def upgradeToFaceSamples ( samples ):
sample_list = []
for s in io.progress_bar_generator(samples, "Loading"):
s_filename_path = Path(s.filename)
try:
if s_filename_path.suffix == '.png':
dflimg = DFLPNG.load ( str(s_filename_path) )
elif s_filename_path.suffix == '.jpg':
dflimg = DFLJPG.load ( str(s_filename_path) )
else:
dflimg = None
if dflimg is None:
print ("%s is not a dfl image file required for training" % (s_filename_path.name) )
continue
pitch, yaw = LandmarksProcessor.estimate_pitch_yaw ( dflimg.get_landmarks() )
sample_list.append( s.copy_and_set(sample_type=SampleType.FACE,
face_type=FaceType.fromString (dflimg.get_face_type()),
shape=dflimg.get_shape(),
landmarks=dflimg.get_landmarks(),
pitch=pitch,
yaw=yaw) )
except:
print ("Unable to load %s , error: %s" % (str(s_filename_path), traceback.format_exc() ) )
return sample_list
def process_data(self, data):
filepath = Path(data[0])
try:
if filepath.suffix == '.png':
dflimg = DFLPNG.load(str(filepath))
elif filepath.suffix == '.jpg':
dflimg = DFLJPG.load(str(filepath))
else:
dflimg = None
if dflimg is None:
self.log_err("%s is not a dfl image file" %
(filepath.name))
return [1, [str(filepath)]]
bgr = cv2_imread(str(filepath))
if bgr is None:
raise Exception("Unable to load %s" % (filepath.name))
gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
sharpness = estimate_sharpness(
gray) if self.include_by_blur else 0
pitch, yaw = LandmarksProcessor.estimate_pitch_yaw(
dflimg.get_landmarks())
hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
except Exception as e:
self.log_err(e)
return [1, [str(filepath)]]
return [0, [str(filepath), sharpness, hist, yaw]]
def sort_by_face_pitch(input_path):
io.log_info("Sorting by face pitch...")
img_list = []
trash_img_list = []
for filepath in io.progress_bar_generator(
Path_utils.get_image_paths(input_path), "Loading"):
filepath = Path(filepath)
if filepath.suffix == '.png':
dflimg = DFLPNG.load(str(filepath))
elif filepath.suffix == '.jpg':
dflimg = DFLJPG.load(str(filepath))
else:
dflimg = None
if dflimg is None:
io.log_err("%s is not a dfl image file" % (filepath.name))
trash_img_list.append([str(filepath)])
continue
pitch, yaw = LandmarksProcessor.estimate_pitch_yaw(
dflimg.get_landmarks())
img_list.append([str(filepath), pitch])
io.log_info("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list, trash_img_list
def onClientProcessData(self, data):
filepath = Path(data[0])
try:
if filepath.suffix == '.png':
dflimg = DFLPNG.load(str(filepath),
print_on_no_embedded_data=True)
elif filepath.suffix == '.jpg':
dflimg = DFLJPG.load(str(filepath),
print_on_no_embedded_data=True)
else:
print("%s is not a dfl image file" % (filepath.name))
raise Exception("")
bgr = cv2_imread(str(filepath))
if bgr is None:
raise Exception("Unable to load %s" % (filepath.name))
gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
gray_masked = (gray * LandmarksProcessor.get_image_hull_mask(
bgr.shape, dflimg.get_landmarks())[:, :, 0]).astype(np.uint8)
sharpness = estimate_sharpness(gray_masked)
pitch, yaw = LandmarksProcessor.estimate_pitch_yaw(
dflimg.get_landmarks())
hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
except Exception as e:
print(e)
return [1, [str(filepath)]]
return [0, [str(filepath), sharpness, hist, yaw]]
def sort_by_face_pitch(input_path):
print("Sorting by face pitch...")
