class GroupedVideoGenerator2:
def __init__(self, image_generators,fps=None,modification_pipelines=None,structured_modification_pipelines=None):
self.video_lists=[]
self.file_lists=[]
for i in range(0,len(image_generators)):
if(isinstance(image_generators[i], VideoGenerator)):
assert(isinstance(image_generators[i].videos, OrderedDict))
self.file_lists.append(image_generators[i].file_list);
self.video_lists.append(image_generators[i].videos)
fps=image_generators[i].fps
elif(isinstance(image_generators[i],list)):
self.file_lists.append(image_generators[i]);
tmp_mpio_obj_list=map(lambda vp:menpo.io.import_video(vp, exact_frame_count=False, normalise=False), image_generators[i])
self.video_lists.append(tmp_mpio_obj_list)
else:
raise TypeError('You can only make a GroupedImageGenerator2 from a list of string-lists or ImageGenerator2s')
self.file_lists[i] = filter(None,self.file_lists[i]);
self.fps=fps
self.modification_pipelines=modification_pipelines if not modification_pipelines is None else {}
self.structured_modification_pipelines=structured_modification_pipelines if not structured_modification_pipelines is None else {}
self.io_pool = None
self.enable_caching=0;
self.enable_pp_caching=0;
self.img_pp_cache={}
self.img_pp_oo_cache={}
self.img_cache={}
self.opts={}
def group_lists(self,get_key_callback):
new_file_lists=sbpy_utils.core.sets.group_by(self.file_lists,get_key_callback);
self.file_lists=new_file_lists;
reordered_video_lists=[]
old_video_lists=self.video_lists
for view_idx,file_list in enumerate(new_file_lists):
reordered_video_list=OrderedDict()
old_video_list=old_video_lists[view_idx]
for g in file_list:
reordered_video_list[g]=old_video_list[g]
def dynamic_func(self,key,*args):
fhandle = self.opts[key];
out = fhandle(*args);
return out
def get_sample_dims(self, pipeline_mask=[], invert_pipeline_mask=True):
modified_img,_=self.get(0,0,pipeline_mask, invert_pipeline_mask)
all_dims=[]
for view in modified_img:
dims = view[0].shape;
all_dims.append(dims)
return all_dims
def structured_get(self, idx, time_idxs, pipeline_mask=[], invert_pipeline_mask=True):
return self._get(idx, time_idxs, pipeline_mask, invert_pipeline_mask,collect_pipeline_multi_outputs=True)
def get(self, idx, time_idxs, pipeline_mask=[], invert_pipeline_mask=True):
return self._get(idx, time_idxs, pipeline_mask, invert_pipeline_mask,collect_pipeline_multi_outputs=False)
def get_num_samples(self):
return len(self.file_lists[0]);
def _get(self, idx, time_idxs, pipeline_mask=[], invert_pipeline_mask=True,collect_pipeline_multi_outputs=False):
segments=[]
metadatas=[]
all_oos=[]
if isinstance(time_idxs, int):
time_idxs=[time_idxs]
for i in range(0,len(self.file_lists)):
vid_path = self.video_lists[i].keys()[idx]
video_obj = self.video_lists[i][vid_path]
safe_time_idxs=[safe_idx for safe_idx in time_idxs if safe_idx <len(video_obj)]
sliced_frame_list=video_obj[safe_time_idxs]
if self.io_pool is None:
processed_imgs=[]
pipeline_oos=[]
for frame in sliced_frame_list:
if collect_pipeline_multi_outputs:
processed_img,oo=apply_func_pipeline_masked_wrapped(menpo_grayscale_to_rgb(frame), self.structured_modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
pipeline_oos.append(oo)
processed_imgs.append(processed_img)
else:
processed_img = apply_func_pipeline_masked(menpo_grayscale_to_rgb(frame), self.modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
processed_imgs.append(processed_img)
else:
if collect_pipeline_multi_outputs:
frame_proc = lambda frame: apply_func_pipeline_masked_wrapped(menpo_grayscale_to_rgb(frame), self.structured_modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
processed_imgs_oos = self.io_pool.map(frame_proc,sliced_frame_list)
