diff options
Diffstat (limited to 'megapixels/app')
| -rw-r--r-- | megapixels/app/server/api.py | 1 | ||||
| -rw-r--r-- | megapixels/app/server/api_task.py | 40 | ||||
| -rw-r--r-- | megapixels/app/server/tasks/__init__.py | 19 | ||||
| -rw-r--r-- | megapixels/app/server/tasks/blur.py | 23 | ||||
| -rw-r--r-- | megapixels/app/server/tasks/fullmonte.py | 199 | ||||
| -rw-r--r-- | megapixels/app/utils/im_utils.py | 12 |
6 files changed, 245 insertions, 49 deletions
diff --git a/megapixels/app/server/api.py b/megapixels/app/server/api.py index 48279040..663f52cc 100644 --- a/megapixels/app/server/api.py +++ b/megapixels/app/server/api.py @@ -30,7 +30,6 @@ def index(): """List the datasets and their fields""" return jsonify({ 'datasets': list_datasets() }) - @api.route('/dataset/<dataset_name>') def show(dataset_name): """Show the data that a dataset will return""" diff --git a/megapixels/app/server/api_task.py b/megapixels/app/server/api_task.py index 36990997..23e11454 100644 --- a/megapixels/app/server/api_task.py +++ b/megapixels/app/server/api_task.py @@ -6,30 +6,21 @@ import dlib import simplejson as json import numpy as np from flask import Blueprint, request, jsonify -from PIL import Image # todo: try to remove PIL dependency - -# from app.models.sql_factory import load_sql_datasets, list_datasets, get_dataset, get_table -# from app.utils.im_utils import pil2np +from PIL import Image, ImageOps # todo: try to remove PIL dependency from celery.result import AsyncResult from app.server.tasks import celery from app.server.tasks import task_lookup, list_active_tasks +# from app.models.sql_factory import load_sql_datasets, list_datasets, get_dataset, get_table -api_task = Blueprint('api_task', __name__) +api_task = Blueprint('task', __name__) @api_task.route('/') def index(): """List active tasks""" return jsonify(list_active_tasks) -# from flask import render_template, redirect, url_for, send_from_directory -# from flask import request, make_response, jsonify -# from . import main, utils -# from PIL import Image, ImageOps -# import cv2 as cv -# import imutils - -@api_task.route('/<task_name>/<task_id>') +@api_task.route('/status/<task_name>/<task_id>') def task_status(task_name, task_id): """Return celery image processing status""" if task_name in task_lookup: @@ -69,6 +60,9 @@ def task_status(task_name, task_id): @api_task.route('/upload/sleep', methods=['GET', 'POST']) def sleep_test(): + """ + Test the Celery system using a task that sleeps. + """ async_task = task_lookup['sleep']['task'].apply_async(args=['sleep_test']) task_url = '/task/{}/{}'.format('sleep', async_task.id) return jsonify({ @@ -76,10 +70,12 @@ def sleep_test(): 'task_url': task_url, }) -@api_task.route('/upload', methods=['POST']) -def upload(): - style = request.form['style'] - print('style',style) +@api_task.route('/upload/:style', methods=['POST']) +def upload(style): + """ + Process a images in a particular style + """ + print('style: {}'.format(style)) if style in task_lookup: task = task_lookup[style]['task'] print('task',task) @@ -103,19 +99,19 @@ def upload(): # convert PNG to JPG print('[+] Resizing image') - # LOL MaskRCNN needs to be run outside of the Celery Task im = Image.open(file.stream).convert('RGB') - im = ImageOps.fit(im,(512,512)) + im = ImageOps.fit(im, (256, 256)) # # Save image to disk # print('[+] Save image to {}'.format(fpath)) # im.save(fpath, 'JPEG', quality=100) # im_pil_256 = im.resize((256,256)) - print('[+] ensure_np...') - im_np = imx.ensure_np(im_pil_256) + # print('[+] ensure_np...') + # im_np = imx.ensure_np(im_pil_256) celery_result = { + im: im, } print('[+] Start