diff --git a/aihub/deep-learning/face-paint/face-paint.ipynb b/aihub/deep-learning/face-paint/face-paint.ipynb index fa4f3b9c..09941007 100644 --- a/aihub/deep-learning/face-paint/face-paint.ipynb +++ b/aihub/deep-learning/face-paint/face-paint.ipynb @@ -1 +1 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyP2f3JoE7FzJTLLv4x3sCN8"},"kernelspec":{"name":"python3","display_name":"Python 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LIN","userId":"15916362134103428022"}},"outputId":"d6d0aa5b-1232-4629-b62f-55c27f6d71e5"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/torch/hub.py:267: UserWarning: You are about to download and run code from an untrusted repository. In a future release, this won't be allowed. To add the repository to your trusted list, change the command to {calling_fn}(..., trust_repo=False) and a command prompt will appear asking for an explicit confirmation of trust, or load(..., trust_repo=True), which will assume that the prompt is to be answered with 'yes'. You can also use load(..., trust_repo='check') which will only prompt for confirmation if the repo is not already trusted. This will eventually be the default behaviour\n"," \"You are about to download and run code from an untrusted repository. In a future release, this won't \"\n","Downloading: \"https://github.com/bryandlee/animegan2-pytorch/zipball/main\" to /root/.cache/torch/hub/main.zip\n","Downloading: \"https://github.com/bryandlee/animegan2-pytorch/raw/main/weights/face_paint_512_v2.pt\" to /root/.cache/torch/hub/checkpoints/face_paint_512_v2.pt\n"]},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0.00/8.20M [00:00 1:\n"," rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))\n"," img = img.resize(rsize, PIL.Image.ANTIALIAS)\n"," quad /= shrink\n"," qsize /= shrink\n","\n"," # Crop.\n"," border = max(int(np.rint(qsize * 0.1)), 3)\n"," crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))\n"," crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))\n"," if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:\n"," img = img.crop(crop)\n"," quad -= crop[0:2]\n","\n"," # Pad.\n"," pad = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))\n"," pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))\n"," if enable_padding and max(pad) > border - 4:\n"," pad = np.maximum(pad, int(np.rint(qsize * 0.3)))\n"," img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')\n"," h, w, _ = img.shape\n"," y, x, _ = np.ogrid[:h, :w, :1]\n"," mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w-1-x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h-1-y) / pad[3]))\n"," blur = qsize * 0.02\n"," img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)\n"," img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0)\n"," img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')\n"," quad += pad[:2]\n","\n"," # Transform.\n"," img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)\n"," if output_size < transform_size:\n"," img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)\n","\n"," return img"],"metadata":{"id":"BdtS4FgoZCeP","executionInfo":{"status":"ok","timestamp":1664325500724,"user_tz":-480,"elapsed":907,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["! pip install paddlepaddle"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0x1Sms2lUBzg","executionInfo":{"status":"ok","timestamp":1664325569151,"user_tz":-480,"elapsed":18780,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"b24739be-62de-4f2c-d497-3c6f9f0e904e"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting paddlepaddle\n"," Downloading paddlepaddle-2.3.2-cp37-cp37m-manylinux1_x86_64.whl (112.5 MB)\n","\u001b[K |████████████████████████████████| 112.5 MB 56 kB/s \n","\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from paddlepaddle) (4.4.2)\n","Requirement already satisfied: requests>=2.20.0 in /usr/local/lib/python3.7/dist-packages (from paddlepaddle) (2.23.0)\n","Collecting paddle-bfloat==0.1.7\n"," Downloading paddle_bfloat-0.1.7-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (394 kB)\n","\u001b[K |████████████████████████████████| 394 kB 55.6 MB/s \n","\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from 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scikit-learn in /usr/local/lib/python3.7/dist-packages (from sklearn->paddleseg) (1.0.2)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn->paddleseg) (3.1.0)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn->paddleseg) (1.1.0)\n","Building wheels for collected packages: sklearn\n"," Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1310 sha256=b89a1fbdc309ad9a29b5b0bbbd63c735833ad9bd86e5e367da424ffcbd3ef1a8\n"," Stored in directory: /root/.cache/pip/wheels/46/ef/c3/157e41f5ee1372d1be90b09f74f82b10e391eaacca8f22d33e\n","Successfully built sklearn\n","Installing collected packages: pycryptodome, multiprocess, Flask-Babel, bce-python-sdk, visualdl, sklearn, paddleseg\n","Successfully installed Flask-Babel-2.0.0 bce-python-sdk-0.8.74 multiprocess-0.70.13 paddleseg-2.6.0 pycryptodome-3.15.0 sklearn-0.0 visualdl-2.4.1\n"]}]},{"cell_type":"code","source":["!git clone https://github.com/PaddlePaddle/PaddleSeg.git"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uekKYt1jUCje","executionInfo":{"status":"ok","timestamp":1664325608667,"user_tz":-480,"elapsed":17077,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"e298a62c-f6be-47a2-812f-e16c28a7f9fa"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'PaddleSeg'...\n","remote: Enumerating objects: 20557, done.\u001b[K\n","remote: Counting objects: 100% (8/8), done.\u001b[K\n","remote: Compressing objects: 100% (8/8), done.\u001b[K\n","remote: Total 20557 (delta 2), reused 1 (delta 0), pack-reused 20549\u001b[K\n","Receiving objects: 100% (20557/20557), 345.93 MiB | 24.02 MiB/s, done.\n","Resolving deltas: 100% (13398/13398), done.\n"]}]},{"cell_type":"code","source":["%cd PaddleSeg/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dTrIgetVUCly","executionInfo":{"status":"ok","timestamp":1664325625547,"user_tz":-480,"elapsed":371,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"3a1cb387-3f33-4e16-a7a1-671c56952189"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/PaddleSeg\n"]}]},{"cell_type":"code","source":["%cd contrib/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"m5Rp6mj8UCn_","executionInfo":{"status":"ok","timestamp":1664325626951,"user_tz":-480,"elapsed":3,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"d9def58e-3721-4bee-8993-d87da4465887"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/PaddleSeg/contrib\n"]}]},{"cell_type":"code","source":["%cd PP-HumanSeg/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u2Re_7WLUCqQ","executionInfo":{"status":"ok","timestamp":1664325628769,"user_tz":-480,"elapsed":3,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"bf97b6c0-f8b3-4cbf-b127-b46a122cdfdb"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/PaddleSeg/contrib/PP-HumanSeg\n"]}]},{"cell_type":"code","source":["# 执行以下脚本下载所有Inference Model\n","!python src/download_inference_models.py"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OEwR0i14UCso","executionInfo":{"status":"ok","timestamp":1664325703033,"user_tz":-480,"elapsed":72282,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"a5465b01-9675-441a-d1ec-ab3440043be7"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/portrait_pp_humansegv1_lite_398x224_inference_model_with_softmax.zip\n","Downloading