diff options
Diffstat (limited to 'megapixels/commands/cv')
| -rw-r--r-- | megapixels/commands/cv/csv_to_faces_mt.py | 105 | ||||
| -rw-r--r-- | megapixels/commands/cv/embeddings.py | 100 | ||||
| -rw-r--r-- | megapixels/commands/cv/face_pose_to_csv.py | 105 | ||||
| -rw-r--r-- | megapixels/commands/cv/faces_to_csv.py | 6 | ||||
| -rw-r--r-- | megapixels/commands/cv/faces_to_csv_indexed.py | 156 | ||||
| -rw-r--r-- | megapixels/commands/cv/resize.py | 73 |
6 files changed, 518 insertions, 27 deletions
diff --git a/megapixels/commands/cv/csv_to_faces_mt.py b/megapixels/commands/cv/csv_to_faces_mt.py new file mode 100644 index 00000000..64c8b965 --- /dev/null +++ b/megapixels/commands/cv/csv_to_faces_mt.py @@ -0,0 +1,105 @@ +""" +Reads in CSV of ROIs and extracts facial regions with padding +""" + +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 CSV') +@click.option('-m', '--media', 'opt_dir_media', required=True, + help='Input image/video directory') +@click.option('-o', '--output', 'opt_dir_out', required=True, + help='Output directory for extracted ROI images') +@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None), + help='Slice list of files') +@click.option('--padding', 'opt_padding', default=0.25, + help='Facial padding as percentage of face width') +@click.option('--ext', 'opt_ext_out', default='png', type=click.Choice(['jpg', 'png']), + help='Output image type') +@click.option('--min', 'opt_min', default=(60, 60), + help='Minimum original face size') +@click.pass_context +def cli(ctx, opt_fp_in, opt_dir_media, opt_dir_out, opt_slice, + opt_padding, opt_ext_out, opt_min): + """Converts ROIs to images""" + + import os + from os.path import join + from pathlib import Path + from glob import glob + + from tqdm import tqdm + import numpy as np + from PIL import Image, ImageOps, ImageFilter, ImageDraw + import cv2 as cv + import pandas as pd + + from app.utils import logger_utils, file_utils, im_utils + from app.models.bbox import BBox + + # ------------------------------------------------- + # process here + log = logger_utils.Logger.getLogger() + + df_rois = pd.read_csv(opt_fp_in, dtype={'subdir': str, 'fn': str}) + if opt_slice: + df_rois = df_rois[opt_slice[0]:opt_slice[1]] + + log.info('Processing {:,} rows'.format(len(df_rois))) + + file_utils.mkdirs(opt_dir_out) + + df_rois_grouped = df_rois.groupby(['fn']) # group by fn/filename + groups = df_rois_grouped.groups + skipped = [] + + for group in tqdm(groups): + # get image + group_rows = df_rois_grouped.get_group(group) + + row = group_rows.iloc[0] + fp_im = join(opt_dir_media, str(row['subdir']), '{fn}.{ext}'.format(**row)) # TODO change to ext + try: + im = Image.open(fp_im).convert('RGB') + im.verify() + except Exception as e: + log.warn('Could not open: {}'.format(fp_im)) + log.error(e) + continue + + for idx, roi in group_rows.iterrows(): + # get bbox to im dimensions + xywh = [roi['x'], roi['y'], roi['w'] , roi['h']] + bbox = BBox.from_xywh(*xywh) + dim = im.size + bbox_dim = bbox.to_dim(dim) + # expand + opt_padding_px = int(opt_padding * bbox_dim.width) + bbox_dim_exp = bbox_dim.expand_dim(opt_padding_px, dim) + # crop + x1y2 = bbox_dim_exp.pt_tl + bbox_dim_exp.pt_br + im_crop = im.crop(box=x1y2) + + # strip exif, create new image and paste data + im_crop_data = list(im_crop.getdata()) + im_crop_no_exif = Image.new(im_crop.mode, im_crop.size) + im_crop_no_exif.putdata(im_crop_data) + + # save + idx_zpad = file_utils.zpad(idx, zeros=3) + subdir = '' if roi['subdir'] == '.' else '{}_'.format(roi['subdir']) + subdir = subdir.replace('/', '_') + fp_im_out = join(opt_dir_out, '{}{}{}.{}'.format(subdir, roi['fn'], idx_zpad, opt_ext_out)) + # threshold size and save + if im_crop_no_exif.size[0] < opt_min[0] or im_crop_no_exif.size[1] < opt_min[1]: + skipped.append(fp_im_out) + log.info('Face too small: {}, idx: {}'.format(fp_im, idx)) + else: + im_crop_no_exif.save(fp_im_out) + + log.info('Skipped {:,} images'.format(len(skipped))) diff --git a/megapixels/commands/cv/embeddings.py b/megapixels/commands/cv/embeddings.py new file mode 100644 index 00000000..9cb26ae7 --- /dev/null +++ b/megapixels/commands/cv/embeddings.py @@ -0,0 +1,100 @@ +""" +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/face_pose_to_csv.py b/megapixels/commands/cv/face_pose_to_csv.py new file mode 100644 index 00000000..ca7489de --- /dev/null +++ b/megapixels/commands/cv/face_pose_to_csv.py @@ -0,0 +1,105 @@ +""" +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('-f', '--files', 'opt_fp_files', required=True, + help='Input