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Merge pull request #233 from yujiahu415/master
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v2.7.1
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@yujiahu415
yujiahu415 authored Nov 22, 2024
2 parents fd88c7b + 4b1d3cc commit d0594e6
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Showing 4 changed files with 158 additions and 13 deletions.
2 changes: 1 addition & 1 deletion LabGym/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@



__version__='2.7.0'
__version__='2.7.1'



58 changes: 52 additions & 6 deletions LabGym/analyzebehavior.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,6 +83,7 @@ def __init__(self):
self.pattern_images={}
self.event_probability={}
self.all_behavior_parameters={}
self.log=[]


def prepare_analysis(self,
Expand Down Expand Up @@ -111,7 +112,9 @@ def prepare_analysis(self,
):

print('Preparation started...')
self.log.append('Preparation started...')
print(datetime.datetime.now())
self.log.append(str(datetime.datetime.now()))

self.path_to_video=path_to_video
self.basename=os.path.basename(self.path_to_video)
Expand Down Expand Up @@ -145,12 +148,15 @@ def prepare_analysis(self,
capture.release()

print('Video fps: '+str(self.fps))
self.log.append('Video fps: '+str(self.fps))
print('The original video framesize: '+str(int(frame.shape[0]))+' X '+str(int(frame.shape[1])))
self.log.append('The original video framesize: '+str(int(frame.shape[0]))+' X '+str(int(frame.shape[1])))

if self.framewidth is not None:
self.frameheight=int(frame.shape[0]*self.framewidth/frame.shape[1])
self.background=cv2.resize(frame,(self.framewidth,self.frameheight),interpolation=cv2.INTER_AREA)
print('The resized video framesize: '+str(self.frameheight)+' X '+str(self.framewidth))
self.log.append('The resized video framesize: '+str(self.frameheight)+' X '+str(self.framewidth))
else:
self.background=frame
framesize=min(self.background.shape[0],self.background.shape[1])
Expand All @@ -175,6 +181,7 @@ def prepare_analysis(self,
es_start=self.t
constants=estimate_constants(self.path_to_video,self.delta,self.animal_number,framewidth=self.framewidth,frameheight=self.frameheight,stable_illumination=stable_illumination,ex_start=ex_start,ex_end=ex_end,t=es_start,duration=self.duration,animal_vs_bg=self.animal_vs_bg,path_background=path_background,kernel=self.kernel)
self.animal_area=constants[4]
self.log.append('The area of single animal is: '+str(self.animal_area)+'.')
self.background=constants[0]
self.background_low=constants[1]
self.background_high=constants[2]
Expand Down Expand Up @@ -211,6 +218,7 @@ def prepare_analysis(self,
self.pattern_images[i]=[np.zeros((self.dim_conv,self.dim_conv,3),dtype='uint8')]*self.total_analysis_framecount

print('Preparation completed!')
self.log.append('Preparation completed!')


def track_animal(self,frame_count_analyze,contours,centers,heights,inners=None,blobs=None):
Expand Down Expand Up @@ -275,7 +283,9 @@ def acquire_information(self,background_free=True,black_background=True):
# black_background: whether to set background black

print('Acquiring information in each frame...')
self.log.append('Acquiring information in each frame...')
print(datetime.datetime.now())
self.log.append(str(datetime.datetime.now()))

capture=cv2.VideoCapture(self.path_to_video)

Expand Down Expand Up @@ -314,7 +324,9 @@ def acquire_information(self,background_free=True,black_background=True):

if (frame_count_analyze+1)%1000==0:
print(str(frame_count_analyze+1)+' frames processed...')
self.log.append(str(frame_count_analyze+1)+' frames processed...')
print(datetime.datetime.now())
self.log.append(str(datetime.datetime.now()))

if self.framewidth is not None:
frame=cv2.resize(frame,(self.framewidth,self.frameheight),interpolation=cv2.INTER_AREA)
Expand Down Expand Up @@ -357,6 +369,7 @@ def acquire_information(self,background_free=True,black_background=True):
capture.release()

print('Information acquisition completed!')
self.log.append('Information acquisition completed!')


