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import click
from app.settings import types
from app.models.dataset import Dataset
from app.utils import click_utils
from app.settings import app_cfg as cfg
from app.utils.logger_utils import Logger
log = Logger.getLogger()
@click.command()
@click.option('-i', '--input', 'opt_fp_in', required=True,
help='Face image file to lookup')
@click.option('--data_store', 'opt_data_store',
type=cfg.DataStoreVar,
default=click_utils.get_default(types.DataStore.HDD),
show_default=True,
help=click_utils.show_help(types.DataStore))
@click.option('--dataset', 'opt_dataset',
type=cfg.DatasetVar,
required=True,
show_default=True,
help=click_utils.show_help(types.Dataset))
@click.option('--results', 'opt_results', default=5,
help='Number of match results to display')
@click.option('--gpu', 'opt_gpu', default=0,
help='GPU index (use -1 for CPU')
@click.pass_context
def cli(ctx, opt_fp_in, opt_data_store, opt_dataset, opt_results, opt_gpu):
"""Display image info"""
import sys
from glob import glob
from os.path import join
from pathlib import Path
import time
import pandas as pd
import cv2 as cv
from tqdm import tqdm
import imutils
from PIL import Image, ImageOps
from app.utils import file_utils, im_utils, display_utils, draw_utils
from app.models.data_store import DataStore
from app.processors import face_detector
from app.processors import face_extractor
log = Logger.getLogger()
# init dataset
dataset = Dataset(opt_data_store, opt_dataset)
dataset.load_metadata(types.Metadata.FILE_RECORD)
dataset.load_metadata(types.Metadata.FACE_VECTOR)
dataset.load_metadata(types.Metadata.FACE_ROI)
dataset.load_metadata(types.Metadata.IDENTITY)
# init face detection
detector = face_detector.DetectorCVDNN()
# init face extractor
extractor = face_extractor.ExtractorVGG()
# load query image
im_query = cv.imread(opt_fp_in)
# get detection as BBox object
bboxes = detector.detect(im_query, largest=True)
bbox_norm = bboxes[0]
dim = im_query.shape[:2][::-1]
bbox_dim = bbox_norm.to_dim(dim) # convert back to real dimensions
if not bbox_norm:
log.error('No face detected. Exiting')
return
# extract the face vectors
vec_query = extractor.extract(im_query, bbox_norm)
log.debug(f'len query: {len(vec_query)}')
# find matches
image_records = dataset.find_matches(vec_query, n_results=opt_results)
# summary
im_query = draw_utils.draw_bbox(im_query, bbox_norm, stroke_weight=4)
ims_match = [im_query]
opt_size = (256,256)
for image_record in image_records:
image_record.summarize()
log.info(f'{image_record.filepath}')
im_match = cv.imread(image_record.filepath)
dim_match = im_match.shape[:2][::-1]
bbox_match = image_record.bbox
score = image_record.score
if score < .5:
clr = (0,255,0)
elif score < .6:
clr = (0,255,125)
elif score < .65:
clr = (0,125,125)
elif score < .7:
clr = (0,125,255)
else:
clr = (0,0,255)
im_match = draw_utils.draw_bbox(im_match, bbox_match, stroke_weight=4, color=clr )
bbox_match_dim = bbox_match.to_dim(dim_match)
im_pil = im_utils.ensure_pil(im_match)
center = (bbox_match_dim.cx, bbox_match_dim.cy)
im_pil = ImageOps.fit(im_pil, opt_size, centering=center)
im_np = im_utils.ensure_np(im_pil)
if image_record.identity is not None:
log.debug(f'identity: {image_record.identity.name_display}')
else:
log.debug('no identity info')
log.debug(f'score: {image_record.score}')
ims_match.append(im_np)
# make montages of most similar faces
montages = imutils.build_montages(ims_match, (256, 256), (3,2))
# display
for i, montage in enumerate(montages):
cv.imshow(f'{opt_dataset.name.upper()}: page {i}', montage)
fp_out = join(Path(opt_fp_in).parent, f'{Path(opt_fp_in).stem}_{i}.png')
cv.imwrite(fp_out, montage)
display_utils.handle_keyboard()
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