diff options
Diffstat (limited to 'megapixels')
| -rw-r--r-- | megapixels/app/models/bbox.py | 14 | ||||
| -rw-r--r-- | megapixels/app/models/dataset.py | 14 | ||||
| -rw-r--r-- | megapixels/app/site/parser.py | 30 | ||||
| -rw-r--r-- | megapixels/app/utils/draw_utils.py | 42 | ||||
| -rw-r--r-- | megapixels/app/utils/im_utils.py | 14 | ||||
| -rw-r--r-- | megapixels/commands/datasets/megaface_age_from_orig.py | 62 | ||||
| -rw-r--r-- | megapixels/commands/demo/face_search.py | 2 | ||||
| -rw-r--r-- | megapixels/commands/processor/_old_files_to_face_rois.py | 2 | ||||
| -rw-r--r-- | megapixels/commands/processor/face_roi_from_annos.py | 187 | ||||
| -rw-r--r-- | megapixels/commands/processor/file_record.py (renamed from megapixels/commands/datasets/file_record.py) | 2 | ||||
| -rw-r--r-- | megapixels/commands/site/age_gender_to_site.py | 100 |
11 files changed, 451 insertions, 18 deletions
diff --git a/megapixels/app/models/bbox.py b/megapixels/app/models/bbox.py index 8ecc8971..c840ea1b 100644 --- a/megapixels/app/models/bbox.py +++ b/megapixels/app/models/bbox.py @@ -207,11 +207,21 @@ class BBox: # ----------------------------------------------------------------- # Convert to - def to_square(self, bounds): + def to_square(self): '''Forces bbox to square dimensions - :param bounds: (int, int) w, h of the image :returns (BBox) in square ratio ''' + if self._width > self._height: + delta = (self._width - self._height) / 2 + self._y1 -= delta + self._y2 += delta + elif self._height > self._width: + delta = (self._height - self._width) / 2 + self._x1 -= delta + self._x2 += delta + return BBox(self._x1, self._y1, self._x2, self._y2) + + def to_dim(self, dim): """scale is (w, h) is tuple of dimensions""" diff --git a/megapixels/app/models/dataset.py b/megapixels/app/models/dataset.py index a7227a70..c908da1b 100644 --- a/megapixels/app/models/dataset.py +++ b/megapixels/app/models/dataset.py @@ -152,6 +152,8 @@ class Dataset: image_records = [] # list of image matches w/identity if available # find most similar feature vectors indexes #match_idxs = self.similar(query_vec, n_results, threshold) + + # TODO: add cosine similarity sim_scores = np.linalg.norm(np.array([query_vec]) - np.array(self._face_vectors), axis=1) match_idxs = np.argpartition(sim_scores, range(n_results))[:n_results] @@ -180,7 +182,17 @@ class Dataset: s3_url = self.data_store_s3.face(ds_record.uuid) bbox_norm = BBox.from_xywh_norm_dim(ds_roi.x, ds_roi.y, ds_roi.w, ds_roi.h, dim) self.log.debug(f'bbox_norm: {bbox_norm}') - score = sim_scores[match_idx] + self.log.debug(f'match_idx: {match_idx}, record_idx: {record_idx}, roi_index: {roi_index}, len sim_scores: {len(sim_scores)}') + try: + score = sim_scores[match_idx] + except Exception as e: + self.log.error(e) + try: + score = sim_scores[record_idx] + except Exception as e: + self.log.error(e) + + if types.Metadata.IDENTITY in self._metadata.keys(): ds_id = df_identity.loc[df_identity['identity_key'] == ds_record.identity_key].iloc[0] diff --git a/megapixels/app/site/parser.py b/megapixels/app/site/parser.py index 3700efd1..6ab8c700 100644 --- a/megapixels/app/site/parser.py +++ b/megapixels/app/site/parser.py @@ -163,6 +163,35 @@ def intro_section(metadata, s3_path): """ section = "<section class='intro_section' style='background-image: url({})'>".format(s3_path + metadata['image']) + # section += "<div class='inner'>" + + # parts = [] + # if 'desc' in metadata: + # desc = metadata['desc'] + # # colorize the first instance of the database name in the header + # if 'color' in metadata and metadata['title'] in desc: + # desc = desc.replace(metadata['title'], "<span style='color: {}'>{}</span>".format(metadata['color'], metadata['title']), 1) + # section += "<div class='hero_desc'><span class='bgpad'>{}</span></div>".format(desc, desc) + + # if 'subdesc' in metadata: + # subdesc = markdown(metadata['subdesc']).replace('<p>', '').replace('</p>', '') + # section += "<div