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Diffstat (limited to 'megapixels/commands/demo/all.py')
| -rw-r--r-- | megapixels/commands/demo/all.py | 219 |
1 files changed, 219 insertions, 0 deletions
diff --git a/megapixels/commands/demo/all.py b/megapixels/commands/demo/all.py new file mode 100644 index 00000000..e447492b --- /dev/null +++ b/megapixels/commands/demo/all.py @@ -0,0 +1,219 @@ +""" +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 + + +@click.command() +@click.option('-i', '--input', 'opt_fp_in', default=None, required=True, + help='Image filepath') +@click.option('-o', '--output', 'opt_fp_out', default=None, + help='GIF output path') +@click.option('--size', 'opt_size', + type=(int, int), default=(300, 300), + help='Output image size') +@click.option('--gif-size', 'opt_gif_size', + type=(int, int), default=(480, 480), + help='GIF output size') +@click.option('--gif-frames', 'opt_gif_frames', default=15, + help='GIF frames') +@click.option('-g', '--gpu', 'opt_gpu', default=0, + help='GPU index') +@click.option('-f', '--force', 'opt_force', is_flag=True, + help='Force overwrite file') +@click.option('--display/--no-display', 'opt_display', is_flag=True, default=False, + help='Display detections to debug') +@click.pass_context +def cli(ctx, opt_fp_in, opt_fp_out, opt_gpu, opt_gif_frames, + opt_size, opt_gif_size, opt_force, opt_display): + """Generates 3D landmark animations from CSV files""" + + import sys + import os + from os.path import join + from pathlib import Path + import time + + from tqdm import tqdm + import numpy as np + import pandas as pd + import cv2 as cv + import dlib + from PIL import Image + import matplotlib.pyplot as plt + + from app.utils import logger_utils, file_utils, im_utils, display_utils, draw_utils + from app.utils import plot_utils + from app.processors import face_detector, face_landmarks + from app.models.data_store import DataStore + + # TOOD add selective testing + opt_run_pose = True + opt_run_2d_68 = True + opt_run_3d_68 = True + opt_run_3d_68 = True + + + # ------------------------------------------------- + # init here + + + log = logger_utils.Logger.getLogger() + + # load image + im = cv.imread(opt_fp_in) + im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1]) + + + # ---------------------------------------------------------------------------- + # detect face + + face_detector = face_detector.DetectorDLIBCNN(gpu=opt_gpu) # -1 for CPU + log.info('detecting face...') + st = time.time() + bboxes = face_detector.detect(im_resized, largest=True) + bbox = bboxes[0] + dim = im_resized.shape[:2][::-1] + bbox_dim = bbox.to_dim(dim) + if not bbox: + log.error('no face detected') + return + else: + log.info(f'Detected face in {(time.time() - st):.2f}s') + log.info('') + + + # ---------------------------------------------------------------------------- + # detect 3D landmarks + + log.info('loading 3D landmark generator files...') + landmark_detector_3d_68 = face_landmarks.FaceAlignment3D_68(gpu=opt_gpu) # -1 for CPU + log.info('generating 3D landmarks...') + st = time.time() + points_3d_68 = landmark_detector_3d_68.landmarks(im_resized, bbox_dim.to_xyxy()) + log.info(f'generated 3D landmarks in {(time.time() - st):.2f}s') + log.info('') + + + # ---------------------------------------------------------------------------- + # generate 3D GIF animation + + log.info('generating 3D animation...') + if not opt_fp_out: + fpp_im = Path(opt_fp_in) + fp_out = join(fpp_im.parent, f'{fpp_im.stem}_anim.gif') + else: + fp_out = opt_fp_out + st = time.time() + plot_utils.generate_3d_landmark_anim(np.array(points_3d_68), fp_out, + size=opt_gif_size, num_frames=opt_gif_frames) + log.info(f'Generated animation in {(time.time() - st):.2f}s') + log.info(f'Saved to: {fp_out}') + log.info('') + + + # ---------------------------------------------------------------------------- + # generate face vectors, only to test if feature extraction works + + log.info('initialize face recognition model...') + from app.processors import face_recognition + face_rec = face_recognition.RecognitionDLIB() + st = time.time() + log.info('generating face vector...') + vec = face_rec.vec(im_resized, bbox_dim) + log.info(f'generated face vector in {(time.time() - st):.2f}s') + log.info('') + + + # ---------------------------------------------------------------------------- + # generate 68 point landmarks using dlib + + log.info('initializing face landmarks 68 dlib...') + from app.processors import face_landmarks + landmark_detector_2d_68 = face_landmarks.Dlib2D_68() + log.info('generating 2D 68PT landmarks...') + st = time.time() + points_2d_68 = landmark_detector_2d_68.landmarks(im_resized, bbox_dim) + log.info(f'generated 2D 68PT face landmarks in {(time.time() - st):.2f}s') + log.info('') + + + # ---------------------------------------------------------------------------- + # generate pose from 68 point 2D landmarks + + if opt_run_pose: + log.info('initialize pose...') + from app.processors import face_pose + pose_detector = face_pose.FacePoseDLIB() + log.info('generating pose...') + st = time.time() + pose_data = pose_detector.pose(points_2d_68, dim) + log.info(f'generated pose {(time.time() - st):.2f}s') + log.info('') + + + # x + + + + # display + if opt_display: + + # draw bbox + + # draw 3d landmarks + im_landmarks_3d_68 = im_resized.copy() + draw_utils.draw_landmarks3D(im_landmarks_3d_68, points_3d_68) + draw_utils.draw_bbox(im_landmarks_3d_68, bbox_dim) + + # draw 2d landmarks + im_landmarks_2d_68 = im_resized.copy() + draw_utils.draw_landmarks2D(im_landmarks_2d_68, points_2d_68) + draw_utils.draw_bbox(im_landmarks_2d_68, bbox_dim) + + # draw pose + if opt_run_pose: + im_pose = im_resized.copy() + draw_utils.draw_pose(im_pose, pose_data['point_nose'], pose_data['points']) + draw_utils.draw_degrees(im_pose, pose_data) + + # draw animated GIF + im = Image.open(fp_out) + im_frames = [] + duration = im.info['duration'] + try: + while True: + im.seek(len(im_frames)) + mypalette = im.getpalette() + im.putpalette(mypalette) + im_jpg = Image.new("RGB", im.size) + im_jpg.paste(im) + im_np = im_utils.pil2np(im_jpg.copy()) + im_frames.append(im_np) + except EOFError: + pass # end of GIF sequence + + n_frames = len(im_frames) + frame_number = 0 + + while True: + # show all images here + cv.imshow('Original', im_resized) + cv.imshow('2D 68PT Landmarks', im_landmarks_2d_68) + cv.imshow('3D 68PT Landmarks', im_landmarks_3d_68) + cv.imshow('Pose', im_pose) + cv.imshow('3D 68pt GIF', im_frames[frame_number]) + frame_number = (frame_number + 1) % n_frames + k = cv.waitKey(duration) & 0xFF + if k == 27 or k == ord('q'): # ESC + cv.destroyAllWindows() + sys.exit() + elif k != 255: + # any key to continue + break
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