change name

1 files changed, 0 insertions, 219 deletions

diff --git a/megapixels/commands/demo/3d_landmark_anim.py b/megapixels/commands/demo/3d_landmark_anim.py
deleted file mode 100644
index 22e09297..00000000
--- a/megapixels/commands/demo/3d_landmark_anim.py
+++ /dev/null
@@ -1,219 +0,0 @@
-"""
-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
-
-
-@click.command()
-@click.option('-i', '--input', 'opt_fp_in', default=None, required=True,
-  help='Image filepath')
-@click.option('-o', '--output', 'opt_fp_out', default=None,
-  help='GIF output path')
-@click.option('--size', 'opt_size', 
-  type=(int, int), default=(300, 300),
-  help='Output image size')
-@click.option('--gif-size', 'opt_gif_size', 
-  type=(int, int), default=(480, 480),
-  help='GIF output size')
-@click.option('--gif-frames', 'opt_gif_frames', default=15,
-  help='GIF frames')
-@click.option('-g', '--gpu', 'opt_gpu', default=0,
-  help='GPU index')
-@click.option('-f', '--force', 'opt_force', is_flag=True,
-  help='Force overwrite file')
-@click.option('--display/--no-display', 'opt_display', is_flag=True, default=False,
-  help='Display detections to debug')
-@click.pass_context
-def cli(ctx, opt_fp_in, opt_fp_out, opt_gpu, opt_gif_frames, 
-  opt_size, opt_gif_size, opt_force, opt_display):
-  """Generates 3D landmark animations from CSV files"""
-  
-  import sys
-  import os
-  from os.path import join
-  from pathlib import Path
-  import time
-  
-  from tqdm import tqdm
-  import numpy as np
-  import pandas as pd
-  import cv2 as cv
-  import dlib
-  from PIL import Image
-  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, face_landmarks
-  from app.models.data_store import DataStore
-  
-  # TOOD add selective testing
-  opt_run_pose = True
-  opt_run_2d_68 = True
-  opt_run_3d_68 = True
-  opt_run_3d_68 = True
-
-
-  # -------------------------------------------------
-  # init here
-
-
-  log = logger_utils.Logger.getLogger()
-
-  # load image
-  im = cv.imread(opt_fp_in)
-  im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
-
-
-  # ----------------------------------------------------------------------------
-  # detect face
-
-  face_detector = face_detector.DetectorDLIBCNN(gpu=opt_gpu)  # -1 for CPU
-  log.info('detecting face...')
-  st = time.time()
-  bboxes = face_detector.detect(im_resized, largest=True)
-  bbox = bboxes[0]
-  dim = im_resized.shape[:2][::-1]
-  bbox_dim = bbox.to_dim(dim)
-  if not bbox:
-    log.error('no face detected')
-    return
-  else:
-    log.info(f'Detected face in {(time.time() - st):.2f}s')
-  log.info('')
-
-
-  # ----------------------------------------------------------------------------
-  # detect 3D landmarks
-  
-  log.info('loading 3D landmark generator files...')
-  landmark_detector_3d_68 = face_landmarks.FaceAlignment3D_68(gpu=opt_gpu)  # -1 for CPU
-  log.info('generating 3D landmarks...')
-  st = time.time()
-  points_3d_68 = landmark_detector_3d_68.landmarks(im_resized, bbox_dim.to_xyxy())
-  log.info(f'generated 3D landmarks in {(time.time() - st):.2f}s')
-  log.info('')
-
-
-  # ----------------------------------------------------------------------------
-  # generate 3D GIF animation
-  
-  log.info('generating 3D animation...')