processed_imgs,pipeline_oos=zip(*processed_imgs_oos)
else:
frame_proc = lambda frame: apply_func_pipeline_masked(menpo_grayscale_to_rgb(frame), self.modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
processed_imgs = self.io_pool.map(frame_proc,sliced_frame_list)
metadata={'video_path':vid_path,'time_idxs':time_idxs}
#if(self.enable_caching and (mij_path in self.img_cache)):
#img = self.img_cache[mij_path];
##print(mij_path+'(using cache) ; ')
#elif(self.enable_caching and (not mij_path in self.img_cache)):
#img = self.imreader_callback(mij_path);
#self.img_cache[mij_path]=img
##print(mij_path + '(caching) ; ');
#else:
#img = self.imreader_callback(mij_path);
##print(mij_path+' ; ');
#pp_cache_key=stringman.sanitize_string(mij_path+str(pipeline_mask)+str(invert_pipeline_mask));
#if(self.enable_pp_caching and kv_haskey(pp_cache_key,self.img_pp_cache)):
#modified_img = self.img_pp_cache[pp_cache_key];
##print(mij_path+'(using img_pp_cache) ; ');
#elif(self.enable_pp_caching and not (pp_cache_key in self.img_pp_cache)):
#modified_img = apply_func_pipeline_masked(img, self.modification_pipelines[i], pipeline_mask, invert_pipeline_mask);
#self.img_pp_cache = kv_set(pp_cache_key,modified_img,self.img_pp_cache);
##print([mij_path,'(caching to img_pp_cache) ; ']);
#else:
#modified_img = apply_func_pipeline_masked(img, self.modification_pipelines[i], pipeline_mask, invert_pipeline_mask);
##print([mij_path,' ; ']);
segments.append(processed_imgs);
metadatas.append(metadata);
all_oos.append(pipeline_oos)
ret_val=(segments, metadatas)
if collect_pipeline_multi_outputs:
ret_val+=(all_oos,)
return ret_val
def start_fill(self,queuesize=10,pipeline_mask=[],invert_pipeline_mask=True,min_length=3,max_length=5,alpha=12,beta=0.5):
self.read_queue = Queue(maxsize=queuesize)
self.worker_threads_events=[]
self.worker_threads=[]
def structured_get_loop(queue_obj,event_obj):
while True:
print('fetching sequence')
data_obj,metadatas,data_obj_oos = self.get_random_segment_group(pipeline_mask, invert_pipeline_mask,min_length,max_length,alpha,beta)
queue_obj.put( {'data_obj':data_obj,
'data_paths':metadatas,
'data_obj_oos':data_obj_oos,
'metadatas':metadatas} )
event_is_set = event_obj.wait()
num_threads=1
for i in range(num_threads):
worker_event = threading.Event()
worker_event.set()
worker = threading.Thread(target=structured_get_loop, args=(self.read_queue,worker_event))
worker.setDaemon(True)
self.worker_threads.append(worker)
self.worker_threads_events.append(worker_event)
worker.start()
def get_random_segment_group_from_q(self,num_items=1):
return self.structured_gets_from_q(num_items)
def structured_gets_from_q(self,num_items=1):
items=[]
for i in range(0,num_items):
res=self.read_queue.get()
item_consumed_handle = self.read_queue.task_done
items.append((res,item_consumed_handle))
return items
def structured_get_from_q(self):
return self.structured_gets_from_q()
def pause_fill(self):
for event_obj in self.worker_threads_events:
event_obj.clear()
with self.read_queue.mutex:
self.read_queue.queue.clear()
def resume_fill(self):
for event_obj in self.worker_threads_events:
event_obj.set()
def gets(self, idxs, time_idxs_s, io_pool, pipeline_mask=[], invert_pipeline_mask=True):
f = lambda idx,time_idxs: self.get(idx, time_idxs,pipeline_mask, invert_pipeline_mask)
data_objs = io_pool.map(f,idxs,time_idxs_s)
return data_objs
def structured_gets(self, idxs, time_idxs_s, io_pool, pipeline_mask=[], invert_pipeline_mask=True):
f = lambda idx, time_idx: self.structured_get(idx, time_idxs, pipeline_mask, invert_pipeline_mask)
data_objs_tmp = io_pool.map(f,idxs,time_idxs_s)
data_obj_tmp,data_paths_tmp,data_obj_oos_tmp = [list(c) for c in zip(*data_objs_tmp)]
