def demo():
nSub = opts.getint('general', 'nTotSub')
fileList, outputFolder = myutil.parse(sys.argv)
# check for dlib saved weights for face landmark detection
# if it fails, dowload and extract it manually from
# http://sourceforge.net/projects/dclib/files/d.10/shape_predictor_68_face_landmarks.dat.bz2
check.check_dlib_landmark_weights()
## Preloading all the models for speed
allModels = myutil.preload(this_path, pose_models_folder, pose_models,
nSub)
for f in fileList:
if '#' in f: #skipping comments
continue
splitted = f.split(',')
image_key = splitted[0]
image_path = splitted[1]
image_landmarks = splitted[2]
img = cv2.imread(image_path, 1)
if image_landmarks != "None":
lmark = np.loadtxt(image_landmarks)
lmarks = []
lmarks.append(lmark)
else:
print('> Detecting landmarks')
lmarks = feature_detection.get_landmarks(img, this_path)
if len(lmarks) != 0:
## Copy back original image and flipping image in case we need
## This flipping is performed using all the model or all the poses
## To refine the estimation of yaw. Yaw can change from model to model...
img_display = img.copy()
img, lmarks, yaw = myutil.flipInCase(img, lmarks, allModels)
listPose = myutil.decidePose(yaw, opts, newModels)
## Looping over the poses
for poseId in listPose:
posee = pose_models[poseId]
## Looping over the subjects
for subj in range(1, nSub + 1):
pose = posee + '_' + str(subj).zfill(2) + '.mat'
print('> Looking at file: ' + image_path + ' with ' + pose)
# load detections performed by dlib library on 3D model and Reference Image
print("> Using pose model in " + pose)
## Indexing the right model instead of loading it each time from memory.
model3D = allModels[pose]
eyemask = model3D.eyemask
# perform camera calibration according to the first face detected
proj_matrix, camera_matrix, rmat, tvec = calib.estimate_camera(
model3D, lmarks[0])
## We use eyemask only for frontal
if not myutil.isFrontal(pose):
eyemask = None
##### Main part of the code: doing the rendering #############
rendered_raw, rendered_sym, face_proj, background_proj, temp_proj2_out_2, sym_weight = renderer.render(img, proj_matrix,\
model3D.ref_U, eyemask, model3D.facemask, opts)
########################################################
if myutil.isFrontal(pose):
rendered_raw = rendered_sym
## Cropping if required by crop_models
rendered_raw = myutil.cropFunc(pose, rendered_raw,
crop_models[poseId])
## Resizing if required
if resizeCNN:
rendered_raw = cv2.resize(
rendered_raw, (cnnSize, cnnSize),
interpolation=cv2.INTER_CUBIC)
## Saving if required
if opts.getboolean('general', 'saveON'):
subjFolder = outputFolder + '/' + image_key.split(
'_')[0]
myutil.mymkdir(subjFolder)
savingString = subjFolder + '/' + image_key + '_rendered_' + pose[
8:-7] + '_' + str(subj).zfill(2) + '.jpg'
cv2.imwrite(savingString, rendered_raw)
## Plotting if required
if opts.getboolean('general', 'plotON'):
myutil.show(img_display, img, lmarks, rendered_raw, \
face_proj, background_proj, temp_proj2_out_2, sym_weight)
else:
print('> Landmark not detected for this image...')
def render_fpn(inputFile, output_pose_db, outputFolder):
## Opening FPN pose db
pose_env = lmdb.open(output_pose_db, readonly=True)
pose_cnn_lmdb = pose_env.begin()
## looping over images
with open(inputFile, 'r') as csvfile:
csvreader = csv.reader(csvfile, delimiter=',')
lines = csvfile.readlines()
for lin in lines:
### key1, image_path_key_1
image_key = lin.split(',')[0]
if 'flip' in image_key:
continue
image_path = lin.split(',')[-1].rstrip('\n')
img = cv2.imread(image_path, 1)
pose_Rt_raw = pose_cnn_lmdb.get(image_key)
pose_Rt_flip_raw = pose_cnn_lmdb.get(image_key + '_flip')
if pose_Rt_raw is not None:
pose_Rt = np.frombuffer(pose_Rt_raw, np.float32)
pose_Rt_flip = np.frombuffer(pose_Rt_flip_raw, np.float32)
yaw = myutil.decideSide_from_db(img, pose_Rt, allModels)
if yaw < 0: # Flip image and get the corresponsidng pose
img = cv2.flip(img, 1)
pose_Rt = pose_Rt_flip
listPose = myutil.decidePose(yaw, opts, newModels)
## Looping over the poses
for poseId in listPose:
posee = pose_models[poseId]
## Looping over the subjects
for subj in [10]:
pose = posee + '_' + str(subj).zfill(2) + '.mat'
print '> Looking at file: ' + image_path + ' with ' + pose
# load detections performed by dlib library on 3D model and Reference Image
print "> Using pose model in " + pose
## Indexing the right model instead of loading it each time from memory.
model3D = allModels[pose]
eyemask = model3D.eyemask
# perform camera calibration according to the first face detected
proj_matrix, camera_matrix, rmat, tvec = calib.estimate_camera(
model3D, pose_Rt, pose_db_on=True)
## We use eyemask only for frontal
if not myutil.isFrontal(pose):
eyemask = None
##### Main part of the code: doing the rendering #############
rendered_raw, rendered_sym, face_proj, background_proj, temp_proj2_out_2, sym_weight = renderer_core.render(img, proj_matrix,\
model3D.ref_U, eyemask, model3D.facemask, opts)
########################################################
if myutil.isFrontal(pose):
rendered_raw = rendered_sym
## Cropping if required by crop_models
#rendered_raw = myutil.cropFunc(pose,rendered_raw,crop_models[poseId])
## Resizing if required
#if resizeCNN:
# rendered_raw = cv2.resize(rendered_raw, ( cnnSize, cnnSize ), interpolation=cv2.INTER_CUBIC )
## Saving if required
if opts.getboolean('general', 'saveON'):
subjFolder = outputFolder + '/' + image_key.split(
'_')[0]
myutil.mymkdir(subjFolder)
savingString = subjFolder + '/' + image_key + '_rendered_' + pose[
8:-7] + '_' + str(subj).zfill(2) + '.jpg'
cv2.imwrite(savingString, rendered_raw)
