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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qbM01EucvW58"
},
"outputs": [],
"source": [
"!pip install controlnet-aux==0.0.7\n",
"!pip install -U openmim\n",
"!pip install cog\n",
"!pip install mediapipe\n",
"!mim install mmengine\n",
"!mim install \"mmcv>=2.0.1\"\n",
"!mim install \"mmdet>=3.1.0\"\n",
"!mim install \"mmpose>=1.1.0\""
]
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"uploaded = files.upload()"
],
"metadata": {
"id": "rvxvKxkiR8ih"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title break video down into frames\n",
"import cv2\n",
"\n",
"# Open the video file\n",
"cap = cv2.VideoCapture('/content/a.mp4')\n",
"\n",
"i = 0\n",
"while(cap.isOpened()):\n",
" ret, frame = cap.read()\n",
"\n",
" if ret == False:\n",
" break\n",
"\n",
" # Save each frame of the video\n",
" cv2.imwrite('/content/frames/frame_' + str(i) + '.jpg', frame)\n",
"\n",
" i += 1\n",
"\n",
"cap.release()\n",
"cv2.destroyAllWindows()"
],
"metadata": {
"id": "Kw0hIeYnvjLV"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#==========Interpolate the pose frames==========\n",
"!pip install moviepy"
],
"metadata": {
"id": "jTIwuo4ESGBw"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title interpolate processed frames (best to keep fps same as input video)\n",
"!ffmpeg -r 8 -i /content/test/frame_%d.jpg -c:v libx264 -vf \"fps=8,format=yuv420p\" testpose.mp4\n"
],
"metadata": {
"id": "8kUk-kFPwzmq"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#=======AVAILABLE PROCESSORS========\n",
"# load processor from processor_id\n",
"# options are:\n",
"# [\"canny\", \"depth_leres\", \"depth_leres++\", \"depth_midas\", \"depth_zoe\", \"lineart_anime\",\n",
"# \"lineart_coarse\", \"lineart_realistic\", \"mediapipe_face\", \"mlsd\", \"normal_bae\", \"normal_midas\",\n",
"# \"openpose\", \"openpose_face\", \"openpose_faceonly\", \"openpose_full\", \"openpose_hand\",\n",
"# \"scribble_hed, \"scribble_pidinet\", \"shuffle\", \"softedge_hed\", \"softedge_hedsafe\",\n",
"# \"softedge_pidinet\", \"softedge_pidsafe\", \"dwpose\"]"
],
"metadata": {
"id": "l5aAanvtyMz9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title simply change the processor under 'processor' at the bottom, may need to add if not available, see above (goto v1 notebook if errors start)\n",
"#=======AVAILABLE PROCESSORS========\n",
"# load processor from processor_id\n",
"# options are:\n",
"# [\"canny\", \"depth_leres\", \"depth_leres++\", \"depth_midas\", \"depth_zoe\", \"lineart_anime\",\n",
"# \"lineart_coarse\", \"lineart_realistic\", \"mediapipe_face\", \"mlsd\", \"normal_bae\", \"normal_midas\",\n",
"# \"openpose\", \"openpose_face\", \"openpose_faceonly\", \"openpose_full\", \"openpose_hand\",\n",
"# \"scribble_hed, \"scribble_pidinet\", \"shuffle\", \"softedge_hed\", \"softedge_hedsafe\",\n",
"# \"softedge_pidinet\", \"softedge_pidsafe\", \"dwpose\"]\n",
"import torch\n",
"import os\n",
"from typing import List\n",
"from cog import BasePredictor, Input, Path\n",
"from PIL import Image\n",
"from io import BytesIO\n",
"import time\n",
"from tqdm import tqdm\n",
"from controlnet_aux.processor import Processor\n",
"from controlnet_aux import (\n",
" HEDdetector,\n",
" MidasDetector,\n",
" MLSDdetector,\n",
" OpenposeDetector,\n",
" PidiNetDetector,\n",
" NormalBaeDetector,\n",
" LineartDetector,\n",
" LineartAnimeDetector,\n",
" CannyDetector,\n",
