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Commit b64c606a authored by Ketan Kotwal's avatar Ketan Kotwal
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script to annotate both databases

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bob/paper/nir_patch_pooling/script/annotate_database.py
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#
# script to generate face annotations for NMAD directly over NIR data
# @ Ketan Kotwal
# Script to generate face annotations for NIR data
# @author: Ketan Kotwal
#
#----------------------------------------------------------
#------------------------------------------------------------------------------
# imports
from bob.pad.face.preprocessor.FaceCropAlign import detect_face_landmarks_in_image
from bob.ip.color import rgb_to_gray
from bob.ip.facelandmarks import detect_landmarks
import bob.bio.video
import bob.io.base
from bob.bio.video import FrameContainer
from bob.io.base import create_directories_safe
import numpy as np
import json, h5py
import json
import os, sys
from bob.paper.makeup_aim.database import MLFPDatabase
#------------------------------------------------------------------------------
ORIGINAL_DIRECTORY = "/idiap/temp/kkotwal/nmad_experiments/mlfp_int/"
class Annotator:
#----------------------------------------------------------
def __init__(self):
pass
class AnnotationGenerator:
def __init__(self, input_directory, annotation_directory):
self.input_directory = input_directory.rstrip("/")
self.annotation_directory = annotation_directory.rstrip("/")
if not os.path.exists(self.annotation_directory):
os.makedirs(self.annotation_directory)
self.file_objects = self._load_db(self)
print("Input directory: {}".format(self.input_directory))
print("Annotation directory: {}".format(self.annotation_directory))
#--------------------------------------
def _load_db(self, protocol):
database = MLFPDatabase(original_directory = ORIGINAL_DIRECTORY,
protocol = "grandtest", annotation_directory = None)
return database.objects(protocol="grandtest")
#----------------------------------------------------------
#------------------------------------------------------------------------------
def find_annotations(self, image):
# if image is grayscale, convert to 3 channel for face detection
if len(image.shape) == 2:
image = np.repeat(image[:, :, np.newaxis], 3, axis=2)
image = np.transpose(image, (2,0,1))
else:
print("\t\tNot altering dimensions")
# find annotations using MTCNN
frame_annotations = detect_face_landmarks_in_image(image, method="mtcnn")
if frame_annotations:
print(" --> Found using MTCNN")
logger.debug(" --> Found using MTCNN")
return frame_annotations
# else, find annotations using dlib
frame_annotations = detect_face_landmarks_in_image(image, method="dlib")
if frame_annotations:
print(" --> Found using dlib")
return frame_annotations
# else, find annotations using landmark detection routines from bob
frame_annotations = self.bob_annotate(image.astype(np.uint8))
if frame_annotations:
print(" --> Found using bob routine")
logger.debug(" --> Found using dlib")
return frame_annotations
# else, return empty dictionary with warning
print(" --> Could not find annotations")
logger.warning(" --> Could not find annotations")
return {}
#----------------------------------------------------------
def _get_eye_pos(self, lm):
"""
This function returns the locations of left and right eyes
"""
left_eye_t = (lm[36, :] + lm[39, :]) / 2.0
right_eye_t = (lm[42, :] + lm[45, :]) / 2.0
right_eye = (int(left_eye_t[1]), int(left_eye_t[0]))
left_eye = (int(right_eye_t[1]), int(right_eye_t[0]))
return right_eye, left_eye
#--------------------------------------
#------------------------------------------------------------------------------
def bob_annotate(self, image):
def normalize_image(self, image, n_sigma=3.0): # or 4.0
image = rgb_to_gray(image)
lm1 = detect_landmarks(image, 1)
lm = lm1[0].landmarks
bounding_box = lm1[0].bounding_box
annotations = {}
assert(len(image.shape)==2)
image = image.astype(np.float64)
if lm is not None:
lm = np.array(lm)
lm = np.vstack((lm[:, 1], lm[:, 0])).T
right_eye, left_eye = self._get_eye_pos(lm)
# use central region of image to determine parameters for normalization
h, w = image.shape
region = image[int(0.25*h):int(0.75*h), int(0.25*w):int(0.75*w)]
# calculate median values
med = np.median(region)
mad = np.median(np.abs(region - med))
image_n = ((image-med+n_sigma*mad)/(2.0*n_sigma*mad))*255.0
points = []
for i in range(lm.shape[0]):
points.append((int(lm[i, 0]), int(lm[i, 1])))
# Clamping to 0-255
image_n = np.maximum(image_n, 0)
image_n = np.minimum(image_n, 255)
annotations['topleft'] = bounding_box.topleft
annotations['bottomright'] = bounding_box.bottomright
annotations['landmarks'] = points
annotations['leye'] = left_eye
annotations['reye'] = right_eye
image_n = image_n.astype(np.uint8)
return annotations
return image_n
#--------------------------------------
#------------------------------------------------------------------------------
def normalize_image(self, image, n_sigma=4.0, norm_method="MAD"):
def process(self, fc):
face = image[image.shape[0]>>2:3*image.shape[0]>>2, \
image.shape[1]>>2:3*image.shape[1]>>2]
"""
fc: FrameContainer
returns:
annotations: dictionary where each frame index is a key.