portrait_pp_humansegv1_lite_398x224_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Uncompress portrait_pp_humansegv1_lite_398x224_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/portrait_pp_humansegv2_lite_256x144_smaller/portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax.zip\n","Downloading portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Uncompress portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/human_pp_humansegv1_lite_192x192_inference_model_with_softmax.zip\n","Downloading human_pp_humansegv1_lite_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Uncompress human_pp_humansegv1_lite_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/human_pp_humansegv2_lite_192x192_inference_model_with_softmax.zip\n","Downloading human_pp_humansegv2_lite_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Uncompress human_pp_humansegv2_lite_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/human_pp_humansegv1_mobile_192x192_inference_model_with_softmax.zip\n","Downloading human_pp_humansegv1_mobile_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Uncompress human_pp_humansegv1_mobile_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/human_pp_humansegv2_mobile_192x192_inference_model_with_softmax.zip\n","Downloading human_pp_humansegv2_mobile_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Uncompress human_pp_humansegv2_mobile_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Download inference models finished.\n"]}]},{"cell_type":"code","source":["# 可选\n","# 下载测试数据集\n","!python src/download_data.py"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0sp1SpEOUCvB","executionInfo":{"status":"ok","timestamp":1664325930050,"user_tz":-480,"elapsed":100116,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"8933c381-74c9-4f99-f5ce-4455515d3f64"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Connecting to https://paddleseg.bj.bcebos.com/humanseg/data/mini_supervisely.zip\n","Downloading mini_supervisely.zip\n","[==================================================] 100.00%\n","Uncompress mini_supervisely.zip\n","[==================================================] 100.00%\n","Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/videos.zip\n","Downloading videos.zip\n","[==================================================] 100.00%\n","Uncompress videos.zip\n","[==================================================] 100.00%\n","Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/images.zip\n","Downloading images.zip\n","[==================================================] 100.00%\n","Uncompress images.zip\n","[==================================================] 100.00%\n","Data download finished!\n"]}]},{"cell_type":"code","source":["%cd src"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WaQ54aEHY1f3","executionInfo":{"status":"ok","timestamp":1664325940485,"user_tz":-480,"elapsed":367,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"a7eb2247-5b3d-4ea6-c506-3daa885df61e"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/PaddleSeg/contrib/PP-HumanSeg/src\n"]}]},{"cell_type":"code","source":["import argparse\n","import os\n","import sys\n","\n","import cv2\n","import numpy as np\n","from tqdm import tqdm\n","\n","# __dir__ = os.path.dirname(os.path.abspath(__file__))\n","# sys.path.append(os.path.abspath(os.path.join(__dir__, '../../../')))\n","from paddleseg.utils import get_sys_env, logger, get_image_list\n","\n","import codecs\n","import os\n","import sys\n","import time\n","\n","import yaml\n","import numpy as np\n","import cv2\n","import paddle\n","from paddle.inference import create_predictor, PrecisionType\n","from paddle.inference import Config as PredictConfig\n","\n","\n","import paddleseg.transforms as T\n","from paddleseg.core.infer import reverse_transform\n","from paddleseg.cvlibs import manager\n","from paddleseg.utils import TimeAverager\n","\n","from optic_flow_process import optic_flow_process\n","\n","from PIL import Image\n","\n","\n","def get_bg_img(bg_img_path, img_shape):\n"," if bg_img_path is None:\n"," bg = 255 * np.ones(img_shape)\n"," elif not os.path.exists(bg_img_path):\n"," raise Exception('The --bg_img_path is not existed: {}'.format(\n"," bg_img_path))\n"," else:\n"," bg = cv2.imread(bg_img_path)\n"," return bg\n","\n","\n","def makedirs(save_dir):\n"," dirname = save_dir if os.path.isdir(save_dir) else \\\n"," os.path.dirname(save_dir)\n"," if not os.path.exists(dirname):\n"," os.makedirs(dirname)\n","\n","\n","def seg_image(args):\n"," print(args)\n"," assert os.path.exists(args['img_path']), \\\n"," \"The --img_path is not existed: {}.\".format(args['img_path'])\n","\n"," logger.info(\"Input: image\")\n"," logger.info(\"Create predictor...\")\n"," predictor = Predictor(args)\n","\n"," logger.info(\"Start predicting...\")\n"," img = cv2.imread(args['img_path'])\n"," bg_img = get_bg_img(args['bg_img_path'], img.shape)\n"," out_img = predictor.run(img, bg_img)\n"," cv2.imwrite(args['save_dir'], out_img)\n"," # im = Image.open(args['save_dir']) \n"," # display(im)\n","\n","class Predictor:\n"," def __init__(self, args):\n"," self.args = args\n"," self.cfg = DeployConfig(args['config'], False)\n"," self.compose = T.Compose(self.cfg.transforms)\n","\n"," pred_cfg = PredictConfig(self.cfg.model, self.cfg.params)\n"," pred_cfg.disable_glog_info()\n"," if self.args['use_gpu']:\n"," pred_cfg.enable_use_gpu(100, 0)\n","\n"," self.predictor = create_predictor(pred_cfg)\n"," if self.args['test_speed']:\n"," self.cost_averager = TimeAverager()\n","\n"," if args['use_optic_flow']:\n","\n"," self.disflow = cv2.DISOpticalFlow_create(\n"," cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST)\n"," width, height = self.cfg.target_size()\n"," self.prev_gray = np.zeros((height, width), np.uint8)\n"," self.prev_cfd = np.zeros((height, width), np.float32)\n"," self.is_first_frame = True\n","\n"," def run(self, img, bg):\n"," input_names = self.predictor.get_input_names()\n"," input_handle = self.predictor.get_input_handle(input_names[0])\n","\n"," data = self.compose({'img': img})\n"," input_data = np.array([data['img']])\n","\n"," input_handle.reshape(input_data.shape)\n"," input_handle.copy_from_cpu(input_data)\n"," if self.args['test_speed']:\n"," start = time.time()\n","\n"," self.predictor.run()\n","\n"," if self.args['test_speed']:\n"," self.cost_averager.record(time.time() - start)\n"," output_names = self.predictor.get_output_names()\n"," output_handle = self.predictor.get_output_handle(output_names[0])\n"," output = output_handle.copy_to_cpu()\n","\n"," return self.postprocess(output, img, data, bg)\n","\n"," def postprocess(self, pred_img, origin_img, data, bg):\n"," trans_info = data['trans_info']\n"," score_map = pred_img[0, 1, :, :]\n","\n"," # post process\n"," if self.args['use_post_process']:\n"," mask_original = score_map.copy()\n"," mask_original = (mask_original * 255).astype(\"uint8\")\n"," _, mask_thr = cv2.threshold(mask_original, 240, 1,\n"," cv2.THRESH_BINARY)\n"," kernel_erode = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))\n"," kernel_dilate = cv2.getStructuringElement(cv2.MORPH_CROSS, (25, 25))\n"," mask_erode = cv2.erode(mask_thr, kernel_erode)\n"," mask_dilate = cv2.dilate(mask_erode, kernel_dilate)\n"," score_map *= mask_dilate\n","\n"," # optical flow\n"," if self.args['use_optic_flow']:\n"," score_map = 255 * score_map\n"," cur_gray = cv2.cvtColor(origin_img, cv2.COLOR_BGR2GRAY)\n"," cur_gray = cv2.resize(cur_gray,\n"," (pred_img.shape[-1], pred_img.shape[-2]))\n"," optflow_map = optic_flow_process(cur_gray, score_map, self.prev_gray, self.prev_cfd, \\\n"," self.disflow, self.is_first_frame)\n"," self.prev_gray = cur_gray.copy()\n"," self.prev_cfd = optflow_map.copy()\n"," self.is_first_frame = False\n"," score_map = optflow_map / 255.