Merge branch 'master' of github.com:adamhrv/megapixels_dev

5 files changed, 154 insertions, 123 deletions

diff --git a/megapixels/commands/cv/faces_to_csv.py b/megapixels/commands/cv/_old_files_to_face_rois.py
index 1fd47571..d92cbd74 100644
--- a/megapixels/commands/cv/faces_to_csv.py
+++ b/megapixels/commands/cv/_old_files_to_face_rois.py
@@ -12,8 +12,8 @@ from app.settings import app_cfg as cfg
 color_filters = {'color': 1, 'gray': 2, 'all': 3}
 
 @click.command()
-@click.option('-i', '--input', 'opt_dirs_in', required=True, multiple=True,
-  help='Input directory')
+@click.option('-i', '--input', 'opt_fp_files', required=True,
+  help='Input file meta CSV')
 @click.option('-o', '--output', 'opt_fp_out', required=True,
   help='Output CSV')
 @click.option('-e', '--ext', 'opt_ext',
diff --git a/megapixels/commands/cv/embeddings.py b/megapixels/commands/cv/embeddings.py
deleted file mode 100644
index 9cb26ae7..00000000
--- a/megapixels/commands/cv/embeddings.py
+++ /dev/null
@@ -1,100 +0,0 @@
-"""
-Crop images to prepare for training
-"""
-
-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', required=True,
-  help='Input directory')
-@click.option('-r', '--records', 'opt_fp_records', required=True,
-  help='Input directory')
-@click.option('-m', '--media', 'opt_fp_media', required=True,
-  help='Image directory')
-@click.option('-o', '--output', 'opt_fp_out', required=True,
-  help='Output CSV')
-@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('--slice', 'opt_slice', type=(int, int), default=(None, None),
-  help='Slice list of files')
-@click.option('-f', '--force', 'opt_force', is_flag=True,
-  help='Force overwrite file')
-@click.option('-j', '--jitters', 'opt_jitters', default=cfg.DLIB_FACEREC_JITTERS,
-  help='Number of jitters')
-@click.option('-p', '--padding', 'opt_padding', default=cfg.DLIB_FACEREC_PADDING,
-  help='Percentage padding')
-@click.pass_context
-def cli(ctx, opt_fp_in, opt_fp_records, opt_fp_out, opt_fp_media, opt_size, opt_gpu,
-  opt_slice, opt_jitters, opt_padding, opt_force):
-  """Converts frames with faces to CSV of rows"""
-  
-  import sys
-  import os
-  from os.path import join
-  from pathlib import Path
-  
-  from tqdm import tqdm
-  import numpy as np
-  import dlib  # must keep a local reference for dlib
-  import cv2 as cv
-  import dlib
-  import pandas as pd
-
-  from app.utils import logger_utils, file_utils, im_utils
-  from app.models.bbox import BBox
-  from app.processors import face_recognition
-
-  # -------------------------------------------------
-  # init here
-
-  log = logger_utils.Logger.getLogger()
-
-  if not opt_force and Path(opt_fp_out).exists():
-    log.error('File exists. Use "-f / --force" to overwite')
-    return
-  
-  # init dlib FR
-  facerec = face_recognition.RecognitionDLIB()
-
-  # load data
-  df_rois = pd.read_csv(opt_fp_in)
-  df_records = pd.read_csv(opt_fp_records)
-
-  if opt_slice:
-    df_rois = df_rois[opt_slice[0]:opt_slice[1]]
-  log.info('Processing {:,} rows'.format(len(df_rois)))
-  nrows = len(df_rois)
-
-  # face vecs
-  vecs = []
-
-  for roi_idx, row in tqdm(df_rois.iterrows(), total=nrows):
-    # make image path
-    record_id = int(row['id'])
-    df = df_records.iloc[record_id]
-    fp_im = join(opt_fp_media, df['subdir'], '{}.{}'.format(df['fn'], df['ext'])) 
-    # load image
-    im = cv.imread(fp_im)
-    # make bbox
-    xywh = [row['x'], row['y'], row['w'] , row['h']]
-    bbox = BBox.from_xywh(*xywh)
-    # scale to actual image size
-    dim = (row['image_width'], row['image_height'])
-    bbox_dim = bbox.to_dim(dim)
-    # compute vec
-    vec = facerec.vec(im, bbox_dim, jitters=opt_jitters, padding=opt_padding)
-    vec_str = ','.join([repr(x) for x in vec])
-    vecs.append( {'id': row['id'], 'vec': vec_str})
-  
-  # save data
-  file_utils.mkdirs(opt_fp_out)
-  df_vecs = pd.DataFrame.from_dict(vecs)
-  df_vecs.to_csv(opt_fp_out, index=False)
-  log.info('saved {:,} lines to {}'.format(len(df_vecs), opt_fp_out))
\ No newline at end of file
diff --git a/megapixels/commands/cv/faces_to_csv_indexed.py b/megapixels/commands/cv/files_to_rois.py
index ef958f89..1aaf991c 100644
--- a/megapixels/commands/cv/faces_to_csv_indexed.py
+++ b/megapixels/commands/cv/files_to_rois.py
@@ -100,7 +100,7 @@ def cli(ctx, opt_fp_in, opt_dir_media, opt_fp_out, opt_size, opt_detector_type,
   data = []
 
   for df_file in tqdm(df_files.itertuples(), total=len(df_files)):
-    fp_im = join(opt_dir_media, df_file.subdir, '{}.{}'.format(df_file.fn, df_file.ext))
+    fp_im = join(opt_dir_media, str(df_file.subdir), f'{df_file.fn}.{df_file.ext}')
     im = cv.imread(fp_im)
 