def acquire_information_interact_basic(self,background_free=True,black_background=True):
Expand All @@ -365,7 +378,9 @@ def acquire_information_interact_basic(self,background_free=True,black_backgroun
# black_background: whether to set background black

print('Acquiring information in each frame...')
self.log.append('Acquiring information in each frame...')
print(datetime.datetime.now())
self.log.append(str(datetime.datetime.now()))

self.register_counts={}
self.register_counts[0]=None
Expand Down Expand Up @@ -416,7 +431,9 @@ def acquire_information_interact_basic(self,background_free=True,black_backgroun

if (frame_count_analyze+1)%1000==0:
print(str(frame_count_analyze+1)+' frames processed...')
self.log.append(str(frame_count_analyze+1)+' frames processed...')
print(datetime.datetime.now())
self.log.append(str(datetime.datetime.now()))

if self.framewidth is not None:
frame=cv2.resize(frame,(self.framewidth,self.frameheight),interpolation=cv2.INTER_AREA)
Expand Down Expand Up @@ -473,12 +490,15 @@ def acquire_information_interact_basic(self,background_free=True,black_backgroun
self.animal_centers[0]=self.animal_centers[0][:len(self.all_time)]

print('Information acquisition completed!')
self.log.append('Information acquisition completed!')


def craft_data(self):

print('Crafting data...')
self.log.append('Crafting data...')
print(datetime.datetime.now())
self.log.append(str(datetime.datetime.now()))

lengths=[]
length=len(self.all_time)
Expand Down Expand Up @@ -521,6 +541,7 @@ def craft_data(self):
self.pattern_images[i]=self.pattern_images[i][:length]

print('Data crafting completed!')
self.log.append('Data crafting completed!')


def categorize_behaviors(self,path_to_categorizer,uncertain=0,min_length=None):
Expand All @@ -530,7 +551,9 @@ def categorize_behaviors(self,path_to_categorizer,uncertain=0,min_length=None):
# min_length: the minimum length (in frames) a behavior should last, can be used to filter out the brief false positives

print('Categorizing behaviors...')
self.log.append('Categorizing behaviors...')
print(datetime.datetime.now())
self.log.append(str(datetime.datetime.now()))

IDs=list(self.pattern_images.keys())

Expand Down Expand Up @@ -618,6 +641,7 @@ def categorize_behaviors(self,path_to_categorizer,uncertain=0,min_length=None):
i+=1

print('Behavioral categorization completed!')
self.log.append('Behavioral categorization completed!')


def annotate_video(self,behavior_to_include,show_legend=True,interact_all=False):
Expand All @@ -627,7 +651,9 @@ def annotate_video(self,behavior_to_include,show_legend=True,interact_all=False)
# interact_all: whether is the interactive basic mode

print('Annotating video...')
self.log.append('Annotating video...')
print(datetime.datetime.now())
self.log.append(str(datetime.datetime.now()))

text_scl=max(0.5,round((self.background.shape[0]+self.background.shape[1])/1080,1))
text_tk=max(1,round((self.background.shape[0]+self.background.shape[1])/540))
Expand Down Expand Up @@ -764,6 +790,7 @@ def annotate_video(self,behavior_to_include,show_legend=True,interact_all=False)
cv2.imwrite(os.path.join(self.results_path,'Trajectory.jpg'),self.background)

print('Video annotation completed!')
self.log.append('Video annotation completed!')


def analyze_parameters(self,normalize_distance=True,parameter_to_analyze=[]):
Expand Down Expand Up @@ -863,7 +890,7 @@ def analyze_parameters(self,normalize_distance=True,parameter_to_analyze=[]):
if normalize_distance:
calibrator=math.sqrt(self.animal_area)
distance_traveled=distance_traveled/calibrator
speed=distance_traveled/(self.length/self.fps)
self.all_behavior_parameters[behavior_name]['speed'][i][n]=distance_traveled/(self.length/self.fps)
end_center=self.animal_centers[i][n]
if end_center is not None:
displacements=[]
Expand All @@ -877,6 +904,12 @@ def analyze_parameters(self,normalize_distance=True,parameter_to_analyze=[]):
displacement=displacement/calibrator
velocity=displacement/((self.length-np.argmax(displacements))/self.fps)
self.all_behavior_parameters[behavior_name]['velocity'][i][n]=velocity
start_center=self.animal_centers[i][n-1]
if start_center is not None:
dt=math.dist(end_center,start_center)
if normalize_distance:
dt=dt/calibrator
self.all_behavior_parameters[behavior_name]['distance'][i]+=dt
velocities_max=[]
velocities_min=[]
for v in self.all_behavior_parameters[behavior_name]['velocity'][i][n-self.length+1:n+1]:
Expand All @@ -892,8 +925,6 @@ def analyze_parameters(self,normalize_distance=True,parameter_to_analyze=[]):
if np.argmax(velocities_max)!=np.argmin(velocities_min):
t=abs(np.argmax(velocities_max)-np.argmin(velocities_min))/self.fps
self.all_behavior_parameters[behavior_name]['acceleration'][i][n]=(vmax-vmin)/t
self.all_behavior_parameters[behavior_name]['distance'][i]+=distance_traveled
self.all_behavior_parameters[behavior_name]['speed'][i][n]=speed

