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| author | adamhrv <adam@ahprojects.com> | 2019-01-07 02:26:34 +0100 |
|---|---|---|
| committer | adamhrv <adam@ahprojects.com> | 2019-01-07 02:26:34 +0100 |
| commit | 5e5a7d09774bde195fe31ae143704eb124a764ac (patch) | |
| tree | 20e48a5f80e94c5021c01e9558de8af873e2eaf7 /megapixels/commands/cv/face_landmark_3d_68.py | |
| parent | 4bcb82c0f295d79d3d247252e7e98b2d986ae821 (diff) | |
add demos, in progress
Diffstat (limited to 'megapixels/commands/cv/face_landmark_3d_68.py')
| -rw-r--r-- | megapixels/commands/cv/face_landmark_3d_68.py | 11 |
1 files changed, 7 insertions, 4 deletions
diff --git a/megapixels/commands/cv/face_landmark_3d_68.py b/megapixels/commands/cv/face_landmark_3d_68.py index 56e60cda..a2d14d72 100644 --- a/megapixels/commands/cv/face_landmark_3d_68.py +++ b/megapixels/commands/cv/face_landmark_3d_68.py @@ -57,6 +57,7 @@ def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, import pandas as pd from app.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils + from app.utils import plot_utils from app.processors import face_landmarks from app.models.data_store import DataStore from app.models.bbox import BBox @@ -65,7 +66,7 @@ def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, # init here log = logger_utils.Logger.getLogger() - log.warn('3D landmark points are normalized in a (200, 200, 200) XYZ space') + log.warn('not normalizing points') # init filepaths data_store = DataStore(opt_data_store, opt_dataset) # set file output path @@ -76,7 +77,7 @@ def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, return # init face landmark processors - if opt_detector_type == types.FaceLandmark2D_5.FACE_ALIGNMENT: + if opt_detector_type == types.FaceLandmark3D_68.FACE_ALIGNMENT: # use FaceAlignment 68 point 3D detector landmark_detector = face_landmarks.FaceAlignment3D_68() else: @@ -122,16 +123,18 @@ def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, # get landmark points points = landmark_detector.landmarks(im_resized, bbox) # NB can't really normalize these points, but are normalized against 3D space - points_norm = landmark_detector.normalize(points, dim) # normalized using 200 + #points_norm = landmark_detector.normalize(points, dim) # normalized using 200 points_flattenend = landmark_detector.flatten(points) # display to screen if optioned if opt_display: - draw_utils.draw_landmarks2D(im_resized, points) + draw_utils.draw_landmarks3D(im_resized, points) draw_utils.draw_bbox(im_resized, bbox) cv.imshow('', im_resized) display_utils.handle_keyboard() + #plot_utils.generate_3d_landmark_anim(points, '/home/adam/Downloads/3d.gif') + results.append(points_flattenend) # create DataFrame and save to CSV |