\n","\n"," score_map = score_map[np.newaxis, np.newaxis, ...]\n"," score_map = reverse_transform(\n"," paddle.to_tensor(score_map), trans_info, mode='bilinear')\n"," alpha = np.transpose(score_map.numpy().squeeze(1), [1, 2, 0])\n","\n"," h, w, _ = origin_img.shape\n"," bg = cv2.resize(bg, (w, h))\n"," if bg.ndim == 2:\n"," bg = bg[..., np.newaxis]\n","\n"," out = (alpha * origin_img + (1 - alpha) * bg).astype(np.uint8)\n"," return out\n","\n","class DeployConfig:\n"," def __init__(self, path, vertical_screen):\n"," with codecs.open(path, 'r', 'utf-8') as file:\n"," self.dic = yaml.load(file, Loader=yaml.FullLoader)\n","\n"," [width, height] = self.dic['Deploy']['transforms'][0]['target_size']\n"," if vertical_screen and width > height:\n"," self.dic['Deploy']['transforms'][0][\n"," 'target_size'] = [height, width]\n","\n"," self._transforms = self._load_transforms(self.dic['Deploy'][\n"," 'transforms'])\n"," self._dir = os.path.dirname(path)\n","\n"," @property\n"," def transforms(self):\n"," return self._transforms\n","\n"," @property\n"," def model(self):\n"," return os.path.join(self._dir, self.dic['Deploy']['model'])\n","\n"," @property\n"," def params(self):\n"," return os.path.join(self._dir, self.dic['Deploy']['params'])\n","\n"," def target_size(self):\n"," [width, height] = self.dic['Deploy']['transforms'][0]['target_size']\n"," return [width, height]\n","\n"," def _load_transforms(self, t_list):\n"," com = manager.TRANSFORMS\n"," transforms = []\n"," for t in t_list:\n"," ctype = t.pop('type')\n"," transforms.append(com[ctype](**t))\n","\n"," return transforms\n","\n","\n","\n","\n"],"metadata":{"id":"Lgw48SeQUCxO","executionInfo":{"status":"ok","timestamp":1664325945374,"user_tz":-480,"elapsed":2571,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["args = {'config': '/content/PaddleSeg/contrib/PP-HumanSeg/inference_models/portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax/deploy.yaml','img_path': '/content/PaddleSeg/contrib/PP-HumanSeg/data/images/human.jpg','bg_img_path': '/content/PaddleSeg/contrib/PP-HumanSeg/data/images/bg_1.jpg','save_dir': '/content/PaddleSeg/contrib/PP-HumanSeg/data/1.jpg','use_gpu': True,'test_speed' : False,'use_optic_flow' : False,'use_post_process' : False}"],"metadata":{"id":"DKM2Y3IWUCz8","executionInfo":{"status":"ok","timestamp":1664326034479,"user_tz":-480,"elapsed":2,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["seg_image(args)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fiTsjsZEeDP8","executionInfo":{"status":"ok","timestamp":1664326036816,"user_tz":-480,"elapsed":681,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"fb63321a-335e-434b-c0e4-e2255a2cc4cb"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["{'config': '/content/PaddleSeg/contrib/PP-HumanSeg/inference_models/portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax/deploy.yaml', 'img_path': '/content/PaddleSeg/contrib/PP-HumanSeg/data/images/human.jpg', 'bg_img_path': '/content/PaddleSeg/contrib/PP-HumanSeg/data/images/bg_1.jpg', 'save_dir': '/content/PaddleSeg/contrib/PP-HumanSeg/data/1.jpg', 'use_gpu': True, 'test_speed': False, 'use_optic_flow': False, 'use_post_process': False}\n","2022-09-28 00:47:16 [INFO]\tInput: image\n","2022-09-28 00:47:16 [INFO]\tCreate predictor...\n","2022-09-28 00:47:17 [INFO]\tStart predicting...\n"]}]},{"cell_type":"code","source":["import requests\n","\n","# 加载网络或本地文件\n","img = Image.open(args['save_dir']).convert(\"RGB\")\n","# img = Image.open(\"/content/sample.jpg\").convert(\"RGB\")\n","\n","face_detector = get_dlib_face_detector()\n","landmarks = face_detector(img)\n","for landmark in landmarks:\n"," face = align_and_crop_face(img, landmark, expand=1.3)\n"," p_face = face2paint(model=model, img=face, size=512)\n"," # display(p_face)\n"," # p_face.save('1.png') # 此输出为对比图片\n"," # 裁剪为需要的部分输出\n"," x_, y_ = p_face.size\n"," out = p_face.crop((int(x_/2), 0, x_, y_))\n"," # display(out)\n"," # out.save('1.png')"],"metadata":{"id":"QEK24X44ZJ4X","executionInfo":{"status":"ok","timestamp":1664326062275,"user_tz":-480,"elapsed":22596,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["%pwd"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"uV8IyX__2v9V","executionInfo":{"status":"ok","timestamp":1664326078810,"user_tz":-480,"elapsed":13,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"14e52fa3-21f0-40bc-fe6a-4d0f8442b2ea"},"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/PaddleSeg/contrib/PP-HumanSeg/src'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":22}]}]} \ No newline at end of file +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":[],"mount_file_id":"1PkKK8tThmnDGqbdIDmtap4gKim9hDqGB","authorship_tag":"ABX9TyOAdEf4HXCjo+WALiw+2Owv"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["#@title 开始之前\n","# 安装项目所需依赖库\n","!pip install pyyaml>=5.1,visualdl>=2.2.0,opencv-python,tqdm,filelock,scipy,prettytable,sklearn,torch>=1.7.1,torchvision"],"metadata":{"id":"rGzneQc6FztT","executionInfo":{"status":"ok","timestamp":1664352590745,"user_tz":-480,"elapsed":3500,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}}},"execution_count":33,"outputs":[]},{"cell_type":"code","execution_count":34,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z4WJcIONY1j6","executionInfo":{"status":"ok","timestamp":1664352591102,"user_tz":-480,"elapsed":361,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"f978a8d2-a416-47f1-ece3-f233d632b94b"},"outputs":[{"output_type":"stream","name":"stderr","text":["Using cache found in /root/.cache/torch/hub/bryandlee_animegan2-pytorch_main\n","Using cache found in /root/.cache/torch/hub/bryandlee_animegan2-pytorch_main\n"]}],"source":["#@title 加载项目\n","# 维护：蒋李雾龙\n","\n","import torch \n","from PIL import Image\n","\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","model = torch.hub.load(\"bryandlee/animegan2-pytorch:main\", \"generator\", device=device).eval()\n","face2paint = torch.hub.load(\"bryandlee/animegan2-pytorch:main\", \"face2paint\", device=device, side_by_side=True)"]},{"cell_type":"code","source":["#@title 加载所需方法\n","\n","import os\n","import dlib\n","import collections\n","from typing import Union, List\n","import numpy as np\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","\n","\n","def get_dlib_face_detector(predictor_path: str = \"shape_predictor_68_face_landmarks.dat\"):\n","\n"," if not os.path.isfile(predictor_path):\n"," model_file = \"shape_predictor_68_face_landmarks.dat.bz2\"\n"," os.system(f\"wget http://dlib.net/files/{model_file}\")\n"," os.system(f\"bzip2 -dk {model_file}\")\n","\n"," detector = dlib.get_frontal_face_detector()\n"," shape_predictor = dlib.shape_predictor(predictor_path)\n","\n"," def detect_face_landmarks(img: Union[Image.Image, np.ndarray]):\n"," if isinstance(img, Image.Image):\n"," img = np.array(img)\n"," faces = []\n"," dets = detector(img)\n"," for d in dets:\n"," shape = shape_predictor(img, d)\n"," faces.append(np.array([[v.x, v.y] for v in shape.parts()]))\n"," return faces\n"," \n"," return