diff options
Diffstat (limited to 'megapixels')
| -rw-r--r-- | megapixels/app/processors/person_detector.py | 65 | ||||
| -rw-r--r-- | megapixels/app/settings/app_cfg.py | 5 | ||||
| -rw-r--r-- | megapixels/app/settings/types.py | 3 | ||||
| -rw-r--r-- | megapixels/app/utils/display_utils.py | 7 | ||||
| -rw-r--r-- | megapixels/app/utils/identity_utils.py | 19 | ||||
| -rw-r--r-- | megapixels/commands/datasets/citations_to_csv.py | 35 | ||||
| -rw-r--r-- | megapixels/commands/datasets/pull_spreadsheet.py | 25 | ||||
| -rw-r--r-- | megapixels/commands/processor/body_roi_video.py | 148 |
8 files changed, 283 insertions, 24 deletions
diff --git a/megapixels/app/processors/person_detector.py b/megapixels/app/processors/person_detector.py new file mode 100644 index 00000000..6daa8c40 --- /dev/null +++ b/megapixels/app/processors/person_detector.py @@ -0,0 +1,65 @@ +import sys +import os +from os.path import join +from pathlib import Path + +import cv2 as cv +import numpy as np +import imutils +import operator + +from app.utils import im_utils, logger_utils +from app.models.bbox import BBox +from app.settings import app_cfg as cfg +from app.settings import types + + +class DetectorCVDNN: + + # MobileNet SSD + dnn_scale = 0.007843 # fixed + dnn_mean = (127.5, 127.5, 127.5) # fixed + dnn_crop = False # crop or force resize + blob_size = (300, 300) + conf = 0.95 + + # detect + CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", + "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", + "dog", "horse", "motorbike", "person", "pottedplant", "sheep", + "sofa", "train", "tvmonitor"] + + def __init__(self): + self.log = logger_utils.Logger.getLogger() + fp_prototxt = join(cfg.DIR_MODELS_CAFFE, 'mobilenet_ssd', 'MobileNetSSD_deploy.prototxt') + fp_model = join(cfg.DIR_MODELS_CAFFE, 'mobilenet_ssd', 'MobileNetSSD_deploy.caffemodel') + self.net = cv.dnn.readNet(fp_prototxt, fp_model) + self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV) + self.net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU) + + def detect(self, im, conf=None, largest=False, pyramids=None, zone=False, blob_size=None): + """Detects bodies and returns (list) of (BBox)""" + conf = self.conf if conf is None else conf + blob_size = self.blob_size if blob_size is None else blob_size + im = cv.resize(im, blob_size) + dim = im.shape[:2][::-1] + blob = cv.dnn.blobFromImage(im, self.dnn_scale, dim, self.dnn_mean) + self.net.setInput(blob) + net_outputs = self.net.forward() + + bboxes = [] + for i in range(0, net_outputs.shape[2]): + det_conf = float(net_outputs[0, 0, i, 2]) + bounds = np.array(net_outputs[0, 0, i, 3:7]) # bug: ensure all x,y within 1.0 ? + if det_conf > conf and np.all(bounds < 1): + idx = int(net_outputs[0, 0, i, 1]) + if self.CLASSES[idx] == "person": + rect_norm = net_outputs[0, 0, i, 3:7] + bboxes.append(BBox(*rect_norm)) + + if largest and len(bboxes) > 1: + # only keep largest + bboxes.sort(key=operator.attrgetter('area'), reverse=True) + bboxes = [bboxes[0]] + + return bboxes
\ No newline at end of file diff --git a/megapixels/app/settings/app_cfg.py b/megapixels/app/settings/app_cfg.py index 1eed1a41..98d36b5f 100644 --- a/megapixels/app/settings/app_cfg.py +++ b/megapixels/app/settings/app_cfg.py @@ -19,6 +19,7 @@ LogLevelVar = click_utils.ParamVar(types.LogLevel) MetadataVar = click_utils.ParamVar(types.Metadata) DatasetVar = click_utils.ParamVar(types.Dataset) DataStoreVar = click_utils.ParamVar(types.DataStore) + # Face analysis HaarCascadeVar = click_utils.ParamVar(types.HaarCascade) FaceDetectNetVar = click_utils.ParamVar(types.FaceDetectNet) @@ -27,6 +28,10 @@ FaceLandmark2D_5Var = click_utils.ParamVar(types.FaceLandmark2D_5) FaceLandmark2D_68Var = click_utils.ParamVar(types.FaceLandmark2D_68) FaceLandmark3D_68Var = click_utils.ParamVar(types.FaceLandmark3D_68) +# Person/Body detector +BodyDetectNetVar = click_utils.ParamVar(types.BodyDetectNet) + + # base path DIR_SELF = os.path.dirname(os.path.realpath(__file__)) DIR_ROOT = Path(DIR_SELF).parent.parent.parent diff --git a/megapixels/app/settings/types.py b/megapixels/app/settings/types.py index 3d7e96c0..2609ece7 100644 --- a/megapixels/app/settings/types.py +++ b/megapixels/app/settings/types.py @@ -59,6 +59,9 @@ class FaceDetectNet(Enum): """Scene text detector networks""" HAAR, DLIB_CNN, DLIB_HOG, CVDNN, MTCNN_TF, MTCNN_PT, MTCNN_CAFFE = range(7) +class BodyDetectNet(Enum): + CVDNN = range(1) + class FaceExtractor(Enum): """Type of face recognition feature extractor""" # TODO deprecate DLIB resnet and use only CVDNN Caffe models diff --git a/megapixels/app/utils/display_utils.py b/megapixels/app/utils/display_utils.py index 43328ae9..8e265ae7 100644 --- a/megapixels/app/utils/display_utils.py +++ b/megapixels/app/utils/display_utils.py @@ -19,3 +19,10 @@ def handle_keyboard(delay_amt=1): break elif k != 255: log.debug(f'k: {k}') + +def handle_keyboard_video(delay_amt=1): + key = cv.waitKey(1) & 0xFF + # if the `q` key was pressed, break from the loop + if key == ord("q"): + cv.destroyAllWindows() + sys.exit() diff --git a/megapixels/app/utils/identity_utils.py b/megapixels/app/utils/identity_utils.py index 775652dc..5855fbbd 100644 --- a/megapixels/app/utils/identity_utils.py +++ b/megapixels/app/utils/identity_utils.py @@ -29,6 +29,13 @@ def names_match_strict(a, b): return len(clean_a) == len(clean_b) and letter_match(clean_a, clean_b) and letter_match(clean_b, clean_a) +def sanitize_name(name, as_str=False): + splits = [unidecode.unidecode(x.strip().lower()) for x in name.strip().split(' ')] + if as_str: + return ' '.join(splits) + else: + return splits + ''' class Dataset(Enum): LFW, VGG_FACE, VGG_FACE2, MSCELEB, UCCS, UMD_FACES, SCUT_FBP, UCF_SELFIE, UTK, \ @@ -106,12 +113,18 @@ def get_names(opt_dataset, opt_data_store=types.DataStore.HDD): def similarity(a, b): return difflib.SequenceMatcher(a=a.lower(), b=b.lower()).ratio() -def names_match(name_a, name_b, threshold=0.9, as_float=False, compound_score=False): +def names_match(name_a, name_b, threshold=0.9, as_float=False, compound_score=False, name_a_pre=False, name_b_pre=False): '''Returns boolean if names are similar enough ''' # strip spaces and split names into list of plain text words - name_a_clean = [unidecode.unidecode(x.strip().lower()) for x in name_a.strip().split(' ')] - name_b_clean = [unidecode.unidecode(x.strip().lower()) for x in name_b.strip().split(' ')] + if name_a_pre: + name_a_clean = name_a + else: + name_a_clean = [unidecode.unidecode(x.strip().lower()) for x in name_a.strip().split(' ')] + if name_b_pre: + name_b_clean = name_b + else: + name_b_clean = [unidecode.unidecode(x.strip().lower()) for x in name_b.strip().split(' ')] # assign short long vars len_a = len(name_a_clean) diff --git a/megapixels/commands/datasets/citations_to_csv.py b/megapixels/commands/datasets/citations_to_csv.py index c6a04bd4..f3277d7e 100644 --- a/megapixels/commands/datasets/citations_to_csv.py +++ b/megapixels/commands/datasets/citations_to_csv.py @@ -35,9 +35,12 @@ def cli(ctx, opt_fp_in, opt_dir_out): else: fps_in = [opt_fp_in] - log.info(f'{fps_in}') + log.info(f'Converting {len(fps_in)} JSON files to CSV') for fp_in in fps_in: + + log.info(f'Processing: {Path(fp_in).name}') + with open(fp_in, 'r') as fp: json_data = json.load(fp) @@ -45,18 +48,22 @@ def cli(ctx, opt_fp_in, opt_dir_out): papers = [] dataset_key = json_data['paper']['key'] dataset_name = json_data['paper']['name'] - papers_main = get_orig_paper(json_data) - papers += papers_main - papers_citations = get_citations(dataset_key, dataset_name, json_data) - papers += papers_citations - papers = [p.to_dict() for p in papers] + try: + papers_main = get_orig_paper(json_data) + papers += papers_main + papers_citations = get_citations(dataset_key, dataset_name, json_data) + papers += papers_citations + papers = [p.to_dict() for p in papers] + except Exception as e: + log.error(f'{e} on {Path(fp_in).name}') + continue # save if not opt_dir_out: # save to same directory replacing ext fp_out = fp_in.replace('.json','.csv') else: - fp_out = join(opt_dir_out, Path(fp_in).name) + fp_out = join(opt_dir_out, f'{Path(fp_in).stem}.csv') df_papers = pd.DataFrame.from_dict(papers) df_papers.index.name = 'id' @@ -76,13 +83,13 @@ def get_citations(dataset_key, dataset_name, json_data): addresses = p.get('addresses', '') if addresses: for a in addresses: - pdf_url = '' if not p['pdf'] else p['pdf'][0] + pdf_url = '' if not p.get('pdf') else p.get('pdf')[0] paper = Paper(dataset_key, dataset_name, p['id'], p['title'], d_type, year, pdf_url, a['name'], a['type'], a['lat'], a['lng'], a['country']) papers.append(paper) else: - pdf_url = '' if not p['pdf'] else p['pdf'][0] + pdf_url = '' if not p.get('pdf') else p.get('pdf')[0] paper = Paper(p['key'], p['name'], d['id'], p['title'], 'main', year, pdf_url) papers.append(paper) return papers @@ -98,13 +105,13 @@ def get_orig_paper(json_data): for a in addresses: if type(a) == str or a is None: continue - pdf_url = '' if not p['pdf'] else p['pdf'][0] - paper = Paper(p['key'], p['name'], p['paper_id'], p['title'], d_type, year, + pdf_url = '' if not p.get('pdf') else p.get('pdf')[0] + paper = Paper(p.get('key'), p.get('name'), p.get('paper_id'), p.get('title'), d_type, year, pdf_url, - a['name'], a['type'], a['lat'], a['lng'], a['country']) + a.get('name'), a.get('type'), a.get('lat'), a.get('lng'), a.get('country')) papers.append(paper) else: - pdf_url = '' if not p['pdf'] else p['pdf'][0] - paper = Paper(p['key'], p['name'], p['paper_id'], p['title'], d_type, year, pdf_url) + pdf_url = '' if not p.get('pdf') else p.get('pdf')[0] + paper = Paper(p.get('key'), p.get('name'), p.get('paper_id'), p.get('title'), d_type, year, pdf_url) papers.append(paper) return papers diff --git a/megapixels/commands/datasets/pull_spreadsheet.py b/megapixels/commands/datasets/pull_spreadsheet.py index b8b68094..caf5eb43 100644 --- a/megapixels/commands/datasets/pull_spreadsheet.py +++ b/megapixels/commands/datasets/pull_spreadsheet.py @@ -21,6 +21,10 @@ from app.utils.logger_utils import Logger log = Logger.getLogger() opt_sheets = ['datasets', 'relationships', 'funding', 'references', 'sources', 'tags', 'citations', 'legal'] +dataset_sheet_keys = ['key', 'name_short', 'name_full', 'url', 'dl_im', 'purpose', 'funded_by', + 'year_start', 'year_end', 'year_published', 'images', 'videos', 'identities', + 'faces_or_persons', 'campus', 'youtube', 'flickr', 'google', 'bing', 'comment'] + @click.command() @click.option('-n', '--name', 'opt_spreadsheets', multiple=True, @@ -30,11 +34,15 @@ opt_sheets = ['datasets', 'relationships', 'funding', 'references', 'sources', ' @click.option('--all', 'opt_all', is_flag=True, help='Get all sheets') @click.option('-o', '--output', 'opt_fp_out', required=True, + type=click.Path(file_okay=False, dir_okay=True), help='Path to directory or filename') +@click.option('--share', 'opt_share', required=True, + type=click.Choice(['nyt', 'ft']), + help='Share filter') @click.option('-f', '--force', 'opt_force', is_flag=True, help='Force overwrite') @click.pass_context -def cli(ctx, opt_spreadsheets, opt_fp_out, opt_all, opt_force): +def cli(ctx, opt_spreadsheets, opt_fp_out, opt_all, opt_share, opt_force): """Fetch Google spreadsheet""" import sys @@ -47,6 +55,12 @@ def cli(ctx, opt_spreadsheets, opt_fp_out, opt_all, opt_force): for sheet_name in opt_spreadsheets: log.info(f'Get spreadsheet: {sheet_name}') + fp_out = join(opt_fp_out, f'{sheet_name}.csv') + fpp_out = Path(fp_out) + if fpp_out.exists() and not opt_force: + log.error(f'File "{fpp_out} exists. Use "-f" to