class='hero_subdesc'><span class='bgpad'>{}</span></div>".format(subdesc, subdesc) + + # section += "</div>" + section += "</section>" + + if 'caption' in metadata: + section += "<section><div class='image'><div class='intro-caption caption'>{}</div></div></section>".format(metadata['caption']) + + return section + + +def intro_section_v1(metadata, s3_path): + """ + Build the intro section for datasets + """ + + section = "<section class='intro_section' style='background-image: url({})'>".format(s3_path + metadata['image']) section += "<div class='inner'>" parts = [] @@ -185,7 +214,6 @@ def intro_section(metadata, s3_path): return section - def fix_images(lines, s3_path): """ do our own transformation of the markdown around images to handle wide images etc diff --git a/megapixels/app/utils/draw_utils.py b/megapixels/app/utils/draw_utils.py index 7044a62f..1836768b 100644 --- a/megapixels/app/utils/draw_utils.py +++ b/megapixels/app/utils/draw_utils.py @@ -3,8 +3,10 @@ from math import sqrt import numpy as np import cv2 as cv +import PIL +from PIL import ImageDraw -from app.utils import logger_utils +from app.utils import logger_utils, im_utils log = logger_utils.Logger.getLogger() @@ -118,6 +120,22 @@ def draw_landmarks2D(im, points_norm, radius=3, color=(0,255,0)): cv.circle(im_dst, pt, radius, color, -1, cv.LINE_AA) return im_dst +def draw_landmarks2D_pil(im, points_norm, radius=3, color=(0,255,0)): + '''Draws facial landmarks, either 5pt or 68pt + ''' + im_pil = im_utils.ensure_pil(im_utils.bgr2rgb(im)) + draw = ImageDraw.Draw(im_pil) + dim = im.shape[:2][::-1] + for x,y in points_norm: + x1, y1 = (int(x*dim[0]), int(y*dim[1])) + xyxy = (x1, y1, x1+radius, y1+radius) + draw.ellipse(xyxy, fill='white') + del draw + im_dst = im_utils.ensure_np(im_pil) + im_dst = im_utils.rgb2bgr(im_dst) + return im_dst + + def draw_landmarks3D(im, points, radius=3, color=(0,255,0)): '''Draws 3D facial landmarks ''' @@ -126,12 +144,26 @@ def draw_landmarks3D(im, points, radius=3, color=(0,255,0)): cv.circle(im_dst, (x,y), radius, color, -1, cv.LINE_AA) return im_dst -def draw_bbox(im, bbox_norm, color=(0,255,0), stroke_weight=2): +def draw_bbox(im, bboxes_norm, color=(0,255,0), stroke_weight=2): '''Draws BBox onto cv image + :param color: RGB value ''' - im_dst = im.copy() - bbox_dim = bbox_norm.to_dim(im.shape[:2][::-1]) - cv.rectangle(im_dst, bbox_dim.pt_tl, bbox_dim.pt_br, color, stroke_weight, cv.LINE_AA) + #im_dst = im.copy() + if not type(bboxes_norm) == list: + bboxes_norm = [bboxes_norm] + + im_pil = im_utils.ensure_pil(im_utils.bgr2rgb(im)) + im_pil_draw = ImageDraw.ImageDraw(im_pil) + + for bbox_norm in bboxes_norm: + bbox_dim = bbox_norm.to_dim(im.shape[:2][::-1]) + #cv.rectangle(im_dst, bbox_dim.pt_tl, bbox_dim.pt_br, color, stroke_weight, cv.LINE_AA) + xyxy = (bbox_dim.pt_tl, bbox_dim.pt_br) + im_pil_draw.rectangle(xyxy, outline=color, width=stroke_weight) + # draw.rectangle([x1, y1, x2, y2], outline=, width=3) + im_dst = im_utils.ensure_np(im_pil) + im_dst = im_utils.rgb2bgr(im_dst) + del im_pil_draw return im_dst def draw_pose(im, pt_nose, image_pts): diff --git a/megapixels/app/utils/im_utils.py b/megapixels/app/utils/im_utils.py index d36c1c32..670d5168 100644 --- a/megapixels/app/utils/im_utils.py +++ b/megapixels/app/utils/im_utils.py @@ -11,11 +11,6 @@ from skimage import feature import imutils import time import numpy as np -import torch -import torch.nn as nn -import torchvision.models as models -import torchvision.transforms as transforms -from torch.autograd import Variable from sklearn.metrics.pairwise import cosine_similarity import datetime @@ -293,6 +288,13 @@ def bgr2rgb(im): """ return cv.cvtColor(im,cv.COLOR_BGR2RGB) +def rgb2bgr(im): + """Wrapper for cv2.cvtColor transform + :param im: Numpy.ndarray (BGR) + :returns: Numpy.ndarray (RGB) + """ + return cv.cvtColor(im,cv.COLOR_RGB2BGR) + def compute_laplacian(im): # below 100 is usually blurry return cv.Laplacian(im, cv.CV_64F).var() @@ -329,7 +331,7 @@ def normalizedGraylevelVariance(img): s = stdev[0]**2 / mean[0] return s[0] -def compute_if_blank(im,width=100,sigma=0,thresh_canny=.1,thresh_mean=4,mask=None): +def is_blank(im,width=100,sigma=0,thresh_canny=.1,thresh_mean=4,mask=None): # im is graysacale np #im = imutils.resize(im,width=width) #mask = imutils.resize(mask,width=width) diff --git a/megapixels/commands/datasets/megaface_age_from_orig.py b/megapixels/commands/datasets/megaface_age_from_orig.py new file mode 100644 index 00000000..489bebf3 --- /dev/null +++ b/megapixels/commands/datasets/megaface_age_from_orig.py @@ -0,0 +1,62 @@ +import click + +@click.command() +@click.option('-i', '--input', 'opt_fp_in', required=True, + help='Input path to metadata directory') +@click.option('-o', '--output', 'opt_fp_out', + help='Output path to age CSV') +@click.pass_context +def cli(ctx, opt_fp_in, opt_fp_out): + """Creates CSV of MegaFace ages from original BBoxes""" + + import os + from os.path import join + from pathlib import Path + from glob import glob + + import dlib + import pandas as pd + from tqdm import tqdm + + from app.settings import types + from app.utils import click_utils + from app.settings import app_cfg + + from PIL import Image, ImageOps, ImageFilter + from app.utils import file_utils, im_utils, logger_utils + + log = logger_utils.Logger.getLogger() + + # ------------------------------------------------- + # process + fp_im_dirs = glob(join(opt_fp_in, '**/'), recursive=True) + + log.info('Found {} directories'.format(len(fp_im_dirs))) + + identities = {} + + for fp_im_dir in tqdm(fp_im_dirs): + # 1234567@N05_identity_1 + try: + dir_id_name = Path(fp_im_dir).name + nsid = dir_id_name.split('_')[0] + identity_num = dir_id_name.split('_')[2] + id_key = '{}_{}'.format(nsid, identity_num) + num_images = len(glob(join(fp_im_dir, '*.jpg'))) + if not id_key in identities.keys(): + identities[id_key] = {'nsid': nsid, 'identity': identity_num, 'images': num_images} + else: + identities[id_key]['images'] += num_images + except Exception as e: + continue + + # convert to dict + identities_list = [v for k, v in identities.items()] + df = pd.DataFrame.from_dict(identities_list) + + file_utils.mkdirs(opt_fp_out) + + log.info('Wrote {} lines to {}'.format(len(df), opt_fp_out)) + df.to_csv(opt_fp_out, index=False) + + diff --git a/megapixels/commands/demo/face_search.py b/megapixels/commands/demo/face_search.py index 4c7036f4..5218d501 100644 --- a/megapixels/commands/demo/face_search.py +++ b/megapixels/commands/demo/face_search.py @@ -10,7 +10,7 @@ log = Logger.getLogger() @click.command() @click.option('-i', '--input', 'opt_fp_in', required=True, - help='File to lookup') + help='Face image file to lookup') @click.option('--data_store', 'opt_data_store', type=cfg.DataStoreVar, default=click_utils.get_default(types.DataStore.HDD), diff --git a/megapixels/commands/processor/_old_files_to_face_rois.py b/megapixels/commands/processor/_old_files_to_face_rois.py index 895f4718..d92cbd74 100644 --- a/megapixels/commands/processor/_old_files_to_face_rois.py +++ b/megapixels/commands/processor/_old_files_to_face_rois.py @@ -1,4 +1,4 @@ - """ +""" Crop images to prepare for training """ diff --git a/megapixels/commands/processor/face_roi_from_annos.py b/megapixels/commands/processor/face_roi_from_annos.py new file mode 100644 index 00000000..fc933049 --- /dev/null +++ b/megapixels/commands/processor/face_roi_from_annos.py @@ -0,0 +1,187 @@ +""" +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('-d', '--detector', 'opt_detector_type', + type=cfg.FaceDetectNetVar, + default=click_utils.get_default(types.FaceDetectNet.CVDNN), + help=click_utils.show_help(types.FaceDetectNet)) +@click.option('-g', '--gpu', 'opt_gpu', default=0, + help='GPU index') +@click.option('--conf', 'opt_conf_thresh', default=0.85, type=click.FloatRange(0,1), + help='Confidence minimum threshold') +@click.option('-p', '--pyramids', 