-  if not opt_fp_out:
-    fpp_im = Path(opt_fp_in)
-    fp_out = join(fpp_im.parent, f'{fpp_im.stem}_anim.gif')
-  else:
-    fp_out = opt_fp_out
-  st = time.time()
-  plot_utils.generate_3d_landmark_anim(np.array(points_3d_68), fp_out, 
-    size=opt_gif_size, num_frames=opt_gif_frames)
-  log.info(f'Generated animation in {(time.time() - st):.2f}s')
-  log.info(f'Saved to: {fp_out}')
-  log.info('')
-
-
-  # ----------------------------------------------------------------------------
-  # generate face vectors, only to test if feature extraction works
-  
-  log.info('initialize face recognition model...')
-  from app.processors import face_recognition
-  face_rec = face_recognition.RecognitionDLIB()
-  st = time.time()
-  log.info('generating face vector...')
-  vec = face_rec.vec(im_resized, bbox_dim)
-  log.info(f'generated face vector in {(time.time() - st):.2f}s')
-  log.info('')
-
-
-  # ----------------------------------------------------------------------------
-  # generate 68 point landmarks using dlib
-  
-  log.info('initializing face landmarks 68 dlib...')
-  from app.processors import face_landmarks
-  landmark_detector_2d_68 = face_landmarks.Dlib2D_68()
-  log.info('generating 2D 68PT landmarks...')
-  st = time.time()
-  points_2d_68 = landmark_detector_2d_68.landmarks(im_resized, bbox_dim)
-  log.info(f'generated 2D 68PT face landmarks in {(time.time() - st):.2f}s')
-  log.info('')
-
-
-  # ----------------------------------------------------------------------------
-  # generate pose from 68 point 2D landmarks
-  
-  if opt_run_pose:
-    log.info('initialize pose...')
-    from app.processors import face_pose
-    pose_detector = face_pose.FacePoseDLIB()
-    log.info('generating pose...')
-    st = time.time()
-    pose_data = pose_detector.pose(points_2d_68, dim)
-    log.info(f'generated pose {(time.time() - st):.2f}s')
-    log.info('')
-
-
-  # x
-
-
-
-  # display
-  if opt_display:
-    
-    # draw bbox
-    
-    # draw 3d landmarks
-    im_landmarks_3d_68 = im_resized.copy()
-    draw_utils.draw_landmarks3D(im_landmarks_3d_68, points_3d_68)
-    draw_utils.draw_bbox(im_landmarks_3d_68, bbox_dim)
-
-    # draw 2d landmarks
-    im_landmarks_2d_68 = im_resized.copy()
-    draw_utils.draw_landmarks2D(im_landmarks_2d_68, points_2d_68)
-    draw_utils.draw_bbox(im_landmarks_2d_68, bbox_dim)
-
-    # draw pose
-    if opt_run_pose:
-      im_pose = im_resized.copy()
-      draw_utils.draw_pose(im_pose, pose_data['point_nose'], pose_data['points'])
-      draw_utils.draw_degrees(im_pose, pose_data)
-
-    # draw animated GIF
-    im = Image.open(opt_fp_out)
-    im_frames = []
-    duration = im.info['duration']
-    try:
-      while True:
-        im.seek(len(im_frames))
-        mypalette = im.getpalette()
-        im.putpalette(mypalette)
-        im_jpg = Image.new("RGB", im.size)
-        im_jpg.paste(im)
-        im_np = im_utils.pil2np(im_jpg.copy())
-        im_frames.append(im_np)
-    except EOFError:
-      pass # end of GIF sequence
-
-    n_frames = len(im_frames)
-    frame_number = 0
-
-    while True:
-      # show all images here
-      cv.imshow('Original', im_resized)
-      cv.imshow('2D 68PT Landmarks', im_landmarks_2d_68)
-      cv.imshow('3D 68PT Landmarks', im_landmarks_3d_68)
-      cv.imshow('Pose', im_pose)
-      cv.imshow('3D 68pt GIF', im_frames[frame_number])
-      frame_number = (frame_number + 1) % n_frames
-      k = cv.waitKey(duration) & 0xFF
-      if k == 27 or k == ord('q'):  # ESC
-        cv.destroyAllWindows()
-        sys.exit()
-      elif k != 255:
-        # any key to continue
-        break
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