in the directory:
>>> deeplabcutcore.triangulate(config,'C:\\yourusername\\rig-95\\Videos')
To analyze only a few pair of videos:
>>> deeplabcutcore.triangulate(config,[['C:\\yourusername\\rig-95\\Videos\\video1-camera-1.avi','C:\\yourusername\\rig-95\\Videos\\video1-camera-2.avi'],['C:\\yourusername\\rig-95\\Videos\\video2-camera-1.avi','C:\\yourusername\\rig-95\\Videos\\video2-camera-2.avi']])
"""
from deeplabcutcore.pose_estimation_tensorflow import predict_videos
from deeplabcutcore.post_processing import filtering
cfg_3d = auxiliaryfunctions.read_config(config)
cam_names = cfg_3d['camera_names']
pcutoff = cfg_3d['pcutoff']
scorer_3d = cfg_3d['scorername_3d']
snapshots = {}
for cam in cam_names:
snapshots[cam] = cfg_3d[str('config_file_' + cam)]
# Check if the config file exists
if not os.path.exists(snapshots[cam]):
raise Exception(
str("It seems the file specified in the variable config_file_"
+ str(cam)) +
" does not exist. Please edit the config file with correct file path and retry."
)
#flag to check if the video_path variable is a string or a list of list
flag = False # assumes that video path is a list
if isinstance(video_path, str) == True:
flag = True
video_list = auxiliaryfunctions_3d.get_camerawise_videos(
video_path, cam_names, videotype=videotype)
else:
video_list = video_path
if video_list == []:
print("No videos found in the specified video path.", video_path)
print(
"Please make sure that the video names are specified with correct camera names as entered in the config file or"
)
print(
"perhaps the videotype is distinct from the videos in the path, I was looking for:",
videotype)
print("List of pairs:", video_list)
scorer_name = {}
run_triangulate = False
for i in range(len(video_list)):
dataname = []
for j in range(len(video_list[i])): #looping over cameras
if cam_names[j] in video_list[i][j]:
print("Analyzing video %s using %s" %
(video_list[i][j], str('config_file_' + cam_names[j])))
config_2d = snapshots[cam_names[j]]
cfg = auxiliaryfunctions.read_config(config_2d)
shuffle = cfg_3d[str('shuffle_' + cam_names[j])]
trainingsetindex = cfg_3d[str('trainingsetindex_' +
cam_names[j])]
trainFraction = cfg['TrainingFraction'][trainingsetindex]
if flag == True:
video = os.path.join(video_path, video_list[i][j])
else:
video_path = str(Path(video_list[i][j]).parents[0])
video = os.path.join(video_path, video_list[i][j])
if destfolder is None:
destfolder = str(Path(video).parents[0])
vname = Path(video).stem
prefix = str(vname).split(cam_names[j])[0]
suffix = str(vname).split(cam_names[j])[-1]
if prefix == "":
pass
elif prefix[-1] == '_' or prefix[-1] == '-':
prefix = prefix[:-1]
if suffix == "":
pass
elif suffix[0] == '_' or suffix[0] == '-':
suffix = suffix[1:]
if prefix == '':
output_file = os.path.join(destfolder, suffix)
else:
if suffix == '':
output_file = os.path.join(destfolder, prefix)
else:
output_file = os.path.join(destfolder,
prefix + '_' + suffix)
output_filename = os.path.join(
output_file + '_' + scorer_3d
) # Check if the videos are already analyzed for 3d
if os.path.isfile(output_filename + '.h5'):
if save_as_csv is True and not os.path.exists(
output_filename + '.csv'):
# In case user adds save_as_csv is True after triangulating
pd.read_hdf(output_filename + '.h5').to_csv(
str(output_filename + '.csv'))
print(
"Already analyzed...Checking the meta data for any change in the camera matrices and/or scorer names",
vname)
pickle_file = str(output_filename +
'_includingmetadata.pickle')
metadata_ = auxiliaryfunctions_3d.LoadMetadata3d(
pickle_file)
img_path, path_corners, path_camera_matrix, path_undistort = auxiliaryfunctions_3d.Foldernames3Dproject(
cfg_3d)
path_stereo_file = os.path.join(path_camera_matrix,
'stereo_params.pickle')
stereo_file = auxiliaryfunctions.read_pickle(
path_stereo_file)
cam_pair = str(cam_names[0] + '-' + cam_names[1])
if_video_analyzed = False # variable to keep track if the video was already analyzed
# Check for the camera matrix
for k in metadata_['stereo_matrix'].keys():
if np.all(metadata_['stereo_matrix'][k] ==
stereo_file[cam_pair][k]):
pass
else:
run_triangulate = True
# Check for scorer names in the pickle file of 3d output
DLCscorer, DLCscorerlegacy = auxiliaryfunctions.GetScorerName(
cfg,
shuffle,
trainFraction,
trainingsiterations='unknown')
if metadata_['scorer_name'][
cam_names[j]] == DLCscorer: #TODO: CHECK FOR BOTH?