"""
annotations = {}
prev_index = None
for index, frame, _ in fc:
face = face.astype(np.float64)
image = image.astype(np.float64)
image = self.normalize_image(frame)
assert(len(image.shape)==2)
try:
frame_annotations = self.find_annotations(image)
if norm_method=='STD':
mu = np.mean(face)
std = np.std(face)
image_n=((image-mu+n_sigma*std)/(2.0*n_sigma*std))*255.0
except Exception as e:
logger.error("\tException: {}".format(e))
# copy annotations of previous frame
if (prev_index is not None):
frame_annotations = annotations[prev_index]
logger.warning("\tCopying annotations of previous frame")
else:
frame_annotations = {}
logger.warning("\tSetting empty annotations")
if norm_method=='MAD':
annotations[str(index)] = frame_annotations
prev_index = str(index)
return annotations
med = np.median(face)
mad = np.median(np.abs(face - med))
image_n = ((image-med+n_sigma*mad)/(2.0*n_sigma*mad))*255.0
#------------------------------------------------------------------------------
if norm_method=='MINMAX':
t_min = np.min(face)
t_max = np.max(face)
image_n = ((image-t_min)/(t_max-t_min))*255.0
#------------------------------------------------------------------------------
# Clamping to 0-255
image_n=np.maximum(image_n, 0)
image_n=np.minimum(image_n, 255)
class AnnotationGenerator:
image_n = image_n.astype(np.uint8)
def __init__(self, database = None, annotation_directory = None):
return image_n
self.database = database
#----------------------------------------------------------
self.annotation_directory = annotation_directory
def process_image(self, filename):
if not os.path.exists(self.annotation_directory):
os.makedirs(self.annotation_directory)
logger.warning("Annotation directory created at: {}"\
.format(self.annotation_directory))
# load the image
fc = h5py.File(os.path.join(ORIGINAL_DIRECTORY, filename.path + ".hdf5"))
annotations = {}
for i, frame in enumerate(fc.keys()):
print(" Frame {:02d}".format(i))
image = fc[frame]["array"].value
self.annotator = Annotator()
try:
tmp_annotations = self.find_annotations(image)
#------------------------------------------------------------------------------
except Exception as e:
print(" Exception: {}".format(e))
def process_video(self, filename):
if (i > 0) and (bool(annotations[str(i-1)])):
tmp_annotations = annotations[str(i-1)]
print(" Copying annotations of previous frame")
else:
tmp_annotations = {}
print(" Annotations for previous frame do not exist. Setting empty annotations")
# load the video into framecontainer
fc = filename.load(directory = self.database.original_directory,\
extension = self.database.original_extension)
annotations[str(i)] = tmp_annotations
# obtain the annotations
annotations = self.annotator.process(fc)
# save the annotations as json.
json_filepath = os.path.join(self.annotation_directory, filename.path + ".json")
bob.io.base.create_directories_safe(directory=os.path.split(json_filepath)[0], dryrun=False)
create_directories_safe(directory=os.path.split(json_filepath)[0], dryrun=False)
with open(json_filepath, "w+") as json_file:
json_file.write(json.dumps(annotations))
fc.close()
return
#--------------------------------------
#------------------------------------------------------------------------------
def run(self, job_index):
total = len(self.file_objects)
print("Found {} files to be annotated".format(total))
# collect the files to be processed
self.filelist = self.database.objects()
total = len(self.filelist)
logger.info("Files to be annotated: {}".format(total))
# calculate split indices if computation is parallel
if(job_index != -1):
num_jobs = 32
num_jobs = 32
file_range = np.linspace(0, total, num_jobs+1, dtype=np.int)
start_id = file_range[job_index]
end_id = file_range[job_index+1]
start_index = file_range[job_index]
end_index = file_range[job_index+1]
else:
start_id = 0
end_id = total
print("Processing Job Index: {} (Files: {} to {})".format(job_index, start_id, end_id))
start_index = 0
end_index = total
for i, f in enumerate(self.file_objects[start_id:end_id]):
print("Processing Job Index: {} (Files: {} to {})".format(job_index, start_index, end_index))
print("[{:03d}/{:03d}] Sample: {}".format(i+1, total, f.path))
# process each video in the given range
for idx, f in enumerate(self.filelist[start_index:end_index]):
logger.info("[{:03d}/{:03d}] Sample: {}".format(idx+1, total, f.path))
json_filepath = os.path.join(self.annotation_directory, f.path + ".json")
if not os.path.exists(json_filepath):
self.process_image(f)
self.process_video(f)
else:
print("Annotations exist for {}. Skipping".format(f.path))
logger.info("Annotations exist: {}. Skipping".format(f.path))
return
#------------------------------------------------------------------------------
#----------------------------------------------------------
def main():
job_index = int(float(sys.argv[1]))
print("# Job Index: {}".format(job_index))
if len(sys.argv) < 3:
print("Usage: {} <database> <annotation-directory> [<job-index>]"\
.format(__name__))
exit(0)
database = sys.argv[1]
annotation_directory = sys.argv[2]
print("Database: {}. Annotation directory: {}".format(database,\
annotation_directory))
input_directory = ORIGINAL_DIRECTORY #sys.argv[1]
annotation_directory = "/idiap/temp/kkotwal/nmad_experiments/annotations/mlfp"
if len(sys.argv) >= 4:
job_index = int(float(sys.argv[3]))
print("# Job Index: {}".format(job_index))
else:
job_index = -1
ag = AnnotationGenerator(input_directory, annotation_directory)
ag = AnnotationGenerator(database, annotation_directory)
ag.run(job_index)
#----------------------------------------------------------
#------------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------
#------------------------------------------------------------------------------
......
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