data_obj,data_paths,data_obj_oos=[list(x) for x in zip(*data_obj_tmp)],[list(x) for x in zip(*data_paths_tmp)],[list(x) for x in zip(*data_obj_oos_tmp)]
return (data_obj,data_paths,data_obj_oos)
def get_random_segment_group(self,pipeline_mask=[], invert_pipeline_mask=True,min_length=3,max_length=5,alpha=12,beta=0.5):
file_group_idx=random.randint(0,len(self.video_lists[0].keys())-1)
vid_paths=[vlist[file_group_idx] for vlist in self.file_lists]
vid_lengths=map(lambda vlist,path: len(vlist[path]), self.video_lists, vid_paths)
vid_length=min(vid_lengths)
start_idx=random.randint(0, vid_length-min_length)
segment_length=min(max_length,max(random.gammavariate(alpha,beta),min_length))*self.fps
end_idx=int(np.round(start_idx+segment_length))
time_idxs=range(start_idx,end_idx)
processed_segment = self.structured_get(file_group_idx,time_idxs,pipeline_mask,invert_pipeline_mask)
return processed_segment
def set_image_modifiers(self,modification_pipelines):
self.modification_pipelines=modification_pipelines;
def set_structured_image_modifiers(self,structured_modification_pipelines):
self.structured_modification_pipelines=structured_modification_pipelines;
def get_func_pipeline(self):
return copy.deepcopy(self.modification_pipelines);
def get_structured_func_pipeline(self):
return copy.deepcopy(self.structured_modification_pipelines)
def get_num_groups(self):
return len(self.file_lists);
def init_thread_pool(self):
self.io_pool=ThreadPool(nodes=4)
def close_thread_pool(self):
self.io_pool.close()
class GroupedVideoGenerator3:
def __init__(self,
image_generators=[],
fps=None,
modification_pipelines=None,
structured_modification_pipelines=None,
use_menpo_type=False,
opts={}):
self.video_lists=[]
self.file_lists=[]
print('GroupedVideoGenerator3()')
for i in range(0,len(image_generators)):
if(isinstance(image_generators[i], VideoGenerator3)):
assert(isinstance(image_generators[i].videos, OrderedDict))
self.file_lists.append(image_generators[i].file_list);
self.video_lists.append(image_generators[i].videos)
fps=image_generators[i].fps
elif(isinstance(image_generators[i],list)):
video_getter_pool=ThreadPool(nodes=8)
tmp_mpio_obj_list=video_getter_pool.map(menpo_import_video_verbose, image_generators[i])
tmp_mpio_obj_list = [x for x in tmp_mpio_obj_list if x is not None]
safe_idxs = [idx for idx,x in enumerate(tmp_mpio_obj_list) if x is not None]
tmp_file_list=image_generators[i]
self.file_lists.append([tmp_file_list[safe_idx] for safe_idx in safe_idxs])
self.video_lists.append(tmp_mpio_obj_list)
else:
raise TypeError('You can only make a GroupedImageGenerator2 '+\
'from a list of string-lists or ImageGenerator2s')
self.file_lists[i] = [tmp for tmp in self.file_lists[i] if not tmp is None]
self.fps=fps
self.modification_pipelines= \
modification_pipelines if not modification_pipelines is None else {}
self.structured_modification_pipelines= \
structured_modification_pipelines if not structured_modification_pipelines is None else {}
self.io_pool = None
self.enable_caching = 0
self.enable_pp_caching = 0
self.img_pp_cache = {}
self.img_pp_oo_cache = {}
self.img_cache = {}
self.opts = opts
self.use_menpo_type = use_menpo_type
def copy3(self):
ret_val=GroupedVideoGenerator3([],
fps=self.fps,
modification_pipelines=self.modification_pipelines,
structured_modification_pipelines=self.structured_modification_pipelines,
use_menpo_type=self.use_menpo_type,
opts=self.opts)
ret_val.file_lists = copy.copy(self.file_lists)
ret_val.video_lists = copy.copy(self.video_lists)
return ret_val
def group_lists(self,get_key_callback):
new_file_lists=sbpy_utils.core.sets.group_by(self.file_lists,get_key_callback);