detect_face_landmarks\n","\n","\n","def display_facial_landmarks(\n"," img: Image, \n"," landmarks: List[np.ndarray],\n"," fig_size=[15, 15]\n","):\n"," plot_style = dict(\n"," marker='o',\n"," markersize=4,\n"," linestyle='-',\n"," lw=2\n"," )\n"," pred_type = collections.namedtuple('prediction_type', ['slice', 'color'])\n"," pred_types = {\n"," 'face': pred_type(slice(0, 17), (0.682, 0.780, 0.909, 0.5)),\n"," 'eyebrow1': pred_type(slice(17, 22), (1.0, 0.498, 0.055, 0.4)),\n"," 'eyebrow2': pred_type(slice(22, 27), (1.0, 0.498, 0.055, 0.4)),\n"," 'nose': pred_type(slice(27, 31), (0.345, 0.239, 0.443, 0.4)),\n"," 'nostril': pred_type(slice(31, 36), (0.345, 0.239, 0.443, 0.4)),\n"," 'eye1': pred_type(slice(36, 42), (0.596, 0.875, 0.541, 0.3)),\n"," 'eye2': pred_type(slice(42, 48), (0.596, 0.875, 0.541, 0.3)),\n"," 'lips': pred_type(slice(48, 60), (0.596, 0.875, 0.541, 0.3)),\n"," 'teeth': pred_type(slice(60, 68), (0.596, 0.875, 0.541, 0.4))\n"," }\n","\n"," fig = plt.figure(figsize=fig_size)\n"," ax = fig.add_subplot(1, 1, 1)\n"," ax.imshow(img)\n"," ax.axis('off')\n","\n"," for face in landmarks:\n"," for pred_type in pred_types.values():\n"," ax.plot(\n"," face[pred_type.slice, 0],\n"," face[pred_type.slice, 1],\n"," color=pred_type.color, **plot_style\n"," )\n"," plt.show()\n","\n","\n","\n","import PIL.Image\n","import PIL.ImageFile\n","import numpy as np\n","import scipy.ndimage\n","\n","\n","def align_and_crop_face(\n"," img: Image.Image,\n"," landmarks: np.ndarray,\n"," expand: float = 1.0,\n"," output_size: int = 1024, \n"," transform_size: int = 4096,\n"," enable_padding: bool = True,\n","):\n"," # Parse landmarks.\n"," # pylint: disable=unused-variable\n"," lm = landmarks\n"," lm_chin = lm[0 : 17] # left-right\n"," lm_eyebrow_left = lm[17 : 22] # left-right\n"," lm_eyebrow_right = lm[22 : 27] # left-right\n"," lm_nose = lm[27 : 31] # top-down\n"," lm_nostrils = lm[31 : 36] # top-down\n"," lm_eye_left = lm[36 : 42] # left-clockwise\n"," lm_eye_right = lm[42 : 48] # left-clockwise\n"," lm_mouth_outer = lm[48 : 60] # left-clockwise\n"," lm_mouth_inner = lm[60 : 68] # left-clockwise\n","\n"," # Calculate auxiliary vectors.\n"," eye_left = np.mean(lm_eye_left, axis=0)\n"," eye_right = np.mean(lm_eye_right, axis=0)\n"," eye_avg = (eye_left + eye_right) * 0.5\n"," eye_to_eye = eye_right - eye_left\n"," mouth_left = lm_mouth_outer[0]\n"," mouth_right = lm_mouth_outer[6]\n"," mouth_avg = (mouth_left + mouth_right) * 0.5\n"," eye_to_mouth = mouth_avg - eye_avg\n","\n"," # Choose oriented crop rectangle.\n"," x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]\n"," x /= np.hypot(*x)\n"," x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)\n"," x *= expand\n"," y = np.flipud(x) * [-1, 1]\n"," c = eye_avg + eye_to_mouth * 0.1\n"," quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])\n"," qsize = np.hypot(*x) * 2\n","\n"," # Shrink.\n"," shrink = int(np.floor(qsize / output_size * 0.5))\n"," if shrink > 1:\n"," rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))\n"," img = img.resize(rsize, PIL.Image.ANTIALIAS)\n"," quad /= shrink\n"," qsize /= shrink\n","\n"," # Crop.\n"," border = max(int(np.rint(qsize * 0.1)), 3)\n"," crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))\n"," crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))\n"," if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:\n"," img = img.crop(crop)\n"," quad -= crop[0:2]\n","\n"," # Pad.\n"," pad = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))\n"," pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))\n"," if enable_padding and max(pad) > border - 4:\n"," pad = np.maximum(pad, int(np.rint(qsize * 0.3)))\n"," img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')\n"," h, w, _ = img.shape\n"," y, x, _ = np.ogrid[:h, :w, :1]\n"," mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w-1-x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h-1-y) / pad[3]))\n"," blur = qsize * 0.02\n"," img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)\n"," img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0)\n"," img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')\n"," quad += pad[:2]\n","\n"," # Transform.\n"," img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)\n"," if output_size < transform_size:\n"," img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)\n","\n"," return img"],"metadata":{"id":"BdtS4FgoZCeP","executionInfo":{"status":"ok","timestamp":1664352591104,"user_tz":-480,"elapsed":4,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}}},"execution_count":35,"outputs":[]},{"cell_type":"code","source":["! pip install paddlepaddle"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0x1Sms2lUBzg","executionInfo":{"status":"ok","timestamp":1664352594833,"user_tz":-480,"elapsed":3732,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"8511ce6f-7fb0-4958-fc8f-348cca70ac60"},"execution_count":36,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: paddlepaddle in /usr/local/lib/python3.7/dist-packages (2.3.2)\n","Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from paddlepaddle) (4.4.2)\n","Requirement already satisfied: protobuf<=3.20.0,>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from paddlepaddle) (3.17.3)\n","Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.7/dist-packages (from paddlepaddle) (1.21.6)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from paddlepaddle) (1.15.0)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from paddlepaddle) (7.1.2)\n","Requirement already satisfied: astor in /usr/local/lib/python3.7/dist-packages (from paddlepaddle) (0.8.1)\n","Requirement already satisfied: opt-einsum==3.3.0 in /usr/local/lib/python3.7/dist-packages (from paddlepaddle) (3.3.0)\n","Requirement already satisfied: requests>=2.20.0 in /usr/local/lib/python3.7/dist-packages (from paddlepaddle) (2.23.0)\n","Requirement already satisfied: paddle-bfloat==0.1.7 in /usr/local/lib/python3.7/dist-packages (from paddlepaddle) (0.1.7)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20.0->paddlepaddle) (3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20.0->paddlepaddle) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20.0->paddlepaddle) (2022.6.15)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20.0->paddlepaddle) (2.10)\n"]}]},{"cell_type":"code","source":["!pip install paddleseg"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WVHS4twdUChW","executionInfo":{"status":"ok","timestamp":1664352598858,"user_tz":-480,"elapsed":4028,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"6c2e5539-e69f-4e50-c019-3cdf901fb6cc"},"execution_count":37,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: paddleseg in /usr/local/lib/python3.7/dist-packages (2.6.0)\n","Requirement already satisfied: prettytable in /usr/local/lib/python3.7/dist-packages (from paddleseg) (3.4.1)\n","Requirement already satisfied: sklearn in /usr/local/lib/python3.7/dist-packages (from paddleseg) (0.0)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from paddleseg) (6.0)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from paddleseg) (1.7.3)\n","Requirement already satisfied: visualdl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from paddleseg) (2.4.1)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from paddleseg) (3.8.0)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from paddleseg) (4.6.0.66)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from