overwrite') + return + sheet_data = fetch_google_sheet_objects(name=sheet_name) df_sheet = pd.DataFrame.from_dict(sheet_data) if sheet_name == 'datasets': @@ -58,22 +72,19 @@ def cli(ctx, opt_spreadsheets, opt_fp_out, opt_all, opt_force): fpp_out = fpp_out.parent else: fpp_out = join(opt_fp_out, f'{sheet_name}.csv') + log.info(f'Writing file: {fpp_out}') df_sheet.to_csv(fpp_out) def clean_datasets_sheet_ft(df): # clean data for FT df = df[df['ft_share'] == 'Y'] - keys = ['key', 'name_short', 'name_full', 'url', 'downloaded', 'purpose', 'wild'] - keys += ['campus', 'year_start', 'year_end', 'year_published', 'images', 'videos', 'identities', 'faces_or_persons', 'youtube', 'flickr', 'google', 'bing', 'comment'] - return df[keys] + return df[dataset_sheet_keys] def clean_datasets_sheet_nyt(df): # clean data for FT df = df[df['ft_share'] == 'Y'] - keys = ['key', 'name_short', 'name_full', 'url', 'downloaded', 'purpose', 'wild'] - keys += ['campus', 'year_start', 'year_end', 'year_published', 'images', 'videos', 'identities', 'faces_or_persons', 'youtube', 'flickr', 'google', 'bing', 'comment'] - return df[keys] + return df[dataset_sheet_keys] def fetch_spreadsheet(): """Open the Google Spreadsheet, which contains the individual worksheets""" diff --git a/megapixels/commands/processor/body_roi_video.py b/megapixels/commands/processor/body_roi_video.py new file mode 100644 index 00000000..84bcebd2 --- /dev/null +++ b/megapixels/commands/processor/body_roi_video.py @@ -0,0 +1,148 @@ +""" +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='Override enum input filename CSV') +@click.option('-o', '--output', 'opt_fp_out', required=True, + help='Override enum output filename CSV') +@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('--size', 'opt_size', + type=(int, int), default=(640, 480), + help='Input image size') +@click.option('-d', '--detector', 'opt_detector_type', + type=cfg.BodyDetectNetVar, + default=click_utils.get_default(types.BodyDetectNet.CVDNN), + help=click_utils.show_help(types.BodyDetectNet)) +@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_fp_out, opt_data_store, 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 person_detector + from app.models.data_store import DataStore + + # ------------------------------------------------- + # init here + + log = logger_utils.Logger.getLogger() + + opt_fp_out = opt_fp_out + if not opt_force and Path(opt_fp_out).exists(): + log.error('File exists. Use "-f / --force" to overwite') + return + + # set detector + if opt_detector_type == types.BodyDetectNet.CVDNN: + detector = person_detector.DetectorCVDNN() + else: + log.error('{} not yet implemented'.format(opt_detector_type.name)) + return + + # set largest flag, to keep all or only largest + opt_largest = (opt_largest == 'largest') + + # process video + cap = cv.VideoCapture(opt_fp_in) + + bboxes_all = [] + data_out = [] + frame_index = 0 + + while cap.isOpened(): + # get video frame + readable, im = cap.read() + if not readable: + break + + im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1]) + + try: + bboxes_norm = detector.detect(im_resized, pyramids=opt_pyramids, largest=opt_largest, + zone=opt_zone, conf=opt_conf_thresh, blob_size=opt_size) + except Exception as e: + log.error('could not detect: {}'.format(frame_index)) + log.error('{}'.format(e)) + continue + + for bbox in bboxes_norm: + roi = { + 'record_index': frame_index, + 'x': bbox.x, + 'y': bbox.y, + 'w': bbox.w, + 'h': bbox.h + } + data_out.append(roi) + + 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_video() + + frame_index += 1 + + + # create DataFrame and save to CSV + file_utils.mkdirs(opt_fp_out) + df = pd.DataFrame.from_dict(data_out) + df.index.name = 'index' + df.to_csv(opt_fp_out) + + # save script + file_utils.write_text(' '.join(sys.argv), '{}.sh'.format(opt_fp_out))
\ No newline at end of file |