img_list = []
for filepath in tqdm(Path_utils.get_image_paths(input_path),
desc="Loading",
ascii=True):
filepath = Path(filepath)
if filepath.suffix == '.png':
dflimg = DFLPNG.load(str(filepath), print_on_no_embedded_data=True)
elif filepath.suffix == '.jpg':
dflimg = DFLJPG.load(str(filepath), print_on_no_embedded_data=True)
else:
print("%s is not a dfl image file" % (filepath.name))
continue
pitch, yaw = LandmarksProcessor.estimate_pitch_yaw(
dflimg.get_landmarks())
img_list.append([str(filepath), pitch])
print("Sorting...")
img_list = sorted(img_list, key=operator.itemgetter(1), reverse=True)
return img_list
def batch_func(self, generator_id):
gen_sq = self.generators_sq[generator_id]
samples = self.samples
samples_len = len(samples)
samples_idxs = [ *range(samples_len) ] [generator_id::self.generators_count]
repeat_samples_idxs = []
if len(samples_idxs) == 0:
raise ValueError('No training data provided.')
if self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
if all ( [ samples[idx] == None for idx in samples_idxs] ):
raise ValueError('Not enough training data. Gather more faces!')
if self.sample_type == SampleType.FACE or self.sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
shuffle_idxs = []
elif self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
shuffle_idxs = []
shuffle_idxs_2D = [[]]*samples_len
while True:
while not gen_sq.empty():
idxs = gen_sq.get()
for idx in idxs:
if idx in samples_idxs:
repeat_samples_idxs.append(idx)
batches = None
for n_batch in range(self.batch_size):
while True:
sample = None
if len(repeat_samples_idxs) > 0:
idx = repeat_samples_idxs.pop()
if self.sample_type == SampleType.FACE or self.sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
sample = samples[idx]
elif self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
sample = samples[(idx >> 16) & 0xFFFF][idx & 0xFFFF]
else:
if self.sample_type == SampleType.FACE or self.sample_type == SampleType.FACE_WITH_CLOSE_TO_SELF:
if len(shuffle_idxs) == 0:
shuffle_idxs = samples_idxs.copy()
np.random.shuffle(shuffle_idxs)
idx = shuffle_idxs.pop()
sample = samples[ idx ]
elif self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
if len(shuffle_idxs) == 0:
shuffle_idxs = samples_idxs.copy()
np.random.shuffle(shuffle_idxs)
idx = shuffle_idxs.pop()
if samples[idx] != None:
if len(shuffle_idxs_2D[idx]) == 0:
shuffle_idxs_2D[idx] = random.sample( range(len(samples[idx])), len(samples[idx]) )
idx2 = shuffle_idxs_2D[idx].pop()
sample = samples[idx][idx2]
idx = (idx << 16) | (idx2 & 0xFFFF)
if sample is not None:
try:
x = SampleProcessor.process (sample, self.sample_process_options, self.output_sample_types, self.debug)
except:
raise Exception ("Exception occured in sample %s. Error: %s" % (sample.filename, traceback.format_exc() ) )
if type(x) != tuple and type(x) != list:
raise Exception('SampleProcessor.process returns NOT tuple/list')
if batches is None:
batches = [ [] for _ in range(len(x)) ]
if self.add_sample_idx:
batches += [ [] ]
i_sample_idx = len(batches)-1
if self.add_pitch:
batches += [ [] ]
i_pitch = len(batches)-1
if self.add_yaw:
batches += [ [] ]
i_yaw = len(batches)-1
for i in range(len(x)):
batches[i].append ( x[i] )
if self.add_sample_idx:
batches[i_sample_idx].append (idx)
if self.add_pitch or self.add_yaw:
pitch, yaw = LandmarksProcessor.estimate_pitch_yaw (sample.landmarks)
if self.add_pitch:
batches[i_pitch].append ([pitch])
if self.add_yaw:
batches[i_yaw].append ([yaw])
break
yield [ np.array(batch) for batch in batches]
