def convert2_tracklets(self, event):
shuffle = self.shuffle.GetValue()
trainingsetindex = self.trainingset.GetValue()
if self.overwrite.GetStringSelection() == "Yes":
overwrite = True
else:
overwrite = False
deeplabcut.convert_detections2tracklets(
self.config,
self.filelist,
videotype=self.videotype.GetValue(),
shuffle=shuffle,
trainingsetindex=trainingsetindex,
edgewisecondition=True,
overwrite=overwrite,
track_method=self.trackertypes.GetValue(),
)
def convert2_tracklets(self, event):
shuffle = self.shuffle.GetValue()
trainingsetindex = self.trainingset.GetValue()
if self.overwrite.GetStringSelection() == "Yes":
overwrite = True
else:
overwrite = False
deeplabcut.convert_detections2tracklets(
self.config,
self.filelist,
videotype=self.videotype.GetValue(),
shuffle=shuffle,
trainingsetindex=trainingsetindex,
overwrite=overwrite,
track_method=self.trackertypes.GetValue(),
calibrate=self.calibrate.GetStringSelection() == "Yes",
window_size=self.winsize.GetValue(),
identity_only=self.identity_toggle.GetStringSelection() == "Yes",
)
print("Video analyzed.")
print("Create video with all detections...")
scorer, _ = auxiliaryfunctions.GetScorerName(cfg, 1, TRAIN_SIZE)
deeplabcut.create_video_with_all_detections(
config_path, [new_video_path], shuffle=1, displayedbodyparts=["bodypart1"]
)
print("Video created.")
print("Convert detections to tracklets...")
deeplabcut.convert_detections2tracklets(
config_path,
[new_video_path],
"mp4",
track_method=TESTTRACKER
)
print("Tracklets created...")
deeplabcut.stitch_tracklets(
config_path,
[new_video_path],
"mp4",
output_name=os.path.splitext(new_video_path)[0] + scorer + "_el.h5",
track_method=TESTTRACKER
)
print("Plotting trajectories...")
deeplabcut.plot_trajectories(
config_path,
def Get_Percent_Good(coords):
bbs, it, mh = coords[0], coords[1], coords[2]
current_video_path = test_video_folder_path.format(bbs=bbs, it=it, mh=mh)
#Writes a new file here for the future labeling and things to use
i_cfg_file = open(inference_cfg_path, "w")
i_cfg_file.write(inference_cfg_text.format(bbs=bbs, it=it, mh=mh))
i_cfg_file.close()
#Creates the folder and videos
if not os.path.isdir(current_video_path):
os.mkdir(current_video_path)
for v in og_video_files:
shutil.copy2(main_video_folder_path + v, current_video_path + v)
for p in og_pickle_files:
shutil.copy2(main_video_folder_path + p, current_video_path + p)
#analyze video
#No longer needed since they are the same each time
#deeplabcut.analyze_videos(config_path,[current_video_path], videotype='.mp4', save_as_csv = True)
#convert to tracklets
deeplabcut.convert_detections2tracklets(config_path, [current_video_path],
videotype='mp4',
shuffle=1,
trainingsetindex=0,
track_method='box')
video_files = os.listdir(current_video_path)
#Convert to h5s
for file in video_files:
if file.endswith("_bx.pickle"):
h5_name = file.replace("pickle", "h5")
video_name = file.replace
if h5_name in video_files:
print("h5 for {} already exists!".format(file))
else:
print("Converting {} to h5!".format(file))
deeplabcut.convert_raw_tracks_to_h5(config_path,
current_video_path + file)
#Need to do this each time to update with new videos
video_files = os.listdir(current_video_path)
#fix the messy bits in the files so all points exist
for file in video_files:
if file.endswith("_bx.h5"):
print(file)
if file.startswith("2020_6"):
x_len = 1920
y_len = 800
else:
x_len = 2368
y_len = 1152
rep_values = [x_len - x_len / 10, y_len / 2, 0]
hf = h5py.File(current_video_path + file, 'r+')
table = hf['df_with_missing/table']
n_table = np.array(table)
new_table = n_table
for i in range(n_table.shape[0]):
dlc_data = n_table[i][1]
dlc_data = np.nan_to_num(dlc_data)
for j in range(len(dlc_data)):
# j%3 gives 0 = x, 1 = y, 2 = prob
pos = j % 3
#if there is no data then replace it with default
if dlc_data[j] == 0:
new_table[i][1][j] = rep_values[pos]
if pos != 2:
print(
"No Points. Set to {} in position {}.".format(
rep_values[pos], pos))
#if x is out of range replace with default
if pos == 0 and (dlc_data[j] > x_len or dlc_data[j] < 0):
new_table[i][1][j] = rep_values[pos]
print(
"Out of X Bounds: Replaced {} with {} in position {}."