paddleseg) (4.64.1)\n","Requirement already satisfied: protobuf>=3.11.0 in /usr/local/lib/python3.7/dist-packages (from visualdl>=2.0.0->paddleseg) (3.17.3)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from visualdl>=2.0.0->paddleseg) (1.3.5)\n","Requirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from visualdl>=2.0.0->paddleseg) (0.70.13)\n","Requirement already satisfied: Pillow>=7.0.0 in /usr/local/lib/python3.7/dist-packages (from visualdl>=2.0.0->paddleseg) (7.1.2)\n","Requirement already satisfied: bce-python-sdk in /usr/local/lib/python3.7/dist-packages (from visualdl>=2.0.0->paddleseg) (0.8.74)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from visualdl>=2.0.0->paddleseg) (21.3)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from visualdl>=2.0.0->paddleseg) (2.23.0)\n","Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from visualdl>=2.0.0->paddleseg) (1.15.0)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from visualdl>=2.0.0->paddleseg) (3.2.2)\n","Requirement already satisfied: flask>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from visualdl>=2.0.0->paddleseg) (1.1.4)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from visualdl>=2.0.0->paddleseg) (1.21.6)\n","Requirement already satisfied: Flask-Babel>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from visualdl>=2.0.0->paddleseg) (2.0.0)\n","Requirement already satisfied: click<8.0,>=5.1 in /usr/local/lib/python3.7/dist-packages (from flask>=1.1.1->visualdl>=2.0.0->paddleseg) (7.1.2)\n","Requirement already satisfied: Werkzeug<2.0,>=0.15 in /usr/local/lib/python3.7/dist-packages (from flask>=1.1.1->visualdl>=2.0.0->paddleseg) (1.0.1)\n","Requirement already satisfied: itsdangerous<2.0,>=0.24 in /usr/local/lib/python3.7/dist-packages (from flask>=1.1.1->visualdl>=2.0.0->paddleseg) (1.1.0)\n","Requirement already satisfied: Jinja2<3.0,>=2.10.1 in /usr/local/lib/python3.7/dist-packages (from flask>=1.1.1->visualdl>=2.0.0->paddleseg) (2.11.3)\n","Requirement already satisfied: Babel>=2.3 in /usr/local/lib/python3.7/dist-packages (from Flask-Babel>=1.0.0->visualdl>=2.0.0->paddleseg) (2.10.3)\n","Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from Flask-Babel>=1.0.0->visualdl>=2.0.0->paddleseg) (2022.2.1)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2<3.0,>=2.10.1->flask>=1.1.1->visualdl>=2.0.0->paddleseg) (2.0.1)\n","Requirement already satisfied: pycryptodome>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from bce-python-sdk->visualdl>=2.0.0->paddleseg) (3.15.0)\n","Requirement already satisfied: future>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from bce-python-sdk->visualdl>=2.0.0->paddleseg) (0.16.0)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->visualdl>=2.0.0->paddleseg) (0.11.0)\n","Requirement already 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prettytable->paddleseg) (0.2.5)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->prettytable->paddleseg) (3.8.1)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->visualdl>=2.0.0->paddleseg) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->visualdl>=2.0.0->paddleseg) (3.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->visualdl>=2.0.0->paddleseg) (2022.6.15)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->visualdl>=2.0.0->paddleseg) (1.24.3)\n","Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sklearn->paddleseg) (1.0.2)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn->paddleseg) (3.1.0)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn->paddleseg) (1.1.0)\n"]}]},{"cell_type":"code","source":["!git clone https://github.com/PaddlePaddle/PaddleSeg.git"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uekKYt1jUCje","executionInfo":{"status":"ok","timestamp":1664352615934,"user_tz":-480,"elapsed":17081,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"221dc120-7d1d-413b-ec72-78d0ad9f8d9f"},"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'PaddleSeg'...\n","remote: Enumerating objects: 20562, done.\u001b[K\n","remote: Counting objects: 100% (13/13), done.\u001b[K\n","remote: Compressing objects: 100% (12/12), done.\u001b[K\n","remote: Total 20562 (delta 3), reused 2 (delta 1), pack-reused 20549\u001b[K\n","Receiving objects: 100% (20562/20562), 345.93 MiB | 24.23 MiB/s, done.\n","Resolving deltas: 100% (13399/13399), done.\n"]}]},{"cell_type":"code","source":["%cd PaddleSeg/contrib/PP-HumanSeg/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dTrIgetVUCly","executionInfo":{"status":"ok","timestamp":1664352615935,"user_tz":-480,"elapsed":20,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"2024ca4c-7d75-4366-e3d1-a17e77ed5070"},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/PaddleSeg/contrib/PP-HumanSeg/src/PaddleSeg/contrib/PP-HumanSeg\n"]}]},{"cell_type":"code","source":["# 执行以下脚本下载所有Inference Model\n","!python src/download_inference_models.py"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OEwR0i14UCso","executionInfo":{"status":"ok","timestamp":1664352680797,"user_tz":-480,"elapsed":64876,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"98a127b3-9137-4f98-bd6a-46a5f2e52805"},"execution_count":40,"outputs":[{"output_type":"stream","name":"stdout","text":["Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/portrait_pp_humansegv1_lite_398x224_inference_model_with_softmax.zip\n","Downloading portrait_pp_humansegv1_lite_398x224_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Uncompress portrait_pp_humansegv1_lite_398x224_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/portrait_pp_humansegv2_lite_256x144_smaller/portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax.zip\n","Downloading portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Uncompress portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/human_pp_humansegv1_lite_192x192_inference_model_with_softmax.zip\n","Downloading human_pp_humansegv1_lite_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Uncompress human_pp_humansegv1_lite_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/human_pp_humansegv2_lite_192x192_inference_model_with_softmax.zip\n","Downloading human_pp_humansegv2_lite_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Uncompress human_pp_humansegv2_lite_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/human_pp_humansegv1_mobile_192x192_inference_model_with_softmax.zip\n","Downloading human_pp_humansegv1_mobile_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Uncompress human_pp_humansegv1_mobile_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Connecting to https://paddleseg.bj.bcebos.com/dygraph/pp_humanseg_v2/human_pp_humansegv2_mobile_192x192_inference_model_with_softmax.zip\n","Downloading human_pp_humansegv2_mobile_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Uncompress human_pp_humansegv2_mobile_192x192_inference_model_with_softmax.zip\n","[==================================================] 100.00%\n","Download inference models finished.