From 198147bef9976a41046c3c513dc4d33babf7a238 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sun, 13 Jan 2019 22:20:06 +0100 Subject: extracting 3d facial vectors --- megapixels/app/server/api.py | 2 - megapixels/app/server/api_task.py | 13 +- megapixels/app/server/tasks/__init__.py | 15 +- megapixels/app/server/tasks/blur.py | 15 +- megapixels/app/server/tasks/demo.py | 244 +++++++++++++++++++++++++++++++ megapixels/app/server/tasks/fullmonte.py | 199 ------------------------- 6 files changed, 271 insertions(+), 217 deletions(-) create mode 100644 megapixels/app/server/tasks/demo.py delete mode 100644 megapixels/app/server/tasks/fullmonte.py (limited to 'megapixels/app/server') diff --git a/megapixels/app/server/api.py b/megapixels/app/server/api.py index 5ad454d8..b3bce9bc 100644 --- a/megapixels/app/server/api.py +++ b/megapixels/app/server/api.py @@ -1,5 +1,3 @@ -import logging -import logging.handlers import os import re import time diff --git a/megapixels/app/server/api_task.py b/megapixels/app/server/api_task.py index c9bc19ed..57ae9f7d 100644 --- a/megapixels/app/server/api_task.py +++ b/megapixels/app/server/api_task.py @@ -27,7 +27,8 @@ def task_status(task_name, task_id): if task_name in task_lookup: task = task_lookup[task_name]['task'].AsyncResult(task_id) # task = AsyncResult(task_id, app=celery) - else: + + if task_name not in task_lookup or task.info is None: return jsonify({ 'state': 'error', 'percent': 100, @@ -75,10 +76,16 @@ def sleep_test(): @api_task.route('/upload/blur', methods=['POST']) def upload(): + return process('blur') + +@api_task.route('/upload/demo', methods=['POST']) +def demo(): + return process('demo') + +def process(style): """ - Process a images in a particular style + Process an image in a particular style """ - style = 'blur' print('style: {}'.format(style)) if style in task_lookup: task = task_lookup[style]['task'] diff --git a/megapixels/app/server/tasks/__init__.py b/megapixels/app/server/tasks/__init__.py index fd6e398a..c0db0be5 100644 --- a/megapixels/app/server/tasks/__init__.py +++ b/megapixels/app/server/tasks/__init__.py @@ -6,6 +6,7 @@ celery = Celery(__name__, backend=cfg.CELERY_RESULT_BACKEND, broker=cfg.CELERY_B from app.server.tasks.sleep import sleep_task from app.server.tasks.blur import blur_task +from app.server.tasks.demo import demo_task def list_active_tasks(): dropdown = {} @@ -35,12 +36,12 @@ task_lookup = { 'blur': { 'title': 'Blur', 'task': blur_task, - 'active': False, + 'active': True, }, - # 'fullmonte': { - # 'title': 'TIA facial processing pipeline', - # 'task': fullmonte, - # 'active': True, - # 'default': True, - # } + 'demo': { + 'title': 'Facial processing pipeline', + 'task': demo_task, + 'active': True, + 'default': True, + } } diff --git a/megapixels/app/server/tasks/blur.py b/megapixels/app/server/tasks/blur.py index 42977097..74798cee 100644 --- a/megapixels/app/server/tasks/blur.py +++ b/megapixels/app/server/tasks/blur.py @@ -14,14 +14,14 @@ import app.settings.app_cfg as cfg from app.server.tasks import celery from celery.utils.log import get_task_logger -celery_logger = get_task_logger(__name__) +log = get_task_logger(__name__) import imutils @celery.task(bind=True) def blur_task(self, uuid_name, fn): """Process image and update during""" - celery_logger.debug('process_image_task, uuid: {}'.format(uuid_name)) - celery_logger.debug('fn: {}'.format(fn)) + log.debug('process_image_task, uuid: {}'.format(uuid_name)) + log.debug('fn: {}'.format(fn)) files = [] @@ -48,7 +48,7 @@ def blur_task(self, uuid_name, fn): fn = uuid_name + '_blur.jpg' fpath = os.path.join(cfg.DIR_SITE_USER_CONTENT, fn) im_blur_pil.save(fpath, 'JPEG', quality=80) - celery_logger.debug('fpath: {}'.format(fpath)) + log.debug('fpath: {}'.format(fpath)) print('fpath: {}'.format(fpath)) # files.append({ @@ -58,7 +58,10 @@ def blur_task(self, uuid_name, fn): meta['step'] += 1 meta['message'] = 'Applying blur' - meta['data']['blur_fn'] = os.path.join('/user_content/', fn) + meta['data']['blur_fn'] = { + 'title': 'Blurred image', + 'url': os.path.join('/user_content/', fn) + } self.update_state(state='PROCESSING', meta=meta) time.sleep(3) @@ -70,7 +73,7 @@ def blur_task(self, uuid_name, fn): self.update_state(state='PROCESSING', meta=meta) time.sleep(2) - celery_logger.debug('done!!') + log.debug('done!!') meta['step'] = meta['total'] meta['state'] = 'complete' diff --git a/megapixels/app/server/tasks/demo.py b/megapixels/app/server/tasks/demo.py new file mode 100644 index 00000000..acc5dbac --- /dev/null +++ b/megapixels/app/server/tasks/demo.py @@ -0,0 +1,244 @@ + +import app.settings.app_cfg as cfg +from app.server.tasks import celery + +from celery.utils.log import