.format(dlc_data[j], rep_values[pos], pos))
#if y is out of range replace with default
if pos == 1 and (dlc_data[j] > y_len or dlc_data[j] < 0):
new_table[i][1][j] = rep_values[pos]
print(
"Out of Y Bounds: Replaced {} with {} in position {}."
.format(dlc_data[j], rep_values[pos], pos))
#This is all to allow me to just correct the first n frames and only extract those
table[...] = new_table
hf.close()
video_files = os.listdir(current_video_path)
#Now create the filtered videos and CSVs
for file in video_files:
if file.endswith(".mp4"):
print(file)
deeplabcut.filterpredictions(config_path,
[current_video_path + file],
videotype='mp4',
shuffle=1,
track_method='box')
deeplabcut.create_labeled_video(config_path,
[current_video_path + file],
videotype='.mp4',
shuffle=1,
draw_skeleton=True,
track_method='box',
save_frames=False,
filtered=True)
video_files = os.listdir(current_video_path)
#Now finally get the percent of "good" points
files = []
good_percents = []
for file_name in video_files:
if file_name.endswith(".csv"):
in_csv = open(current_video_path + file_name, "r")
#Skip the first 4
skip_lines = 0
good_points = 0
total_points = 0
for line in in_csv:
if skip_lines >= 4:
lis = line.split(",")[:-24]
#Off by 1 for the first row of frame counts
probs = np.asarray(lis[3::3]).astype(np.float)
#If there are no zeros in probs
good_points += np.sum(probs > prob_cutoff)
total_points += len(probs)
else:
skip_lines += 1
lis = line.split(",")[:-24]
files.append(file_name)
good_percents.append(round(good_points / total_points * 100, 2))
in_csv.close()
#out_csv.close()
files = np.asarray(files)
good_percents = np.asarray(good_percents)
sort_percent = np.flip(good_percents[np.argsort(good_percents)])
sorted_files = np.flip(files[np.argsort(good_percents)])
out_file = open(
current_video_path +
"PGV_{bbs}_{it}_{mh}.txt".format(bbs=bbs, it=it, mh=mh), "w")
for i in range(len(sorted_files)):
out_file.write("{} : {}% good points\n".format(sorted_files[i][:22],
sort_percent[i]))
print("{} : {}% good points".format(sorted_files[i][:22],
sort_percent[i]))
out_file.close()
score = np.sum((100 - good_percents)**2)
main_csv = open(
main_video_folder_path + "PGV_all.csv".format(bbs=bbs, it=it, mh=mh),
"a")
main_csv.write(
"{bbs}_{it}_{mh},{bbs},{it},{mh},{v0},{v1},{v2},{v3},{v4},{m},{score}\n"
.format(bbs=bbs,
it=it,
mh=mh,
v0=good_percents[0],
v1=good_percents[1],
v2=good_percents[2],
v3=good_percents[3],
v4=good_percents[4],
m=np.mean(good_percents),
score=score))
main_csv.close()
shutil.rmtree(current_video_path)
return (score)
#Empty string to do all of them, fill it with the ID to just run it with one
single_vid = ""
#Writes a new file here for the future labeling and things to use
i_cfg_file = open(inference_cfg_path, "w")
i_cfg_file.write(inference_cfg_text.format(bbs=bbs, it=it, mh=mh))
i_cfg_file.close()
#analyze video
#No longer needed since they are the same each time
#deeplabcut.analyze_videos(config_path,[current_video_path], videotype='.mp4', save_as_csv = True)
#convert to tracklets
deeplabcut.convert_detections2tracklets(config_path, [main_video_folder_path],
videotype='mp4',
shuffle=1,
trainingsetindex=0,
track_method='box')
video_files = os.listdir(main_video_folder_path)
#Convert to h5s
for file in video_files:
if file.endswith("_bx.pickle") and single_vid in file:
h5_name = file.replace("pickle", "h5")
video_name = file.replace
if h5_name in video_files:
print("h5 for {} already exists!".format(file))
else:
print("Converting {} to h5!".format(file))
"mp4",
robust_nframes=True)
print("Video analyzed.")