'opt_pyramids', default=0, type=click.IntRange(0,4), + help='Number pyramids to upscale for DLIB detectors') +@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.option('--color', 'opt_color_filter', + type=click.Choice(color_filters.keys()), default='color', + help='Filter to keep color or grayscale images (color = keep color') +@click.option('--keep', 'opt_largest', type=click.Choice(['largest', 'all']), default='largest', + help='Only keep largest face') +@click.option('--zone', 'opt_zone', default=(0.0, 0.0), type=(float, float), + help='Face center must be located within zone region (0.5 = half width/height)') +@click.pass_context +def cli(ctx, opt_fp_in, opt_dir_media, opt_fp_out, opt_data_store, opt_dataset, opt_size, opt_detector_type, + opt_gpu, opt_conf_thresh, opt_pyramids, opt_slice, opt_display, opt_force, opt_color_filter, + opt_largest, opt_zone): + """Converts frames with faces to CSV of ROIs""" + + 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.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils + from app.processors import face_detector + from app.models.data_store import DataStore + + # ------------------------------------------------- + # init here + + log = logger_utils.Logger.getLogger() + + # set data_store + data_store = DataStore(opt_data_store, opt_dataset) + + # get filepath out + fp_out = data_store.metadata(types.Metadata.FACE_ROI) if opt_fp_out is None else opt_fp_out + if not opt_force and Path(fp_out).exists(): + log.error('File exists. Use "-f / --force" to overwite') + return + + # set detector + if opt_detector_type == types.FaceDetectNet.CVDNN: + detector = face_detector.DetectorCVDNN() + elif opt_detector_type == types.FaceDetectNet.DLIB_CNN: + detector = face_detector.DetectorDLIBCNN(gpu=opt_gpu) + elif opt_detector_type == types.FaceDetectNet.DLIB_HOG: + detector = face_detector.DetectorDLIBHOG() + elif opt_detector_type == types.FaceDetectNet.MTCNN_TF: + detector = face_detector.DetectorMTCNN_TF(gpu=opt_gpu) + elif opt_detector_type == types.FaceDetectNet.HAAR: + log.error('{} not yet implemented'.format(opt_detector_type.name)) + return + + + # get list of files to process + fp_record = data_store.metadata(types.Metadata.FILE_RECORD) if opt_fp_in is None else opt_fp_in + df_record = pd.read_csv(fp_record, dtype=cfg.FILE_RECORD_DTYPES).set_index('index') + if opt_slice: + df_record = df_record[opt_slice[0]:opt_slice[1]] + log.debug('processing {:,} files'.format(len(df_record))) + + # filter out grayscale + color_filter = color_filters[opt_color_filter] + # set largest flag, to keep all or only largest + opt_largest = (opt_largest == 'largest') + + data = [] + skipped_files = [] + processed_files = [] + + for df_record in tqdm(df_record.itertuples(), total=len(df_record)): + fp_im = data_store.face(str(df_record.subdir), str(df_record.fn), str(df_record.ext)) + try: + im = cv.imread(fp_im) + im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1]) + except Exception as e: + log.debug(f'could not read: {fp_im}') + return + # filter out color or grayscale iamges + if color_filter != color_filters['all']: + try: + is_gray = im_utils.is_grayscale(im) + if is_gray and color_filter != color_filters['gray']: + log.debug('Skipping grayscale image: {}'.format(fp_im)) + continue + except Exception as e: + log.error('Could not check grayscale: {}'.format(fp_im)) + continue + + try: + bboxes_norm = detector.detect(im_resized, pyramids=opt_pyramids, largest=opt_largest, + zone=opt_zone, conf_thresh=opt_conf_thresh) + except Exception as e: + log.error('could not detect: {}'.format(fp_im)) + log.error('{}'.format(e)) + continue + + if len(bboxes_norm) == 0: + skipped_files.append(fp_im) + log.warn(f'no faces in: {fp_im}') + log.warn(f'skipped: {len(skipped_files)}. found:{len(processed_files)} files') + else: + processed_files.append(fp_im) + for bbox in bboxes_norm: + roi = { + 'record_index': int(df_record.Index), + 'x': bbox.x, + 'y': bbox.y, + 