\n"]}]},{"cell_type":"code","source":["# 可选\n","# 下载测试数据集\n","# !python src/download_data.py"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0sp1SpEOUCvB","executionInfo":{"status":"ok","timestamp":1664352697267,"user_tz":-480,"elapsed":16482,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"31d6e101-4206-48c3-eab8-d05940f8a11a"},"execution_count":41,"outputs":[{"output_type":"stream","name":"stdout","text":["Connecting to https://paddleseg.bj.bcebos.com/humanseg/data/mini_supervisely.zip\n","Downloading mini_supervisely.zip\n","[= ] 3.89%Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/urllib3/response.py\", line 362, in _error_catcher\n"," yield\n"," File \"/usr/local/lib/python3.7/dist-packages/urllib3/response.py\", line 444, in read\n"," data = self._fp.read(amt)\n"," File \"/usr/lib/python3.7/http/client.py\", line 465, in read\n"," n = self.readinto(b)\n"," File \"/usr/lib/python3.7/http/client.py\", line 509, in readinto\n"," n = self.fp.readinto(b)\n"," File \"/usr/lib/python3.7/socket.py\", line 589, in readinto\n"," return self._sock.recv_into(b)\n"," File \"/usr/lib/python3.7/ssl.py\", line 1071, in recv_into\n"," return self.read(nbytes, buffer)\n"," File \"/usr/lib/python3.7/ssl.py\", line 929, in read\n"," return self._sslobj.read(len, buffer)\n","KeyboardInterrupt\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"src/download_data.py\", line 32, in \n"," url=url, savepath=data_path, extrapath=data_path)\n"," File \"/usr/local/lib/python3.7/dist-packages/paddleseg/utils/download.py\", line 150, in download_file_and_uncompress\n"," _download_file(url, savepath, print_progress)\n"," File \"/usr/local/lib/python3.7/dist-packages/paddleseg/utils/download.py\", line 56, in _download_file\n"," for data in r.iter_content(chunk_size=4096):\n"," File \"/usr/local/lib/python3.7/dist-packages/requests/models.py\", line 751, in generate\n"," for chunk in self.raw.stream(chunk_size, decode_content=True):\n"," File \"/usr/local/lib/python3.7/dist-packages/urllib3/response.py\", line 496, in stream\n"," data = self.read(amt=amt, decode_content=decode_content)\n"," File \"/usr/local/lib/python3.7/dist-packages/urllib3/response.py\", line 461, in read\n"," raise IncompleteRead(self._fp_bytes_read, self.length_remaining)\n"," File \"/usr/lib/python3.7/contextlib.py\", line 130, in __exit__\n"," self.gen.throw(type, value, traceback)\n"," File \"/usr/local/lib/python3.7/dist-packages/urllib3/response.py\", line 393, in _error_catcher\n"," self._original_response.close()\n"," File \"/usr/lib/python3.7/http/client.py\", line 426, in close\n"," self._close_conn()\n"," File \"/usr/lib/python3.7/http/client.py\", line 419, in _close_conn\n"," fp.close()\n"," File \"/usr/lib/python3.7/socket.py\", line 658, in close\n"," io.RawIOBase.close(self)\n","KeyboardInterrupt\n"]}]},{"cell_type":"code","source":["%cd src"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WaQ54aEHY1f3","executionInfo":{"status":"ok","timestamp":1664352697267,"user_tz":-480,"elapsed":5,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"4783985c-3e26-4227-e462-52efc0b0662f"},"execution_count":42,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/PaddleSeg/contrib/PP-HumanSeg/src/PaddleSeg/contrib/PP-HumanSeg/src\n"]}]},{"cell_type":"code","source":["# ima_path与bg_img_path应为自己的图片路径，注意根据演示路径替换自己本地的路径\n","args = { 'config': '/content/PaddleSeg/contrib/PP-HumanSeg/inference_models/portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax/deploy.yaml',\n"," 'img_path': '/content/PaddleSeg/contrib/PP-HumanSeg/src/1.jpg',\n"," 'bg_img_path': '/content/PaddleSeg/contrib/PP-HumanSeg/data/images/2.jpg',\n"," 'save_dir': '/content/PaddleSeg/contrib/PP-HumanSeg/data/1.jpg',\n"," 'use_gpu': True,\n"," 'test_speed' : False,\n"," 'use_optic_flow' : False,\n"," 'use_post_process' : False\n"," }"],"metadata":{"id":"DKM2Y3IWUCz8","executionInfo":{"status":"ok","timestamp":1664352697267,"user_tz":-480,"elapsed":3,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}}},"execution_count":43,"outputs":[]},{"cell_type":"code","source":["# 先动漫化后增加背景效果更佳\n","import requests\n","\n","# 加载网络或本地文件\n","img = Image.open(args['img_path']).convert(\"RGB\")\n","# img = Image.open(\"/content/sample.jpg\").convert(\"RGB\")\n","\n","face_detector = get_dlib_face_detector()\n","landmarks = face_detector(img)\n","for landmark in landmarks:\n"," face = align_and_crop_face(img, landmark, expand=1.3)\n"," p_face = face2paint(model=model, img=face, size=512)\n"," # display(p_face)\n"," # p_face.save('1.png') # 此输出为对比图片\n"," # 裁剪为需要的部分输出\n"," x_, y_ = p_face.size\n"," out = p_face.crop((int(x_/2), 0, x_, y_))\n"," # display(out)\n"," out.save(args['img_path'])"],"metadata":{"id":"QEK24X44ZJ4X","executionInfo":{"status":"ok","timestamp":1664352719767,"user_tz":-480,"elapsed":22502,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}}},"execution_count":44,"outputs":[]},{"cell_type":"code","source":["import argparse\n","import os\n","import sys\n","\n","import cv2\n","import numpy as np\n","from tqdm import tqdm\n","\n","# __dir__ = os.path.dirname(os.path.abspath(__file__))\n","# sys.path.append(os.path.abspath(os.path.join(__dir__, '../../../')))\n","from paddleseg.utils import get_sys_env, logger, get_image_list\n","\n","import codecs\n","import os\n","import sys\n","import time\n","\n","import yaml\n","import numpy as np\n","import cv2\n","import paddle\n","from paddle.inference import create_predictor, PrecisionType\n","from paddle.inference import Config as PredictConfig\n","\n","\n","import paddleseg.transforms as T\n","from paddleseg.core.infer import reverse_transform\n","from paddleseg.cvlibs import manager\n","from paddleseg.utils import TimeAverager\n","\n","from optic_flow_process import optic_flow_process\n","\n","from PIL import Image\n","\n","\n","def get_bg_img(bg_img_path, img_shape):\n"," if bg_img_path is None:\n"," bg = 255 * np.ones(img_shape)\n"," elif not os.path.exists(bg_img_path):\n"," raise Exception('The --bg_img_path is not existed: {}'.format(\n"," bg_img_path))\n"," else:\n"," bg = cv2.imread(bg_img_path)\n"," return bg\n","\n","\n","def makedirs(save_dir):\n"," dirname = save_dir if os.path.isdir(save_dir) else \\\n"," os.path.dirname(save_dir)\n"," if not os.path.exists(dirname):\n"," os.makedirs(dirname)\n","\n","\n","def seg_image(args):\n"," print(args)\n"," assert os.path.exists(args['img_path']), \\\n"," \"The --img_path is not existed: {}.\".format(args['img_path'])\n","\n"," logger.info(\"Input: image\")\n"," logger.info(\"Create predictor...\")\n"," predictor = Predictor(args)\n","\n"," logger.info(\"Start predicting...\")\n"," img = cv2.imread(args['img_path'])\n"," bg_img = get_bg_img(args['bg_img_path'], img.shape)\n"," out_img = predictor.run(img, bg_img)\n"," cv2.imwrite(args['save_dir'], out_img)\n"," im = Image.open(args['save_dir']) \n"," display(im)\n","\n","class Predictor:\n"," def __init__(self, args):\n"," self.args = args\n"," self.cfg = DeployConfig(args['config'], False)\n"," self.compose = T.Compose(self.cfg.transforms)\n","\n"," pred_cfg = PredictConfig(self.cfg.model, self.cfg.params)\n"," pred_cfg.disable_glog_info()\n"," if self.args['use_gpu']:\n"," pred_cfg.enable_use_gpu(100, 0)\n","\n"," self.predictor = create_predictor(pred_cfg)\n"," if self.args['test_speed']:\n"," self.cost_averager = TimeAverager()\n","\n"," if args['use_optic_flow']:\n","\n"," self.disflow = cv2.DISOpticalFlow_create(\n"," cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST)\n"," width, height = self.cfg.target_size()\n"," self.prev_gray = np.zeros((height, width), np.uint8)\n"," self.prev_cfd = np.zeros((height, width), np.float32)\n"," self.is_first_frame = True\n","\n"," def run(self, img, bg):\n"," input_names = self.predictor.get_input_names()\n"," input_handle = self.predictor.get_input_handle(input_names[0])\n","\n"," data = self.compose({'img': img})\n"," input_data = np.array([data['img']])\n","\n"," input_handle.reshape(input_data.shape)\n"," input_handle.copy_from_cpu(input_data)\n"," if self.args['test_speed']:\n"," start = time.time()\n","\n"," self.predictor.run()\n","\n"," if self.args['test_speed']:\n"," self.cost_averager.record(time.time() - start)\n"," output_names = self.predictor.get_output_names()\n"," output_handle = self.predictor.get_output_handle(output_names[0])\n"," output = output_handle.copy_to_cpu()\n","\n"," return self.postprocess(output, img, data, bg)\n","\n"," def postprocess(self, pred_img, origin_img, data, bg):\n"," trans_info = data['trans_info']\n"," score_map = pred_img[0, 1, :, :]\n","\n"," # post process\n"," if self.args['use_post_process']:\n"," mask_original = score_map.copy()\n"," mask_original = (mask_original * 255).astype(\"uint8\")\n"," _, mask_thr = cv2.threshold(mask_original, 240, 1,\n"," cv2.THRESH_BINARY)\n"," kernel_erode = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))\n"," kernel_dilate = cv2.getStructuringElement(cv2.MORPH_CROSS, (25, 25))\n"," mask_erode = cv2.erode(mask_thr, kernel_erode)\n"," mask_dilate = cv2.dilate(mask_erode, kernel_dilate)\n"," score_map *= mask_dilate\n","\n"," # optical flow\n"," if self.args['use_optic_flow']:\n"," score_map = 255 * score_map\n"," cur_gray = cv2.cvtColor(origin_img, cv2.COLOR_BGR2GRAY)\n"," cur_gray = cv2.resize(cur_gray,\n"," (pred_img.shape[-1], pred_img.shape[-2]))\n"," optflow_map = optic_flow_process(cur_gray, score_map, self.prev_gray, self.prev_cfd, \\\n"," self.disflow, self.is_first_frame)\n"," self.prev_gray = cur_gray.copy()\n"," self.prev_cfd = optflow_map.copy()\n"," self.is_first_frame = False\n"," score_map = optflow_map / 255.\n","\n"," score_map = score_map[np.newaxis, np.newaxis, ...]\n"," score_map = reverse_transform(\n"," paddle.to_tensor(score_map), trans_info, mode='bilinear')\n"," alpha = np.transpose(score_map.numpy().squeeze(1), [1, 2, 0])\n","\n"," h, w, _ = origin_img.shape\n"," bg = cv2.resize(bg, (w, h))\n"," if bg.ndim == 2:\n"," bg = bg[..., np.newaxis]\n","\n"," out = (alpha * origin_img + (1 - alpha) * bg).astype(np.uint8)\n"," return out\n","\n","class DeployConfig:\n"," def __init__(self, path, vertical_screen):\n"," with codecs.open(path, 'r', 'utf-8') as file:\n"," self.dic = yaml.load(file, Loader=yaml.FullLoader)\n","\n"," [width, height] = self.dic['Deploy']['transforms'][0]['target_size']\n"," if vertical_screen and width > height:\n"," self.dic['Deploy']['transforms'][0][\n"," 'target_size'] = [height, width]\n","\n"," self._transforms = self._load_transforms(self.dic['Deploy'][\n"," 'transforms'])\n"," self._dir = os.path.dirname(path)\n","\n"," @property\n"," def transforms(self):\n"," return self._transforms\n","\n"," @property\n"," def model(self):\n"," return os.path.join(self._dir, self.dic['Deploy']['model'])\n","\n"," @property\n"," def params(self):\n"," return os.path.join(self._dir, self.dic['Deploy']['params'])\n","\n"," def target_size(self):\n"," [width, height] = self.dic['Deploy']['transforms'][0]['target_size']\n"," return [width, height]\n","\n"," def _load_transforms(self, t_list):\n"," com = manager.TRANSFORMS\n"," transforms = []\n"," for t in t_list:\n"," ctype = t.pop('type')\n"," transforms.append(com[ctype](**t))\n","\n"," return transforms\n","\n","\n","\n","\n"],"metadata":{"id":"Lgw48SeQUCxO","executionInfo":{"status":"ok","timestamp":1664353791293,"user_tz":-480,"elapsed":352,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}}},"execution_count":48,"outputs":[]},{"cell_type":"code","source":["seg_image(args)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":622},"id":"fiTsjsZEeDP8","executionInfo":{"status":"ok","timestamp":1664353796276,"user_tz":-480,"elapsed":1338,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"cae803e9-5eaa-4eba-99e3-2f2df94eb07e"},"execution_count":49,"outputs":[{"output_type":"stream","name":"stdout","text":["{'config': '/content/PaddleSeg/contrib/PP-HumanSeg/inference_models/portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax/deploy.yaml', 'img_path': '/content/PaddleSeg/contrib/PP-HumanSeg/src/1.jpg', 'bg_img_path': '/content/PaddleSeg/contrib/PP-HumanSeg/data/images/2.jpg', 'save_dir': '/content/PaddleSeg/contrib/PP-HumanSeg/data/1.jpg', 'use_gpu': True, 'test_speed': False, 'use_optic_flow': False, 'use_post_process': False}\n","2022-09-28 08:29:55 [INFO]\tInput: image\n","2022-09-28 08:29:55 [INFO]\tCreate predictor...\n","2022-09-28 08:29:56 [INFO]\tStart predicting...\n"]},{"output_type":"display_data","data":{"text/plain":[""],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["%pwd"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"uV8IyX__2v9V","executionInfo":{"status":"ok","timestamp":1664352720167,"user_tz":-480,"elapsed":6,"user":{"displayName":"shing LIN","userId":"15916362134103428022"}},"outputId":"48aa0e23-0c48-4f3d-e5a2-a855475ac106"},"execution_count":47,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/PaddleSeg/contrib/PP-HumanSeg/src/PaddleSeg/contrib/PP-HumanSeg/src'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":47}]}]} \ No newline at end of file diff --git a/aihub/deep-learning/face-paint/seg_demo.py b/aihub/deep-learning/face-paint/seg_demo.py new file mode 100644 index 00000000..8c83cffc --- /dev/null +++ b/aihub/deep-learning/face-paint/seg_demo.py @@ -0,0 +1,300 @@ +import argparse +import base64 +import datetime +import os +import sys + +import cv2 +import numpy as np +from tqdm import tqdm + + +from paddleseg.utils import get_sys_env, logger, get_image_list + +from infer import Predictor + +import os +import dlib +import collections +from typing import Union, List +import numpy as np +from PIL import Image +import matplotlib.pyplot as plt +import torch +from PIL import Image + + +def get_bg_img(bg_img_path, img_shape): + if bg_img_path is None: + bg = 255 * np.ones(img_shape) + elif not os.path.exists(bg_img_path): + raise Exception('The --bg_img_path is not existed: {}'.format( + bg_img_path)) + else: + bg = cv2.imread(bg_img_path) + return bg + + +def makedirs(save_dir): + dirname = save_dir if os.path.isdir(save_dir) else \ + os.path.dirname(save_dir) + if not os.path.exists(dirname): + os.makedirs(dirname) + + +def seg_image(args): + assert os.path.exists(args['img_path']), \ + "The --img_path is not existed: {}.".format(args['img_path']) + + logger.info("Input: image") + logger.info("Create predictor...") + predictor = Predictor(args) + + logger.info("Start predicting...") + img = cv2.imread(args['re_save_path']) + bg_img = get_bg_img(args['bg_img_path'], img.shape) + out_img = predictor.run(img, bg_img) + # print(type(out_img)) + cv2.imwrite(args['save_dir'], out_img) + file = open(args['save_dir'], 'rb') + base64_str = base64.b64encode(file.read()).decode('utf-8') + print(len(base64_str)) + return base64_str + # img_ = Image.open(out_img) + # print(img_) + + + +def get_dlib_face_detector(predictor_path: str = "shape_predictor_68_face_landmarks.dat"): + if not os.path.isfile(predictor_path): + model_file = "shape_predictor_68_face_landmarks.dat.bz2" + os.system(f"wget http://dlib.net/files/{model_file}") + os.system(f"bzip2 -dk {model_file}") + + detector = dlib.get_frontal_face_detector() + shape_predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat') + + def detect_face_landmarks(img: Union[Image.Image, np.ndarray]): + if