     # filter out color or grayscale iamges
@@ -115,7 +115,7 @@ def cli(ctx, opt_fp_in, opt_dir_media, opt_fp_out, opt_size, opt_detector_type,
         continue
         
     try:
-      bboxes = detector.detect(im, opt_size=opt_size, opt_pyramids=opt_pyramids, opt_largest=opt_largest)
+      bboxes = detector.detect(im, size=opt_size, pyramids=opt_pyramids, largest=opt_largest)
     except Exception as e:
       log.error('could not detect: {}'.format(fp_im))
       log.error('{}'.format(e))
diff --git a/megapixels/commands/cv/face_pose_to_csv.py b/megapixels/commands/cv/rois_to_pose.py
index ca7489de..3877cecf 100644
--- a/megapixels/commands/cv/face_pose_to_csv.py
+++ b/megapixels/commands/cv/rois_to_pose.py
@@ -1,18 +1,15 @@
 """
-Crop images to prepare for training
+Converts ROIs to pose: yaw, roll, pitch
 """
 
 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('-f', '--files', 'opt_fp_files', required=True,
+@click.option('-i', '--input', 'opt_fp_files', required=True,
   help='Input ROI CSV')
 @click.option('-r', '--rois', 'opt_fp_rois', required=True,
   help='Input ROI CSV')
@@ -27,9 +24,11 @@ color_filters = {'color': 1, 'gray': 2, 'all': 3}
   help='Slice list of files')
 @click.option('-f', '--force', 'opt_force', is_flag=True,
   help='Force overwrite file')
+@click.option('-d', '--display', 'opt_display', is_flag=True,
+  help='Display image for debugging')
 @click.pass_context
 def cli(ctx, opt_fp_files, opt_fp_rois, opt_dir_media, opt_fp_out, opt_size, 
-  opt_slice, opt_force):
+  opt_slice, opt_force, opt_display):
   """Converts ROIs to pose: roll, yaw, pitch"""
   
   import sys
@@ -58,6 +57,7 @@ def cli(ctx, opt_fp_files, opt_fp_rois, opt_dir_media, opt_fp_out, opt_size,
   face_pose = FacePoseDLIB()
   face_landmarks = LandmarksDLIB()
 
+  # load datra
   df_files = pd.read_csv(opt_fp_files)
   df_rois = pd.read_csv(opt_fp_rois)
 
@@ -70,32 +70,54 @@ def cli(ctx, opt_fp_files, opt_fp_rois, opt_dir_media, opt_fp_out, opt_size,
   
   # -------------------------------------------------
   # process here
-
-  df_roi_groups = df_rois.groupby('index')
-  log.debug('processing {:,} groups'.format(len(df_roi_groups)))
+  df_img_groups = df_rois.groupby('image_index')
+  log.debug('processing {:,} groups'.format(len(df_img_groups)))
 
 
   poses = []
 
-  #for df_roi_group in tqdm(df_roi_groups.itertuples(), total=len(df_roi_groups)):
-  for df_roi_group_idx, df_roi_group in tqdm(df_roi_groups):
+  # iterate 
+  #for df_roi_group_idx, df_roi_group in tqdm(df_roi_groups):
+  for image_index, df_img_group in tqdm(df_img_groups):
     # make fp
-    image_index = df_roi_group.image_index.values[0]
+    #image_index = df_roi_group.image_index.values[0]
     pds_file = df_files.iloc[image_index]
     fp_im = join(opt_dir_media, pds_file.subdir, '{}.{}'.format(pds_file.fn, pds_file.ext))
     im = cv.imread(fp_im)
     # get bbox
-    x = df_roi_group.x.values[0]
-    y = df_roi_group.y.values[0]
-    w = df_roi_group.w.values[0]
-    h = df_roi_group.h.values[0]
+    x = df_img_group.x.values[0]
+    y = df_img_group.y.values[0]
+    w = df_img_group.w.values[0]
+    h = df_img_group.h.values[0]
     dim = im.shape[:2][::-1]
     bbox = BBox.from_xywh(x, y, w, h).to_dim(dim)
     # get pose
     landmarks = face_landmarks.landmarks(im, bbox)
-    pose = face_pose.pose(landmarks, dim)
-    pose['image_index'] = image_index
-    poses.append(pose)
+    pose_data = face_pose.pose(landmarks, dim, project_points=opt_display)
+    pose_degrees = pose_data['degrees']  # only keep the degrees data
+
+    # use the project point data if display flag set
+    if opt_display:
+      pts_im = pose_data['points_image']
+      pts_model = pose_data['points_model']
+      pt_nose = pose_data['point_nose']
+      dst = im.copy()
+      face_pose.draw_pose(dst, pts_im, pts_model, pt_nose)
+      face_pose.draw_degrees(dst, pose_degrees)
+      # display to cv window
+      cv.imshow('', dst)
+      while True:
+        k = cv.waitKey(1) & 0xFF
+        if k == 27 or k == ord('q'):  # ESC
+          cv.destroyAllWindows()
+          sys.exit()
+        elif k != 255:
+          # any key to continue
+          break
+
+    # add image index and append to result CSV data
+    pose_degrees['image_index'] = image_index
+    poses.append(pose_degrees)
 