if_video_analyzed = True
elif metadata_['scorer_name'][
cam_names[j]] == DLCscorerlegacy:
if_video_analyzed = True
else:
if_video_analyzed = False
run_triangulate = True
if if_video_analyzed:
print("This file is already analyzed!")
dataname.append(
os.path.join(destfolder,
vname + DLCscorer + '.h5'))
scorer_name[cam_names[j]] = DLCscorer
else:
# Analyze video if score name is different
DLCscorer = predict_videos.analyze_videos(
config_2d, [video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
gputouse=gputouse,
destfolder=destfolder)
scorer_name[cam_names[j]] = DLCscorer
if_video_analyzed = False
run_triangulate = True
if filterpredictions:
filtering.filterpredictions(
config_2d, [video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
filtertype=filtertype,
destfolder=destfolder)
dataname.append(
os.path.join(destfolder,
vname + DLCscorer + '.h5'))
else: # need to do the whole jam.
DLCscorer = predict_videos.analyze_videos(
config_2d, [video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
gputouse=gputouse,
destfolder=destfolder)
scorer_name[cam_names[j]] = DLCscorer
run_triangulate = True
print(destfolder, vname, DLCscorer)
if filterpredictions:
filtering.filterpredictions(
config_2d, [video],
videotype=videotype,
shuffle=shuffle,
trainingsetindex=trainingsetindex,
filtertype=filtertype,
destfolder=destfolder)
dataname.append(
os.path.join(destfolder,
vname + DLCscorer + '.h5'))
if run_triangulate:
# if len(dataname)>0:
#undistort points for this pair
print("Undistorting...")
dataFrame_camera1_undistort, dataFrame_camera2_undistort, stereomatrix, path_stereo_file = undistort_points(
config, dataname, str(cam_names[0] + '-' + cam_names[1]),
destfolder)
if len(dataFrame_camera1_undistort) != len(
dataFrame_camera2_undistort):
import warnings
warnings.warn(
"The number of frames do not match in the two videos. Please make sure that your videos have same number of frames and then retry! Excluding the extra frames from the longer video."
)
if len(dataFrame_camera1_undistort) > len(
dataFrame_camera2_undistort):
dataFrame_camera1_undistort = dataFrame_camera1_undistort[:len(
dataFrame_camera2_undistort)]
if len(dataFrame_camera2_undistort) > len(
dataFrame_camera1_undistort):
dataFrame_camera2_undistort = dataFrame_camera2_undistort[:len(
dataFrame_camera1_undistort)]
# raise Exception("The number of frames do not match in the two videos. Please make sure that your videos have same number of frames and then retry!")