if '3 areal parameters' in parameter_to_analyze:
mask=np.zeros_like(self.background)
Expand Down Expand Up @@ -969,7 +1000,7 @@ def analyze_parameters(self,normalize_distance=True,parameter_to_analyze=[]):
if normalize_distance:
calibrator=math.sqrt(self.animal_area)
distance_traveled=distance_traveled/calibrator
speed=distance_traveled/(self.length/self.fps)
self.all_behavior_parameters['speed'][i][n]=distance_traveled/(self.length/self.fps)
end_center=self.animal_centers[i][n]
if end_center is not None:
displacements=[]
Expand All @@ -983,6 +1014,13 @@ def analyze_parameters(self,normalize_distance=True,parameter_to_analyze=[]):
displacement=displacement/calibrator
velocity=displacement/((self.length-np.argmax(displacements))/self.fps)
self.all_behavior_parameters['velocity'][i][n]=velocity
start_center=self.animal_centers[i][n-1]
if start_center is not None:
dt=math.dist(end_center,start_center)
if normalize_distance:
dt=dt/calibrator
self.all_behavior_parameters['distance'][i]+=dt

velocities_max=[]
velocities_min=[]
for v in self.all_behavior_parameters['velocity'][i][n-self.length+1:n+1]:
Expand All @@ -998,8 +1036,6 @@ def analyze_parameters(self,normalize_distance=True,parameter_to_analyze=[]):
if np.argmax(velocities_max)!=np.argmin(velocities_min):
t=abs(np.argmax(velocities_max)-np.argmin(velocities_min))/self.fps
self.all_behavior_parameters['acceleration'][i][n]=(vmax-vmin)/t
self.all_behavior_parameters['distance'][i]+=distance_traveled
self.all_behavior_parameters['speed'][i][n]=speed

if '3 areal parameters' in parameter_to_analyze:
mask=np.zeros_like(self.background)
Expand Down Expand Up @@ -1063,14 +1099,19 @@ def export_results(self,normalize_distance=True,parameter_to_analyze=[]):
# parameter_to_analyze: the behavior parameters that are selected in the analysis

print('Quantifying behaviors...')
self.log.append('Quantifying behaviors...')
print(datetime.datetime.now())
self.log.append(str(datetime.datetime.now()))

self.analyze_parameters(normalize_distance=normalize_distance,parameter_to_analyze=parameter_to_analyze)

print('Behavioral quantification completed!')
self.log.append('Behavioral quantification Completed!')

print('Exporting results...')
self.log.append('Exporting results...')
print(datetime.datetime.now())
self.log.append(str(datetime.datetime.now()))

if self.categorize_behavior:
events_df=pd.DataFrame(self.event_probability,index=self.all_time)
Expand Down Expand Up @@ -1135,6 +1176,11 @@ def export_results(self,normalize_distance=True,parameter_to_analyze=[]):
pd.concat(summary,axis=1).to_excel(os.path.join(self.results_path,'all_summary.xlsx'),float_format='%.2f',index_label='ID/parameter')

print('All results exported in: '+str(self.results_path))
self.log.append('All results exported in: '+str(self.results_path))
self.log.append('Analysis completed!')
if len(self.log)>0:
with open(os.path.join(self.results_path,'Analysis log.txt'),'w') as analysis_log:
analysis_log.write('\n'.join(str(i) for i in self.log))


def generate_data(self,background_free=True,black_background=True,skip_redundant=1):
Expand Down
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