ROI 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('--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.pass_context +def cli(ctx, opt_fp_files, opt_fp_rois, opt_dir_media, opt_fp_out, opt_size, + opt_slice, opt_force): + """Converts ROIs to pose: roll, yaw, pitch""" + + 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.face_landmarks import LandmarksDLIB + from app.processors.face_pose import FacePoseDLIB + + # ------------------------------------------------- + # init here + + log = logger_utils.Logger.getLogger() + + # init face processors + face_pose = FacePoseDLIB() + face_landmarks = LandmarksDLIB() + + df_files = 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_roi_groups = df_rois.groupby('index') + log.debug('processing {:,} groups'.format(len(df_roi_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): + # make fp + 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] + 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) + + + # save date + file_utils.mkdirs(opt_fp_out) + df = pd.DataFrame.from_dict(poses) + df.index.name = 'index' + df.to_csv(opt_fp_out)
\ No newline at end of file diff --git a/megapixels/commands/cv/faces_to_csv.py b/megapixels/commands/cv/faces_to_csv.py index 07226c31..1fd47571 100644 --- a/megapixels/commands/cv/faces_to_csv.py +++ b/megapixels/commands/cv/faces_to_csv.py @@ -30,7 +30,7 @@ color_filters = {'color': 1, 'gray': 2, 'all': 3} help='GPU index') @click.option('--conf', 'opt_conf_thresh', default=0.85, type=click.FloatRange(0,1), help='Confidence minimum threshold') -@click.option('--pyramids', 'opt_pyramids', default=0, type=click.IntRange(0,4), +@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') @@ -78,6 +78,8 @@ def cli(ctx, opt_dirs_in, opt_fp_out, opt_ext, opt_size, opt_detector_type, detector = face_detector.DetectorDLIBCNN(opt_gpu) elif opt_detector_type == types.FaceDetectNet.DLIB_HOG: detector = face_detector.DetectorDLIBHOG() + elif opt_detector_type == types.FaceDetectNet.MTCNN: + detector = face_detector.DetectorMTCNN() elif opt_detector_type == types.FaceDetectNet.HAAR: log.error('{} not yet implemented'.format(opt_detector_type.name)) return @@ -129,6 +131,8 @@ def cli(ctx, opt_dirs_in, opt_fp_out, opt_ext, opt_size, opt_detector_type, subdir = str(fpp_im.parent.relative_to(opt_dir_in)) for bbox in bboxes: + # log.debug('is square: {}'.format(bbox.w == bbox.h)) + nw,nh = int(bbox.w * im.shape[1]), int(bbox.h * im.shape[0]) roi = { 'fn': fpp_im.stem, 'ext': fpp_im.suffix.replace('.',''), diff --git a/megapixels/commands/cv/faces_to_csv_indexed.py b/megapixels/commands/cv/faces_to_csv_indexed.py new file mode 100644 index 00000000..ef958f89 --- /dev/null +++ b/megapixels/commands/cv/faces_to_csv_indexed.py @@ -0,0 +1,156 @@ +""" +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', required=True, + help='Input CSV (eg image_files.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('-t', '--detector-type', 'opt_detector_type', + type=cfg.FaceDetectNetVar, + default=click_utils.get_default(types.FaceDetectNet.DLIB_CNN), + 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='all', + help='Filter to keep color or grayscale images (color = keep color') +@click.option('--largest', 'opt_largest', is_flag=True, + help='Only keep largest face') +@click.pass_context +def cli(ctx, opt_fp_in, opt_dir_media, opt_fp_out, opt_size, opt_detector_type, + opt_gpu, opt_conf_thresh, opt_pyramids, opt_slice, opt_display, opt_force, opt_color_filter, + opt_largest): + """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 + from app.processors import face_detector + + # ------------------------------------------------- + # 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 + + if opt_detector_type == types.FaceDetectNet.CVDNN: + detector = face_detector.DetectorCVDNN() + elif opt_detector_type == types.FaceDetectNet.DLIB_CNN: + detector = face_detector.DetectorDLIBCNN(opt_gpu) + elif opt_detector_type == types.FaceDetectNet.DLIB_HOG: + detector = face_detector.DetectorDLIBHOG() + elif opt_detector_type == types.FaceDetectNet.MTCNN: + detector = face_detector.DetectorMTCNN() + elif opt_detector_type == types.FaceDetectNet.HAAR: + log.error('{} not yet implemented'.format(opt_detector_type.name)) + return + + + # ------------------------------------------------- + # process here + color_filter = color_filters[opt_color_filter] + + # get list of files to process + df_files = pd.read_csv(opt_fp_in).set_index('index') + + if opt_slice: + df_files = df_files[opt_slice[0]:opt_slice[1]] + log.debug('processing {:,} files'.format(len(df_files))) + + + 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)) + im = cv.imread(fp_im) + + # 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 = detector.detect(im, opt_size=opt_size, opt_pyramids=opt_pyramids, opt_largest=opt_largest) + except Exception as e: + log.error('could not detect: {}'.format(fp_im)) + log.error('{}'.format(e)) + continue + + for bbox in bboxes: + roi = { + 'image_index': int(df_file.Index), + 'x': bbox.x, + 'y': bbox.y, + 'w': bbox.w, + 'h': bbox.h, + 'image_width': im.shape[1], + 'image_height': im.shape[0]} + data.append(roi) + + # debug display + if opt_display and len(bboxes): + bbox_dim = bbox.to_dim(im.shape[:2][::-1]) # w,h + im_md = im_utils.resize(im, width=min(1200, opt_size[0])) + for bbox in bboxes: + bbox_dim = bbox.to_dim(im_md.shape[:2][::-1]) + cv.rectangle(im_md, bbox_dim.pt_tl, bbox_dim.pt_br, (0,255,0), 3) + cv.imshow('', im_md) + 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 + + # save date + file_utils.mkdirs(opt_fp_out) + df = pd.DataFrame.from_dict(data) + df.index.name = 'index' + df.to_csv(opt_fp_out)
\ No newline at end of file diff --git a/megapixels/commands/cv/resize.py b/megapixels/commands/cv/resize.py index f535c8b6..dcd621b3 100644 --- a/megapixels/commands/cv/resize.py +++ b/megapixels/commands/cv/resize.py @@ -62,9 +62,11 @@ centerings = { help='Crop focal point') @click.option('--slice', 'opt_slice', type=(int, int), default=(None, None), help='Slice the input list') +@click.option('-t', '--threads', 'opt_threads', default=8, + help='Number of threads') @click.pass_context def cli(ctx, opt_dir_in, opt_dir_out, opt_glob_ext, opt_size, opt_scale_method, - opt_equalize, opt_sharpen, opt_center, opt_slice): + opt_equalize, opt_sharpen, opt_center, opt_slice, opt_threads): """Crop, mirror images""" import os @@ -72,6 +74,8 @@ def cli(ctx, opt_dir_in, opt_dir_out, opt_glob_ext, opt_size, opt_scale_method, from pathlib import Path from glob import glob from tqdm import tqdm + from multiprocessing.dummy import Pool as ThreadPool + from functools import partial from app.utils import logger_utils, file_utils, im_utils @@ -80,46 +84,63 @@ def cli(ctx, opt_dir_in, opt_dir_out, opt_glob_ext, opt_size, opt_scale_method, log = logger_utils.Logger.getLogger() - centering = centerings[opt_center] # ------------------------------------------------- # process here + def pool_resize(fp_im, opt_size, scale_method, centering): + # Threaded image resize function + try: + pbar.update(1) + try: + im = Image.open(fp_im).convert('RGB') + im.verify() + except Exception as e: + log.warn('Could not open: {}'.format(fp_im)) + log.error(e) + return False + + im = ImageOps.fit(im, opt_size, method=scale_method, centering=centering) + + if opt_equalize: + im_np = im_utils.pil2np(im) + im_np_eq = eq_hist_yuv(im_np) + im_np = cv.addWeighted(im_np_eq, 0.35, im_np, 0.65, 0) + im = im_utils.np2pil(im_np) + + if opt_sharpen: + im = im.filter(ImageFilter.UnsharpMask) + + fp_out = join(opt_dir_out, Path(fp_im).name) + im.save(fp_out) + return True + except: + return False + + centering = centerings[opt_center] + scale_method = methods[opt_scale_method] + # get list of files to process fp_ims = glob(join(opt_dir_in, '*.{}'.format(opt_glob_ext))) if opt_slice: fp_ims = fp_ims[opt_slice[0]:opt_slice[1]] log.info('processing {:,} files'.format(len(fp_ims))) - # set scale method - scale_method = methods[opt_scale_method] - + # ensure output dir exists file_utils.mkdirs(opt_dir_out) - # resize and save images - for fp_im in tqdm(fp_ims): - try: - im = Image.open(fp_im).convert('RGB') - im.verify() - except Exception as e: - log.warn('Could not open: {}'.format(fp_im)) - log.error(e) - continue - - im = ImageOps.fit(im, opt_size, method=scale_method, centering=centering) + # setup multithreading + pbar = tqdm(total=len(fp_ims)) + pool_resize = partial(pool_resize, opt_size=opt_size, scale_method=scale_method, centering=centering) + #result_list = pool.map(prod_x, data_list) + pool = ThreadPool(opt_threads) + with tqdm(total=len(fp_ims)) as pbar: + results = pool.map(pool_resize, fp_ims) + pbar.close() - if opt_equalize: - im_np = im_utils.pil2np(im) - im_np_eq = eq_hist_yuv(im_np) - im_np = cv.addWeighted(im_np_eq, 0.35, im_np, 0.65, 0) - im = im_utils.np2pil(im_np) + log.info('Resized: {} / {} images'.format(results.count(True), len(fp_ims))) - if opt_sharpen: - im = im.filter(ImageFilter.UnsharpMask) - - fp_out = join(opt_dir_out, Path(fp_im).name) - im.save(fp_out) def eq_hist_yuv(im): |