" ContentShuffleDetector,\n",
" ZoeDetector,\n",
" MediapipeFaceDetector,\n",
" SamDetector,\n",
" LeresDetector,\n",
" DWposeDetector,\n",
")\n",
"\n",
"#Processor = processor\n",
"image_dir = '/content/frames'\n",
"\n",
"class Predictor(BasePredictor):\n",
" def setup(self) -> None:\n",
" \"\"\"Load the model into memory to make running multiple predictions efficient\"\"\"\n",
"\n",
" self.annotators = {\n",
" \"canny\": CannyDetector(),\n",
" \"content\": ContentShuffleDetector(),\n",
" \"face_detector\": MediapipeFaceDetector(),\n",
" \"hed\": self.initialize_detector(HEDdetector),\n",
" \"midas\": self.initialize_detector(MidasDetector),\n",
" \"mlsd\": self.initialize_detector(MLSDdetector),\n",
" \"open_pose\": self.initialize_detector(OpenposeDetector),\n",
" \"pidi\": self.initialize_detector(PidiNetDetector),\n",
" \"normal_bae\": self.initialize_detector(NormalBaeDetector),\n",
" \"lineart\": self.initialize_detector(LineartDetector),\n",
" \"lineart_anime\": self.initialize_detector(LineartAnimeDetector),\n",
" # \"zoe\": self.initialize_detector(ZoeDetector),\n",
"\n",
"\n",
" # \"mobile_sam\": self.initialize_detector(\n",
" # SamDetector,\n",
" # model_name=\"dhkim2810/MobileSAM\",\n",
" # model_type=\"vit_t\",\n",
" # filename=\"mobile_sam.pt\",\n",
" # ),\n",
" \"leres\": self.initialize_detector(LeresDetector),\n",
" }\n",
"\n",
" torch.device(\"cuda\")\n",
"\n",
" def initialize_detector(\n",
" self, detector_class, model_name=\"lllyasviel/Annotators\", **kwargs\n",
" ):\n",
" return detector_class.from_pretrained(\n",
" model_name,\n",
" cache_dir=\"model_cache\",\n",
" **kwargs,\n",
" )\n",
"\n",
" def process_images(self, image_dir: str) -> List[Path]:\n",
" # Start time for overall processing\n",
" start_time = time.time()\n",
"\n",
" # Load all images into memory\n",
" images = [Image.open(os.path.join(image_dir, image_name)).convert(\"RGB\").resize((512, 512)) for image_name in os.listdir(image_dir)]\n",
"\n",
" paths = []\n",
"\n",
" def predict(\n",
" self,\n",
" image_dir: str = Input(\n",
" default=\"/content/frames\",\n",
" description=\"Directory containing the images to be processed\"\n",
" )\n",
"):\n",
"\n",
" canny: bool = Input(\n",
" default=True,\n",
" description=\"Run canny edge detection\",\n",
" ),\n",
" content: bool = Input(\n",
" default=True,\n",
" description=\"Run content shuffle detection\",\n",
" ),\n",
" face_detector: bool = Input(\n",
" default=True,\n",
" description=\"Run face detection\",\n",
" ),\n",
" hed: bool = Input(\n",
" default=True,\n",
" description=\"Run HED detection\",\n",
" ),\n",
" midas: bool = Input(\n",
" default=True,\n",
" description=\"Run Midas detection\",\n",
" ),\n",
" mlsd: bool = Input(\n",
" default=True,\n",
" description=\"Run MLSD detection\",\n",
" ),\n",
" open_pose: bool = Input(\n",
" default=True,\n",
" description=\"Run Openpose detection\",\n",
" ),\n",
" pidi: bool = Input(\n",
" default=True,\n",
" description=\"Run PidiNet detection\",\n",
" ),\n",
" normal_bae: bool = Input(\n",
" default=True,\n",
" description=\"Run NormalBae detection\",\n",
" ),\n",
" lineart: bool = Input(\n",
" default=True,\n",
" description=\"Run Lineart detection\",\n",
" ),\n",
" lineart_anime: bool = Input(\n",
" default=True,\n",
" description=\"Run LineartAnime detection\",\n",
"\n",
" ),\n",
" leres: bool = Input(\n",
" default=True,\n",
" description=\"Run Leres detection\",\n",
" ),\n",
"\n",
"\n",
