add demos, in progress

1 files changed, 89 insertions, 0 deletions

diff --git a/megapixels/commands/visualize/plot_3d_landmarks.py b/megapixels/commands/visualize/plot_3d_landmarks.py
new file mode 100644
index 00000000..a0f9e555
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+++ b/megapixels/commands/visualize/plot_3d_landmarks.py
@@ -0,0 +1,89 @@
+"""
+Crop images to prepare for training
+"""
+
+import click
+# from PIL import Image, ImageOps, ImageFilter, ImageDraw
+
+from app.settings import types
+from app.utils import click_utils
+from app.settings import app_cfg as cfg
+
+color_filters = {'color': 1, 'gray': 2, 'all': 3}
+
+@click.command()
+@click.option('-i', '--input', 'opt_fp_in', default=None,
+  help='Override enum input filename CSV')
+@click.option('-o', '--output', 'opt_fp_out', default=None,
+  help='Override enum output filename CSV')
+@click.option('-m', '--media', 'opt_dir_media', default=None,
+  help='Override enum media directory')
+@click.option('--store', 'opt_data_store',
+  type=cfg.DataStoreVar,
+  default=click_utils.get_default(types.DataStore.HDD),
+  show_default=True,
+  help=click_utils.show_help(types.Dataset))
+@click.option('--dataset', 'opt_dataset',
+  type=cfg.DatasetVar,
+  required=True,
+  show_default=True,
+  help=click_utils.show_help(types.Dataset))
+@click.option('--size', 'opt_size', 
+  type=(int, int), default=(480, 480),
+  help='Output image size')
+@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
+  help='Slice list of files')
+@click.option('--display/--no-display', 'opt_display', is_flag=True, default=False,
+  help='Display detections to debug')
+@click.option('-f', '--force', 'opt_force', is_flag=True,
+  help='Force overwrite file')
+@click.pass_context
+def cli(ctx, opt_fp_in, opt_dir_media, opt_fp_out, opt_data_store, opt_dataset, 
+  opt_size, opt_slice, opt_display, opt_force):
+  """Generates 3D landmark animations from CSV files"""
+  
+  import sys
+  import os
+  from os.path import join
+  from pathlib import Path
+  
+  from tqdm import tqdm
+  import numpy as np
+  import pandas as pd
+  import matplotlib.pyplot as plt
+
+  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_detector
+  from app.models.data_store import DataStore
+
+  # -------------------------------------------------
+  # init here
+
+  log = logger_utils.Logger.getLogger()
+
+  # set storage location
+  data_store = DataStore(opt_data_store, opt_dataset)
+  # load file records
+  fp_record = data_store.metadata(types.Metadata.FILE_RECORD)  # file_record.csv
+  df_records = pd.read_csv(fp_record).set_index('index')
+  # load ROI data
+  fp_roi = data_store.metadata(types.Metadata.FACE_ROI)  # face_roi.csv
+  df_rois = pd.read_csv(fp_roi).set_index('index')
+  fp_landmark_3d = data_store.metadata(types.Metadata.FACE_LANDMARK_3D_68) # face_landmark_3d_68.csv
+  df_landmarks_3d = pd.read_csv(fp_landmark_3d).set_index('index')
+  if opt_slice:
+    df_landmarks_3d = df_landmarks_3d[opt_slice[0]:opt_slice[1]]  # slice if you want
+  log.debug('processing {:,} groups'.format(len(df_landmarks_3d)))
+
+  # get filepath out
+  #fp_out = data_store.metadata(types.Metadata.FACE_ROI) if opt_fp_out is None else opt_fp_out
+  fp_out = '/home/adam/Downloads/3d.gif'
+
+
+  for roi_index, df_3d in tqdm(df_landmarks_3d.iterrows(), total=len(df_landmarks_3d)):
+    log.debug(f'roi_index: {roi_index}')
+    # unflatten points
+    points_3d = np.array([(df_3d[f'x{i}'], df_3d[f'y{i}'], df_3d[f'z{i}']) for i in range(1, 68)])
+    plot_utils.generate_3d_landmark_anim(points_3d, fp_out, size=(300,300))
+    break
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