 
   # save date
diff --git a/megapixels/commands/cv/rois_to_vecs.py b/megapixels/commands/cv/rois_to_vecs.py
new file mode 100644
index 00000000..525f4404
--- /dev/null
+++ b/megapixels/commands/cv/rois_to_vecs.py
@@ -0,0 +1,109 @@
+"""
+Converts ROIs to face vector
+"""
+
+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_files', required=True,
+  help='Input file meta CSV')
+@click.option('-r', '--rois', 'opt_fp_rois', required=True,
+  help='Input ROI CSV')
+@click.option('-m', '--media', 'opt_dir_media', required=True,
+  help='Input media directory')
+@click.option('-o', '--output', 'opt_fp_out', required=True,
+  help='Output CSV')
+@click.option('--size', 'opt_size', 
+  type=(int, int), default=(300, 300),
+  help='Output image size')
+@click.option('-j', '--jitters', 'opt_jitters', default=cfg.DLIB_FACEREC_JITTERS,
+  help='Number of jitters')
+@click.option('-p', '--padding', 'opt_padding', default=cfg.DLIB_FACEREC_PADDING,
+  help='Percentage padding')
+@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
+  help='Slice list of files')
+@click.option('-f', '--force', 'opt_force', is_flag=True,
+  help='Force overwrite file')
+@click.option('-g', '--gpu', 'opt_gpu', default=0,
+  help='GPU index')
+@click.pass_context
+def cli(ctx, opt_fp_files, opt_fp_rois, opt_dir_media, opt_fp_out, opt_size, 
+  opt_slice, opt_force, opt_gpu, opt_jitters, opt_padding):
+  """Converts face ROIs to vectors"""
+  
+  import sys
+  import os
+  from os.path import join
+  from pathlib import Path
+  from glob import glob
+  
+  from tqdm import tqdm
+  import numpy as np
+  import dlib  # must keep a local reference for dlib
+  import cv2 as cv
+  import pandas as pd
+
+  from app.models.bbox import BBox
+  from app.utils import logger_utils, file_utils, im_utils
+  from app.processors import face_recognition
+
+
+  # -------------------------------------------------
+  # init here
+
+  log = logger_utils.Logger.getLogger()
+
+  # init face processors
+  facerec = face_recognition.RecognitionDLIB()
+
+  # load data
+  df_file_meta = pd.read_csv(opt_fp_files)
+  df_rois = pd.read_csv(opt_fp_rois)
+
+  if not opt_force and Path(opt_fp_out).exists():
+    log.error('File exists. Use "-f / --force" to overwite')
+    return
+  
+  if opt_slice:
+    df_rois = df_rois[opt_slice[0]:opt_slice[1]]
+  
+  # -------------------------------------------------
+  # process here
+  df_img_groups = df_rois.groupby('image_index')
+  log.debug('processing {:,} groups'.format(len(df_img_groups)))
+
+  vecs = []
+
+  for image_index, df_img_group in tqdm(df_img_groups):
+    # make fp
+    roi_index = df_img_group.index.values[0]
+    file_meta = df_file_meta.iloc[image_index]  # locate image meta
+    fp_im = join(opt_dir_media, file_meta.subdir, '{}.{}'.format(file_meta.fn, file_meta.ext))
+    im = cv.imread(fp_im)
+    # get bbox
+    x = df_img_group.x.values[0]
+    y = df_img_group.y.values[0]
+    w = df_img_group.w.values[0]
+    h = df_img_group.h.values[0]
+    imw = df_img_group.image_width.values[0]
+    imh = df_img_group.image_height.values[0]
+    dim = im.shape[:2][::-1]
+    # get face vector
+    dim = (imw, imh)
+    bbox_dim = BBox.from_xywh(x, y, w, h).to_dim(dim)  # convert to int real dimensions
+    # compute vec
+    # padding=opt_padding not yet implemented in 19.16 but merged in master
+    vec = facerec.vec(im, bbox_dim, jitters=opt_jitters)
+    vec_str = ','.join([repr(x) for x in vec])  # convert to string for CSV
+    vecs.append( {'roi_index': roi_index, 'image_index': image_index, 'vec': vec_str})
+
+
+  # save date
+  file_utils.mkdirs(opt_fp_out)
+  df = pd.DataFrame.from_dict(vecs)
+  df.index.name = 'index'
+  df.to_csv(opt_fp_out)
\ No newline at end of file