'w': bbox.w, + 'h': bbox.h + } + data.append(roi) + + # if display optined + if opt_display and len(bboxes_norm): + # draw each box + for bbox_norm in bboxes_norm: + dim = im_resized.shape[:2][::-1] + bbox_dim = bbox.to_dim(dim) + if dim[0] > 1000: + im_resized = im_utils.resize(im_resized, width=1000) + im_resized = draw_utils.draw_bbox(im_resized, bbox_norm) + + # display and wait + cv.imshow('', im_resized) + display_utils.handle_keyboard() + + # create DataFrame and save to CSV + file_utils.mkdirs(fp_out) + df = pd.DataFrame.from_dict(data) + df.index.name = 'index' + df.to_csv(fp_out) + + # save script + file_utils.write_text(' '.join(sys.argv), '{}.sh'.format(fp_out))
\ No newline at end of file diff --git a/megapixels/commands/datasets/file_record.py b/megapixels/commands/processor/file_record.py index 41a5df28..6403c768 100644 --- a/megapixels/commands/datasets/file_record.py +++ b/megapixels/commands/processor/file_record.py @@ -78,7 +78,7 @@ def cli(ctx, opt_fp_in, opt_fp_out, opt_dataset, opt_data_store, opt_dir_media, fp_out = data_store.metadata(types.Metadata.FILE_RECORD) if opt_fp_out is None else opt_fp_out # exit if exists if not opt_force and Path(fp_out).exists(): - log.error('File exists. Use "-f / --force" to overwite') + log.error(f'File {fp_out} exists. Use "-f / --force" to overwite') return # ---------------------------------------------------------------- diff --git a/megapixels/commands/site/age_gender_to_site.py b/megapixels/commands/site/age_gender_to_site.py new file mode 100644 index 00000000..3ad24a8d --- /dev/null +++ b/megapixels/commands/site/age_gender_to_site.py @@ -0,0 +1,100 @@ +""" + +""" + +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, + 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('-f', '--force', 'opt_force', is_flag=True, + help='Force overwrite file') +@click.pass_context +def cli(ctx, opt_fp_in, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, opt_force): + """Converts age/gender to CSV for pie chartgs""" + + 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 cv2 as cv + import pandas as pd + + from app.utils import logger_utils + from app.models.data_store import DataStore + + # ------------------------------------------------------------------------- + # init here + + log = logger_utils.Logger.getLogger() + + # init filepaths + data_store = DataStore(opt_data_store, opt_dataset) + # set file output path + metadata_type = types.Metadata.FACE_ATTRIBUTES + fp_in = data_store.metadata(metadata_type) if opt_fp_out is None else opt_fp_in + dk = opt_dataset.name.lower() + log.debug(f'dk: {dk}') + fp_out_age = f'../site/content/pages/datasets/{dk}/assets/age.csv' + fp_out_gender = f'../site/content/pages/datasets/{dk}/assets/gender.csv' + + if not opt_force and (Path(fp_out_age).exists() or Path(fp_out_gender).exists()): + log.error('File exists. Use "-f / --force" to overwite') + return + + # ------------------------------------------------------------------------- + # Age + + df = pd.read_csv(fp_in) + + results = [] + brackets = [(0, 12), (13, 18), (19,24), (25, 34), (35, 44), (45, 54), (55, 64), (64, 75), (75, 100)] + df_age = df['age_real'] + + for a1, a2 in brackets: + n = len(df_age.loc[((df_age >= a1) & (df_age <= a2))]) + results.append({'age': f'{a1} - {a2}', 'faces': n}) + + df_out = pd.DataFrame.from_dict(results) + df_out = df_out[['age','faces']] + df_out.to_csv(fp_out_age, index=False) + + # Gender + results = [] + + df_f = df['f'] + nm = len(df_f.loc[((df_f < 0.33))]) + nnb = len(df_f.loc[((df_f >= 0.33) & (df_f <= 0.66))]) + nf = len(df_f.loc[((df_f > 0.66))]) + + results = [] + results.append({'gender': 'Male', 'faces':nm}) + results.append({'gender': 'Female', 'faces': nf}) + results.append({'gender': 'They', 'faces': nnb}) + + df_out = pd.DataFrame.from_dict(results) + df_out = df_out[['gender','faces']] + df_out.to_csv(fp_out_gender, index=False)
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