add scut-fbp beauty predictor

1 files changed, 121 insertions, 0 deletions

diff --git a/megapixels/commands/demo/face_beauty.py b/megapixels/commands/demo/face_beauty.py
new file mode 100644
index 00000000..b1612f7c
--- /dev/null
+++ b/megapixels/commands/demo/face_beauty.py
@@ -0,0 +1,121 @@
+"""
+"""
+
+import click
+
+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('-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_size, opt_force, opt_display):
+  """Face detector demo"""
+  
+  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 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_beauty
+  from app.models.data_store import DataStore
+  
+
+  log = logger_utils.Logger.getLogger()
+
+  
+  # -------------------------------------------------
+  # load image
+
+  im = cv.imread(opt_fp_in)
+  # im = cv.cvtColor(im, cv.COLOR_BGR2RGB)
+  if im.shape[0] > 1280:
+    new_shape = (1280, im.shape[1] * 1280 / im.shape[0])
+  elif im.shape[1] > 1280:
+    new_shape = (im.shape[0] * 1280 / im.shape[1], 1280)
+  elif im.shape[0] < 640 or im.shape[1] < 640:
+    new_shape = (im.shape[0] * 2, im.shape[1] * 2)
+  else:
+    new_shape = im.shape[0:2]
+
+  im_resized = cv.resize(im, (int(new_shape[1]), int(new_shape[0])))
+
+  #im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
+
+
+  # ----------------------------------------------------------------------------
+  # detect face
+
+  face_detector = face_detector.DetectorDLIBCNN()  # -1 for CPU
+  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'face detected: {bbox_dim.to_xyxy()}')
+
+  # ----------------------------------------------------------------------------
+  # beauty
+  
+  beauty_predictor = face_beauty.FaceBeauty()
+  beauty_score = beauty_predictor.beauty(im_resized, bbox_dim)
+
+
+  # ----------------------------------------------------------------------------
+  # output
+  
+  log.info(f'Face coords: {bbox_dim} face')
+  log.info(f'beauty score: {(100*beauty_score):.2f}')
+
+
+  # ----------------------------------------------------------------------------
+  # draw
+
+  # draw 2d landmarks
+  im_beauty = im_resized.copy()
+  draw_utils.draw_bbox(im_beauty, bbox_dim)
+  txt = f'Beauty score: {(100*beauty_score):.2f}'
+  draw_utils.draw_text(im_beauty, bbox_dim.pt_tl, txt)
+
+
+  # ----------------------------------------------------------------------------
+  # save
+  
+  if opt_fp_out:
+    # save pose only
+    cv.imwrite(opt_fp_out, im_beauty)
+
+
+  # ----------------------------------------------------------------------------
+  # display
+
+  if opt_display:
+    # show all images here
+    cv.imshow('Beauty', im_beauty)
+    display_utils.handle_keyboard()
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