self.file_lists=new_file_lists;
reordered_video_lists=[]
old_video_lists=self.video_lists
for view_idx,file_list in enumerate(new_file_lists):
reordered_video_list=OrderedDict()
old_video_list=old_video_lists[view_idx]
for g in file_list:
reordered_video_list[g]=old_video_list[g]
def dynamic_func(self,key,*args):
fhandle = self.opts[key];
out = fhandle(*args);
return out
def get_sample_dims(self, pipeline_mask=[], invert_pipeline_mask=True):
vidgroup_obj=self.get(0,0,pipeline_mask, invert_pipeline_mask)
return vidgroup_obj.shape()
def structured_get(self, idx, time_idxs, pipeline_mask=[], invert_pipeline_mask=True):
return self._get(idx, time_idxs, pipeline_mask, invert_pipeline_mask,collect_pipeline_multi_outputs=True)
def get(self, idx, time_idxs, pipeline_mask=[], invert_pipeline_mask=True):
return self._get(idx, time_idxs, pipeline_mask, invert_pipeline_mask,collect_pipeline_multi_outputs=False)
def get_num_samples(self):
return len(self.file_lists[0]);
def _get(self, idx, time_idxs, pipeline_mask=[], invert_pipeline_mask=True,collect_pipeline_multi_outputs=False):
segments=[]
metadatas=[]
all_oos=[]
if isinstance(time_idxs, int):
time_idxs=[time_idxs]
img_type_converter = (lambda x:x) if self.use_menpo_type else menpo_to_rgb
for i in range(0,len(self.file_lists)):
vid_path = self.video_lists[i].keys()[idx]
video_obj = self.video_lists[i][vid_path]
safe_time_idxs=[safe_idx for safe_idx in time_idxs if safe_idx <len(video_obj)]
sliced_frame_list=video_obj[safe_time_idxs]
if self.io_pool is None:
processed_imgs=[]
pipeline_oos=[]
for frame in sliced_frame_list:
if collect_pipeline_multi_outputs:
processed_img,oo=apply_func_pipeline_masked_wrapped(img_type_converter(frame), self.structured_modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
pipeline_oos.append(oo)
processed_imgs.append(processed_img)
else:
processed_img = apply_func_pipeline_masked(img_type_converter(frame), self.modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
processed_imgs.append(processed_img)
else:
if collect_pipeline_multi_outputs:
frame_proc = lambda frame: apply_func_pipeline_masked_wrapped(img_type_converter(frame), self.structured_modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
processed_imgs_oos = self.io_pool.map(frame_proc,sliced_frame_list)
processed_imgs,pipeline_oos=zip(*processed_imgs_oos)
else:
frame_proc = lambda frame: apply_func_pipeline_masked(img_type_converter(frame), self.modification_pipelines[i],pipeline_mask, invert_pipeline_mask)
processed_imgs = self.io_pool.map(frame_proc,sliced_frame_list)
#if(self.enable_caching and (mij_path in self.img_cache)):
#img = self.img_cache[mij_path];
##print(mij_path+'(using cache) ; ')
#elif(self.enable_caching and (not mij_path in self.img_cache)):
#img = self.imreader_callback(mij_path);
#self.img_cache[mij_path]=img
##print(mij_path + '(caching) ; ');
#else:
#img = self.imreader_callback(mij_path);
##print(mij_path+' ; ');
#pp_cache_key=stringman.sanitize_string(mij_path+str(pipeline_mask)+str(invert_pipeline_mask));
#if(self.enable_pp_caching and kv_haskey(pp_cache_key,self.img_pp_cache)):
#modified_img = self.img_pp_cache[pp_cache_key];
##print(mij_path+'(using img_pp_cache) ; ');
#elif(self.enable_pp_caching and not (pp_cache_key in self.img_pp_cache)):
#modified_img = apply_func_pipeline_masked(img, self.modification_pipelines[i], pipeline_mask, invert_pipeline_mask);
#self.img_pp_cache = kv_set(pp_cache_key,modified_img,self.img_pp_cache);
##print([mij_path,'(caching to img_pp_cache) ; ']);
#else:
#modified_img = apply_func_pipeline_masked(img, self.modification_pipelines[i], pipeline_mask, invert_pipeline_mask);
##print([mij_path,' ; ']);