celery') @@ -124,6 +120,6 @@ def upload(): return jsonify({ 'result': True, - 'task_url': task_url, + 'taskURL': task_url, 'uuid': uuid_name }) diff --git a/megapixels/app/server/tasks/__init__.py b/megapixels/app/server/tasks/__init__.py index bac7309f..fd6e398a 100644 --- a/megapixels/app/server/tasks/__init__.py +++ b/megapixels/app/server/tasks/__init__.py @@ -5,7 +5,7 @@ from celery import Celery celery = Celery(__name__, backend=cfg.CELERY_RESULT_BACKEND, broker=cfg.CELERY_BROKER_URL) from app.server.tasks.sleep import sleep_task -# from app.server.tasks.blur import blur_task +from app.server.tasks.blur import blur_task def list_active_tasks(): dropdown = {} @@ -32,16 +32,15 @@ task_lookup = { 'active': True, 'default': True, }, - # 'blur': { - # 'title': 'Blur', - # 'task': blur_task, - # 'active': False, - # }, - # 'task_dull': { - # 'title': 'DullDream V2', - # 'task': task_dull, + 'blur': { + 'title': 'Blur', + 'task': blur_task, + 'active': False, + }, + # 'fullmonte': { + # 'title': 'TIA facial processing pipeline', + # 'task': fullmonte, # 'active': True, # 'default': True, # } } - diff --git a/megapixels/app/server/tasks/blur.py b/megapixels/app/server/tasks/blur.py index ede75e6a..3b7e20be 100644 --- a/megapixels/app/server/tasks/blur.py +++ b/megapixels/app/server/tasks/blur.py @@ -3,14 +3,14 @@ import sys import time import datetime import json -from PIL import Image, ImageFilter +from PIL import Image import cv2 as cv import numpy as np -from . import main, utils -from .. import basemodels +from app.utils.im_utils import ensure_np, ensure_pil from flask import current_app as app -from .paths import get_paths -celery = basemodels.celery + +from app.server.tasks import celery + from celery.utils.log import get_task_logger celery_logger = get_task_logger(__name__) import imutils @@ -37,9 +37,9 @@ def blur_task(self, uuid_name, extra): 'uuid': uuid_name }) - im_np = utils.ensure_np(im) + im_np = ensure_np(im) im_blur = cv.blur(im_np, (5,5), 1.0) - im_blur_pil = utils.ensure_pil(im_blur) + im_blur_pil = ensure_pil(im_blur) fn = uuid_name + '_blur.jpg' fpath = os.path.join(render_dir, fn) @@ -55,15 +55,6 @@ def blur_task(self, uuid_name, extra): self.update_state( state = 'PROCESSING', meta = { - 'percent': 0.50, - 'message': 'Sleeping for some reason', - 'uuid': uuid_name - }) - time.sleep(2) - - self.update_state( - state = 'PROCESSING', - meta = { 'percent': 0.75, 'message': 'Sleeping some more', 'uuid': uuid_name diff --git a/megapixels/app/server/tasks/fullmonte.py b/megapixels/app/server/tasks/fullmonte.py new file mode 100644 index 00000000..17ca9403 --- /dev/null +++ b/megapixels/app/server/tasks/fullmonte.py @@ -0,0 +1,199 @@ + +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): + return + + # 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('') + + + # ---------------------------------------------------------------------------- + # 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 diff --git a/megapixels/app/utils/im_utils.py b/megapixels/app/utils/im_utils.py index e882c67f..d36c1c32 100644 --- a/megapixels/app/utils/im_utils.py +++ b/megapixels/app/utils/im_utils.py @@ -19,7 +19,19 @@ from torch.autograd import Variable from sklearn.metrics.pairwise import cosine_similarity import datetime +def ensure_pil(im): + """Ensure image is Pillow format""" + try: + im.verify() + return im + except: + return Image.fromarray(im.astype('uint8'), 'RGB') +def ensure_np(im): + """Ensure image is numpy array""" + if type(im) == np.ndarray: + return im + return np.asarray(im, np.uint8) def num_channels(im): '''Returns number of channels in numpy.ndarray image''' |