print("Create video with all detections...")
scorer, _ = auxiliaryfunctions.GetScorerName(cfg, 1, TRAIN_SIZE)
deeplabcut.create_video_with_all_detections(config_path, [new_video_path],
scorer)
print("Video created.")
edgewisecondition = True
print("Convert detections...")
deeplabcut.convert_detections2tracklets(
config_path,
[new_video_path],
"mp4",
track_method="box",
edgewisecondition=edgewisecondition,
)
deeplabcut.convert_detections2tracklets(config_path, [new_video_path],
"mp4",
track_method="skeleton")
print("Extracting maps...")
deeplabcut.extract_save_all_maps(config_path, Indices=[0, 1, 2])
print("Analyzing video...")
deeplabcut.analyze_videos(config_path, [new_video_path],
"mp4",
save_as_csv=True)
print("Video analyzed.")
print("Analyzing video...")
deeplabcut.analyze_videos(config_path, [new_video_path], "mp4", robust_nframes=True,allow_growth=True)
print("Video analyzed.")
print("Create video with all detections...")
scorer, _ = auxiliaryfunctions.GetScorerName(cfg, 1, TRAIN_SIZE)
deeplabcut.create_video_with_all_detections(
config_path, [new_video_path], shuffle=1, displayedbodyparts=["bodypart1"]
)
print("Video created.")
print("Convert detections to tracklets...")
deeplabcut.convert_detections2tracklets(
config_path, [new_video_path], "mp4", track_method="box"
)
deeplabcut.convert_detections2tracklets(
config_path, [new_video_path], "mp4", track_method="ellipse"
)
print("Tracklets created...")
deeplabcut.stitch_tracklets(
config_path,
[new_video_path],
"mp4",
output_name=os.path.splitext(new_video_path)[0] + scorer + "_el.h5",
)
print("Plotting trajectories...")
deeplabcut.plot_trajectories(
import numpy as np
config_path = '/PATH_TO_CONFIG/config.yaml'
videos = glob.glob('/PATH_TO_VIDEOS/*.mp4')
print('analyzing ' + str(len(videos)) + ' videos:')
print(*videos, sep="\n")
deeplabcut.analyze_videos(config_path,
videos,
shuffle=1,
batchsize=8,
dynamic=(True, 0.5, 100))
deeplabcut.create_video_with_all_detections(
config_path, videos, 'DLC_resnet50_aedesNov16shuffle1_80000')
deeplabcut.convert_detections2tracklets(config_path,
videos,
shuffle=1,
videotype='mp4',
trainingsetindex=0,
track_method='box')
trackletPickles = glob.glob('/PATH_TO_VIDEOS/*_bx.pickle')
print('Converting ' + str(len(trackletPickles)) + ' tracklets:')
print(*trackletPickles, sep="\n")
for tracklet in trackletPickles:
deeplabcut.convert_raw_tracks_to_h5(config_path, tracklet)