isinstance(img, Image.Image): + img = np.array(img) + faces = [] + dets = detector(img) + for d in dets: + shape = shape_predictor(img, d) + faces.append(np.array([[v.x, v.y] for v in shape.parts()])) + return faces + + return detect_face_landmarks + + +def display_facial_landmarks( + img: Image, + landmarks: List[np.ndarray], + fig_size=[15, 15] +): + plot_style = dict( + marker='o', + markersize=4, + linestyle='-', + lw=2 + ) + pred_type = collections.namedtuple('prediction_type', ['slice', 'color']) + pred_types = { + 'face': pred_type(slice(0, 17), (0.682, 0.780, 0.909, 0.5)), + 'eyebrow1': pred_type(slice(17, 22), (1.0, 0.498, 0.055, 0.4)), + 'eyebrow2': pred_type(slice(22, 27), (1.0, 0.498, 0.055, 0.4)), + 'nose': pred_type(slice(27, 31), (0.345, 0.239, 0.443, 0.4)), + 'nostril': pred_type(slice(31, 36), (0.345, 0.239, 0.443, 0.4)), + 'eye1': pred_type(slice(36, 42), (0.596, 0.875, 0.541, 0.3)), + 'eye2': pred_type(slice(42, 48), (0.596, 0.875, 0.541, 0.3)), + 'lips': pred_type(slice(48, 60), (0.596, 0.875, 0.541, 0.3)), + 'teeth': pred_type(slice(60, 68), (0.596, 0.875, 0.541, 0.4)) + } + + fig = plt.figure(figsize=fig_size) + ax = fig.add_subplot(1, 1, 1) + ax.imshow(img) + ax.axis('off') + + for face in landmarks: + for pred_type in pred_types.values(): + ax.plot( + face[pred_type.slice, 0], + face[pred_type.slice, 1], + color=pred_type.color, **plot_style + ) + plt.show() + + +import PIL.Image +import PIL.ImageFile +import numpy as np +import scipy.ndimage + + +def align_and_crop_face( + img: Image.Image, + landmarks: np.ndarray, + expand: float = 1.0, + output_size: int = 1024, + transform_size: int = 4096, + enable_padding: bool = True, +): + # Parse landmarks. + # pylint: disable=unused-variable + lm = landmarks + lm_chin = lm[0: 17] # left-right + lm_eyebrow_left = lm[17: 22] # left-right + lm_eyebrow_right = lm[22: 27] # left-right + lm_nose = lm[27: 31] # top-down + lm_nostrils = lm[31: 36] # top-down + lm_eye_left = lm[36: 42] # left-clockwise + lm_eye_right = lm[42: 48] # left-clockwise + lm_mouth_outer = lm[48: 60] # left-clockwise + lm_mouth_inner = lm[60: 68] # left-clockwise + + # Calculate auxiliary vectors. + eye_left = np.mean(lm_eye_left, axis=0) + eye_right = np.mean(lm_eye_right, axis=0) + eye_avg = (eye_left + eye_right) * 0.5 + eye_to_eye = eye_right - eye_left + mouth_left = lm_mouth_outer[0] + mouth_right = lm_mouth_outer[6] + mouth_avg = (mouth_left + mouth_right) * 0.5 + eye_to_mouth = mouth_avg - eye_avg + + # Choose oriented crop rectangle. + x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] + x /= np.hypot(*x) + x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) + x *= expand + y = np.flipud(x) * [-1, 1] + c = eye_avg + eye_to_mouth * 0.1 + quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) + qsize = np.hypot(*x) * 2 + + # Shrink. + shrink = int(np.floor(qsize / output_size * 0.5)) + if shrink > 1: + rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink))) + img = img.resize(rsize, PIL.Image.ANTIALIAS) + quad /= shrink + qsize /= shrink + + # Crop. + border = max(int(np.rint(qsize * 0.1)), 3) + crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), + int(np.ceil(max(quad[:, 1])))) + crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), + min(crop[3] + border, img.size[1])) + if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: + img = img.crop(crop) + quad -= crop[0:2] + + # Pad. + pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), + int(np.ceil(max(quad[:, 1])))) + pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), + max(pad[3] - img.size[1] + border, 0)) + if enable_padding and max(pad) > border - 4: + pad = np.maximum(pad, int(np.rint(qsize * 0.3))) + img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') + h, w, _ = img.shape + y, x, _ = np.ogrid[:h, :w, :1] + mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), + 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3])) + blur = qsize * 0.02 + img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) + img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) + img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') + quad += pad[:2] + + # Transform. + img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR) + if output_size < transform_size: + img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) + + return img + + +def start( + config=r'/home/JLWL/PaddleSeg-release-2.6/contrib/PP-HumanSeg/src/inference_models/portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax/deploy.yaml', + img_path=r'/home/JLWL/PaddleSeg-release-2.6/contrib/PP-HumanSeg/src/data/images/1.jpg', + bg_img_path=r'/home/JLWL/PaddleSeg-release-2.6/contrib/PP-HumanSeg/src/data/images/2.jpg', + re_save_path=r'temp/1_.jpg', + save_dir=r'temp/1.jpg', + use_gpu=True, + test_speed=False, use_optic_flow=False, use_post_process=False): + args = { + 'config': config, + 'img_path': img_path, + 'bg_img_path': bg_img_path, + 're_save_path': re_save_path, + 'save_dir': save_dir, + 'use_gpu': use_gpu, + 'test_speed': test_speed, + 'use_optic_flow': use_optic_flow, + 'use_post_process': use_post_process + } + print(type(args)) + + # 先动漫化后增加背景效果更佳 + + # 加载网络或本地文件 + save_ = datetime.datetime.now().strftime('%Y%m%d%H%M%S') + os.mkdir(save_) + device = "cuda" if torch.cuda.is_available() else "cpu" + model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", device=device).eval() + face2paint = torch.hub.load("bryandlee/animegan2-pytorch:main", "face2paint", device=device, side_by_side=True) + img = Image.open(args['img_path']).convert("RGB") + # img = Image.open("/content/sample.jpg").convert("RGB") + + face_detector = get_dlib_face_detector() + landmarks = face_detector(img) + out = '' + for landmark in landmarks: + face = align_and_crop_face(img, landmark, expand=1.3) + p_face = face2paint(model=model, img=face, size=512) + # display(p_face) + # p_face.save('1.png') # 此输出为对比图片 + # 裁剪为需要的部分输出 + x_, y_ = p_face.size + out = p_face.crop((int(x_ / 2), 0, x_, y_)) + img_ = out + x, y = img_.size + print(x, y) + all_list = [] + for i in range(5): + newIm = Image.new('RGB', (int(x * 1.5), int(y * 1.5)), 'white') + newIm.paste(img_, (int(x * 0.5), int(y * 0.45))) + # args['re_save_path'] = newIm + newIm.save(args['re_save_path']) + base64_ = seg_image(args) + all_list.append({f'{i}': base64_}) + + +if __name__ == "__main__": + image_path = r'/home/JLWL/PaddleSeg-release-2.6/contrib/PP-HumanSeg/src/data/images/human.jpg' + file_after = open(image_path, 'rb') + base64_after_str = base64.b64encode(file_after.read()).decode('utf-8') + print(len(base64_after_str)) + imgdata = base64.b64decode(base64_after_str) + # 将图片保存为文件 + if os.path.exists('temp'): + pass + else: + os.mkdir('temp') + name_ = datetime.datetime.now().strftime('%Y%m%d%H%M%S') + new_image_path = f'temp/{name_}.jpg' + with open(new_image_path, 'wb') as f: + f.write(imgdata) + + start( + # config=r'E:\PaddleSeg-release-2.6\contrib\PP-HumanSeg\src\inference_models\portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax\deploy.yaml', + img_path=new_image_path, + # bg_img_path=r'E:\PaddleSeg-release-2.6\contrib\PP-HumanSeg\src\data\images\bg_1.jpg', + # re_save_path=r'E:\PaddleSeg-release-2.6\contrib\PP-HumanSeg\src\data\images\_1.jpg', + # save_dir=r'E:\PaddleSeg-release-2.6\contrib\PP-HumanSeg\src\data\images_result\1.jpg', + # use_gpu=True, + # test_speed=False, use_optic_flow=False, use_post_process=False) + )