get_task_logger +log = get_task_logger(__name__) + +opt_size = (256, 256,) + +@celery.task(bind=True) +def demo_task(self, uuid_name, fn): + + import sys + import os + from os.path import join + from pathlib import Path + import time + + import numpy as np + 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 + + # TODO add selective testing + opt_gpu = -1 + opt_run_pose = True + opt_run_2d_68 = True + opt_run_3d_68 = True + opt_run_3d_68 = True + paths + + meta = { + 'step': 0, + 'total': 3, + 'message': 'Starting', + 'uuid': uuid_name, + 'data': {}, + } + paths = [] + + def step(msg, step=0): + meta['step'] += step + meta['message'] = msg + log.debug('> {}'.format(msg)) + self.update_state(state='PROCESSING', meta=meta) + + step('Loading image') + self.update_state(state='PROCESSING', meta=meta) + + # os.path.join('/user_content/', fn) + + # ------------------------------------------------- + # init here + + # load image + im = cv.imread(fn) + im_resized = im_utils.resize(im, width=opt_size[0], height=opt_size[1]) + + # ---------------------------------------------------------------------------- + # detect face + + face_detector_instance = face_detector.DetectorDLIBCNN(gpu=opt_gpu) # -1 for CPU + step('Detecting face') + st = time.time() + bboxes = face_detector_instance.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') + meta['error'] = 'No face detected' + self.update_state(state='FAILURE', meta=meta) + return meta + else: + log.info(f'Detected face in {(time.time() - st):.2f}s') + + + # ---------------------------------------------------------------------------- + # detect 3D landmarks + + step('Generating 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('') + + # 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) + + save_image('landmarks_3d_68', '3D Landmarks', im_landmarks_3d_68) + + def save_image(key, title, data): + fn = '{}_{}.jpg'.format(uuid_name, key) + fpath = os.path.join(cfg.DIR_SITE_USER_CONTENT, fn) + paths.append(fpath) + cv.imwrite(fpath, im_landmarks_3d_68) + + meta['data']['landmarks_3d_68'] = { + 'title': '3D Landmarks', + 'url': os.path.join('/user_content/', fn), + } + step('Generated 3D Landmarks', step=0) + + # ---------------------------------------------------------------------------- + # generate 3D GIF animation + + # step('Generating 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 + + # step('Generating face vectors') + # 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 + + # step('Generating 2D 68PT landmarks') + # 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: + # step('Generating 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('') + + + # # ---------------------------------------------------------------------------- + # # generate pose from 68 point 2D landmarks + + step('Done') + + # done + # self.log.debug('Add age real') + # self.log.debug('Add age apparent') + # self.log.debug('Add gender') + + + # # 3DDFA + # self.log.debug('Add depth') + # self.log.debug('Add pncc') + + # # TODO + # self.log.debug('Add 3D face model') + # self.log.debug('Add face texture flat') + # self.log.debug('Add ethnicity') + + # display + # draw bbox + + # # 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 + + # # 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]) + + log.debug('done!!') + + for path in paths: + if os.path.exists(path): + os.remove(path) + + meta['step'] = meta['total'] + meta['state'] = 'SUCCESS' + return meta diff --git a/megapixels/app/server/tasks/fullmonte.py b/megapixels/app/server/tasks/fullmonte.py deleted file mode 100644 index 8215656a..00000000 --- a/megapixels/app/server/tasks/fullmonte.py +++ /dev/null @@ -1,199 +0,0 @@ - -import sys -import os -from os.path import join -from pathlib import Path -import time - -import numpy as np -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 - -@celery.task(bind=True) -def fullmonte_task(self, uuid_name, fn): - # TOOD add selective testing - opt_run_pose = True - opt_run_2d_68 = True - opt_run_3d_68 = True - opt_run_3d_68 = True - - return - - # ------------------------------------------------- - # 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('') - - - # ---------------------------------------------------------------------------- - # generate pose from 68 point 2D landmarks - - # done - self.log.debug('Add age real') - self.log.debug('Add age apparent') - self.log.debug('Add gender') - - - # 3DDFA - self.log.debug('Add depth') - self.log.debug('Add pncc') - - # TODO - self.log.debug('Add 3D face model') - self.log.debug('Add face texture flat') - self.log.debug('Add ethnicity') - - # 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 \ No newline at end of file -- cgit v1.2.3-70-g09d2