" # Load image\n",
" # Load all images into memory\n",
" start_time = time.time() # Start time for overall processing\n",
" images = [Image.open(os.path.join(image_dir, image_name)).convert(\"RGB\").resize((512, 512)) for image_name in os.listdir(image_dir)]\n",
"\n",
" paths = []\n",
" annotator_inputs = {\n",
" \"canny\": canny,\n",
" \"content\": content,\n",
" \"face_detector\": face_detector,\n",
" \"hed\": hed,\n",
" \"midas\": midas,\n",
" \"mlsd\": mlsd,\n",
" \"open_pose\": open_pose,\n",
" \"pidi\": pidi,\n",
" \"normal_bae\": normal_bae,\n",
" \"lineart\": lineart,\n",
" \"lineart_anime\": lineart_anime, \"openpose_full\": openpose_full,\n",
"\n",
" \"leres\": leres,\n",
" }\n",
" for annotator, run_annotator in annotator_inputs.items():\n",
" if run_annotator:\n",
" processed_image = self.process_image(image, annotator)\n",
" #processed_image.save(f\"/tmp/{annotator}.png\")\n",
" processed_path = f'/content/test1/{image_name}'\n",
"\n",
" return paths\n",
"\n",
"import time\n",
"from tqdm import tqdm\n",
"\n",
"# Load images and paths\n",
"images = []\n",
"image_paths = []\n",
"for name in os.listdir(image_dir):\n",
" path = os.path.join(image_dir, name)\n",
" image = Image.open(path)\n",
"\n",
" images.append(image)\n",
" image_paths.append(path)\n",
"\n",
"# Process images\n",
"processed = [\n",
" Processor(\"openpose_full\") for path in tqdm(image_paths)\n",
"]\n",
"\n",
"# Save processed\n",
"import os\n",
"from PIL import Image\n",
"\n",
"# Get a list of filenames in /content/frames\n",
"filenames = os.listdir('/content/frames')\n",
"\n",
"# Save processed\n",
"for filename in filenames:\n",
"\n",
" # Get the full path of the image file\n",
" image_path = os.path.join('/content/frames', filename)\n",
"\n",
" # Load the image\n",
" image = Image.open(image_path)\n",
"\n",
" # Process image\n",
" # Process all images with progress bar\n",
" processed_image = processor(image, to_pil=True)\n",
"\n",
" # Extract original name\n",
" original_name = filename.split('.')[0]\n",
"\n",
" # Save image\n",
" processed_path = f'/content/test2/{original_name}.png'\n",
" processed_image.save(processed_path)\n",
"\n"
],
"metadata": {
"id": "qgKAWKrBL5d2"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"@#title for seeing whats in controlnet_aux\n",
"dir(controlnet_aux)"
],
"metadata": {
"id": "X08c_PPKTQiq"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!zip -r nameof.zip <location of files and folder>"
],
"metadata": {
"id": "Oax1BHwYTZog"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FaF3RdKdaFa8"
},
"outputs": [],
"source": [
"#@title Login to HuggingFace 🤗\n",
"\n",
"#@markdown You need to accept the model license before downloading or using the Stable Diffusion weights. Please, visit the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license and tick the checkbox if you agree. You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work.\n",
"# https://huggingface.co/settings/tokens\n",
"!mkdir -p ~/.huggingface\n",
"HUGGINGFACE_TOKEN = \"\" #@param {type:\"string\"}\n",
"!echo -n \"{HUGGINGFACE_TOKEN}\" > ~/.huggingface/token"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aEJZoFQ2YHIb"
},
"outputs": [],
"source": [
"@#title upload to Huggingface\n",
"from huggingface_hub import HfApi\n",
"api = HfApi()\n",
"api.upload_file(\n",
" path_or_fileobj=\"\",\n",
" path_in_repo=\"name.zip\",\n",
" repo_id=\"\",\n",
" repo_type=\"dataset\",\n",
")"
]
}
]
} |