X_final = []
triangulate = []
scorer_cam1 = dataFrame_camera1_undistort.columns.get_level_values(
0)[0]
scorer_cam2 = dataFrame_camera2_undistort.columns.get_level_values(
0)[0]
df_3d, scorer_3d, bodyparts = auxiliaryfunctions_3d.create_empty_df(
dataFrame_camera1_undistort, scorer_3d, flag='3d')
P1 = stereomatrix['P1']
P2 = stereomatrix['P2']
print("Computing the triangulation...")
for bpindex, bp in enumerate(bodyparts):
# Extract the indices of frames where the likelihood of a bodypart for both cameras are less than pvalue
likelihoods = np.array([
dataFrame_camera1_undistort[scorer_cam1][bp]
['likelihood'].values[:],
dataFrame_camera2_undistort[scorer_cam2][bp]
['likelihood'].values[:]
])
likelihoods = likelihoods.T
#Extract frames where likelihood for both the views is less than the pcutoff
low_likelihood_frames = np.any(likelihoods < pcutoff, axis=1)
#low_likelihood_frames = np.all(likelihoods < pcutoff, axis=1)
low_likelihood_frames = np.where(
low_likelihood_frames == True)[0]
points_cam1_undistort = np.array([
dataFrame_camera1_undistort[scorer_cam1][bp]
['x'].values[:],
dataFrame_camera1_undistort[scorer_cam1][bp]['y'].values[:]
])
points_cam1_undistort = points_cam1_undistort.T
# For cam1 camera: Assign nans to x and y values of a bodypart where the likelihood for is less than pvalue
points_cam1_undistort[low_likelihood_frames] = np.nan, np.nan
points_cam1_undistort = np.expand_dims(points_cam1_undistort,
axis=1)
points_cam2_undistort = np.array([
dataFrame_camera2_undistort[scorer_cam2][bp]
['x'].values[:],
dataFrame_camera2_undistort[scorer_cam2][bp]['y'].values[:]
])
points_cam2_undistort = points_cam2_undistort.T
# For cam2 camera: Assign nans to x and y values of a bodypart where the likelihood is less than pvalue
points_cam2_undistort[low_likelihood_frames] = np.nan, np.nan
points_cam2_undistort = np.expand_dims(points_cam2_undistort,
axis=1)
X_l = auxiliaryfunctions_3d.triangulatePoints(
P1, P2, points_cam1_undistort, points_cam2_undistort)
#ToDo: speed up func. below by saving in numpy.array
X_final.append(X_l)
triangulate.append(X_final)
triangulate = np.asanyarray(triangulate)
metadata = {}
metadata['stereo_matrix'] = stereomatrix
metadata['stereo_matrix_file'] = path_stereo_file
metadata['scorer_name'] = {
cam_names[0]: scorer_name[cam_names[0]],
cam_names[1]: scorer_name[cam_names[1]]
}
# Create an empty dataframe to store x,y,z of 3d data
for bpindex, bp in enumerate(bodyparts):
df_3d.iloc[:][scorer_3d, bp, 'x'] = triangulate[0, bpindex,
0, :]
df_3d.iloc[:][scorer_3d, bp, 'y'] = triangulate[0, bpindex,
1, :]
df_3d.iloc[:][scorer_3d, bp, 'z'] = triangulate[0, bpindex,
2, :]
df_3d.to_hdf(str(output_filename + '.h5'),
'df_with_missing',
format='table',
mode='w')
auxiliaryfunctions_3d.SaveMetadata3d(
str(output_filename + '_includingmetadata.pickle'), metadata)
if save_as_csv:
df_3d.to_csv(str(output_filename + '.csv'))
print("Triangulated data for video", video_list[i])
print("Results are saved under: ", destfolder)
# have to make the dest folder none so that it can be updated for a new pair of videos
if destfolder == str(Path(video).parents[0]):
destfolder = None
if len(video_list) > 0:
print("All videos were analyzed...")
print(
"Now you can create 3D video(s) using deeplabcutcore.create_labeled_video_3d"
)