if collect_pipeline_multi_outputs:
per_frame_extra_data=pipeline_oos
else:
per_frame_extra_data=None
new_vid_obj=Video(processed_imgs, video_path=vid_path, frame_idxs=safe_time_idxs, per_frame_extra_data=per_frame_extra_data)
segments.append(new_vid_obj);
return VideoGroup(segments)
def start_fill(self,queuesize=10,pipeline_mask=[],invert_pipeline_mask=True,min_length=3,max_length=5,alpha=12,beta=0.5):
self.read_queue = Queue(maxsize=queuesize)
self.worker_threads_events=[]
self.worker_threads=[]
def structured_get_loop(queue_obj,event_obj):
while True:
grouped_video_lists = self.get_random_segment_group(pipeline_mask, invert_pipeline_mask,min_length,max_length,alpha,beta)
queue_obj.put( grouped_video_lists )
event_is_set = event_obj.wait()
num_threads=1
for i in range(num_threads):
worker_event = threading.Event()
worker_event.set()
worker = threading.Thread(target=structured_get_loop, args=(self.read_queue,worker_event))
worker.setDaemon(True)
self.worker_threads.append(worker)
self.worker_threads_events.append(worker_event)
worker.start()
def get_random_segment_group_from_q(self,num_items=1):
return self.structured_gets_from_q(num_items)
def structured_gets_from_q(self,num_items=1):
video_groups=[]
for i in range(0,num_items):
video_group=self.read_queue.get()
video_groups.append(video_group)
return (VideoGroupList(video_groups).as_GroupedVideoLists(),[self.read_queue.task_done]*num_items)
def structured_get_from_q(self):
return self.structured_gets_from_q()
def pause_fill(self):
for event_obj in self.worker_threads_events:
event_obj.clear()
with self.read_queue.mutex:
self.read_queue.queue.clear()
def resume_fill(self):
for event_obj in self.worker_threads_events:
event_obj.set()
def gets(self, idxs, time_idxs_s, io_pool, pipeline_mask=[], invert_pipeline_mask=True):
f = lambda idx,time_idxs: self.get(idx, time_idxs,pipeline_mask, invert_pipeline_mask)
data_objs = io_pool.map(f,idxs,time_idxs_s)
return data_objs
def structured_gets(self, idxs, time_idxs_s, io_pool, pipeline_mask=[], invert_pipeline_mask=True):
f = lambda idx, time_idx: self.structured_get(idx, time_idxs, pipeline_mask, invert_pipeline_mask)
video_group_list = io_pool.map(f,idxs,time_idxs_s)
return video_group_list.as_GroupedVideoLists()
def get_random_segment_group(self,pipeline_mask=[], invert_pipeline_mask=True,min_length=3,max_length=5,alpha=12,beta=0.5):
file_group_idx=random.randint(0,len(self.video_lists[0].keys())-1)
vid_paths=[vlist[file_group_idx] for vlist in self.file_lists]
vid_lengths=map(lambda vlist,path: len(vlist[path]), self.video_lists, vid_paths)
vid_length=min(vid_lengths)
start_idx=random.randint(0, vid_length-min_length)
segment_length=min(max_length,max(random.gammavariate(alpha,beta),min_length))*self.fps
end_idx=int(np.round(start_idx+segment_length))
time_idxs=range(start_idx,end_idx)
#print('intended_segment length: '+str(len(time_idxs)))
processed_segment = self.structured_get(file_group_idx,time_idxs,pipeline_mask,invert_pipeline_mask)
print('processed_segment length: '+str(len(processed_segment)))
return processed_segment
def set_image_modifiers(self,modification_pipelines):
self.modification_pipelines=modification_pipelines;
def set_structured_image_modifiers(self,structured_modification_pipelines):
self.structured_modification_pipelines=structured_modification_pipelines;
def get_func_pipeline(self):
return copy.deepcopy(self.modification_pipelines);
def get_structured_func_pipeline(self):
return copy.deepcopy(self.structured_modification_pipelines)
def get_num_groups(self):
return len(self.file_lists);
def init_thread_pool(self):
self.io_pool=ThreadPool(nodes=4)
def close_thread_pool(self):
self.io_pool.close()