Spaces:
Running
on
A10G
Running
on
A10G
add run_sam
Browse files- app.py +13 -11
- run_sam.py +34 -0
app.py
CHANGED
@@ -6,6 +6,7 @@ from PIL import Image
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from copy import deepcopy
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import sys
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import tempfile
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from huggingface_hub import snapshot_download
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LOCAL_CODE = os.environ.get("LOCAL_CODE", "1") == "1"
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@@ -16,7 +17,6 @@ code_dir = snapshot_download("zouzx/TriplaneGaussian", local_dir="./code", token
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sys.path.append(code_dir)
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if not LOCAL_CODE:
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import subprocess
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subprocess.run(["pip", "install", "--upgrade", "gradio"])
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import gradio as gr
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@@ -41,8 +41,8 @@ device = "cuda:{}".format(gpu) if torch.cuda.is_available() else "cpu"
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print("device: ", device)
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# load SAM checkpoint
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sam_predictor = sam_init(SAM_CKPT_PATH, gpu)
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print("load sam ckpt done.")
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# init system
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base_cfg: ExperimentConfig
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@@ -78,24 +78,26 @@ def resize_image(input_raw, size):
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resized_h = int(h * ratio)
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return input_raw.resize((resized_w, resized_h), Image.Resampling.LANCZOS)
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def preprocess(
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# if not preprocess:
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# print("No preprocess")
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# # return image_path
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# input_raw = Image.open(image_path)
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# input_raw.thumbnail([512, 512], Image.Resampling.LANCZOS)
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input_raw = resize_image(input_raw, 512)
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print("image size:", input_raw.size)
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image_sam = sam_out_nosave(
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)
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save_path = os.path.join(save_path, "input_rgba.png")
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# if save_path is None:
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# save_path, ext = os.path.splitext(image_path)
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# save_path = save_path + "_rgba.png"
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image_preprocess(image_sam, save_path, lower_contrast=False, rescale=True)
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# print("image save path = ", save_path)
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return save_path
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@@ -149,7 +151,7 @@ def launch(port):
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with gr.Row(variant='panel'):
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with gr.Column(scale=1):
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input_image = gr.Image(value=None,
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gr.Markdown(
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"""
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**Camera distance** denotes the distance between camera center and scene center.
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from copy import deepcopy
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import sys
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import tempfile
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import subprocess
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from huggingface_hub import snapshot_download
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LOCAL_CODE = os.environ.get("LOCAL_CODE", "1") == "1"
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sys.path.append(code_dir)
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if not LOCAL_CODE:
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subprocess.run(["pip", "install", "--upgrade", "gradio"])
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import gradio as gr
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print("device: ", device)
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# load SAM checkpoint
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# sam_predictor = sam_init(SAM_CKPT_PATH, gpu)
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# print("load sam ckpt done.")
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# init system
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base_cfg: ExperimentConfig
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resized_h = int(h * ratio)
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return input_raw.resize((resized_w, resized_h), Image.Resampling.LANCZOS)
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def preprocess(image_path, save_path):
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# if not preprocess:
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# print("No preprocess")
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# # return image_path
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# input_raw = Image.open(image_path)
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# input_raw.thumbnail([512, 512], Image.Resampling.LANCZOS)
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# input_raw = resize_image(input_raw, 512)
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# print("image size:", input_raw.size)
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# image_sam = sam_out_nosave(
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# sam_predictor, input_raw.convert("RGB"), pred_bbox(input_raw)
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# )
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save_path = os.path.join(save_path, "input_rgba.png")
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# if save_path is None:
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# save_path, ext = os.path.splitext(image_path)
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# save_path = save_path + "_rgba.png"
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# image_preprocess(image_sam, save_path, lower_contrast=False, rescale=True)
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subprocess.run([f"python run_sam.py --image_path {image_path} --save_path {save_path}"], shell=True)
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# print("image save path = ", save_path)
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return save_path
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with gr.Row(variant='panel'):
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with gr.Column(scale=1):
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input_image = gr.Image(value=None, width=512, height=512, type="filepath", sources="upload", label="Input Image")
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gr.Markdown(
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"""
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**Camera distance** denotes the distance between camera center and scene center.
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run_sam.py
ADDED
@@ -0,0 +1,34 @@
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from utils import image_preprocess, pred_bbox, sam_init, sam_out_nosave
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import os
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from PIL import Image
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import argparse
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SAM_CKPT_PATH = "code/checkpoints/sam_vit_h_4b8939.pth"
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def resize_image(input_raw, size):
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w, h = input_raw.size
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ratio = size / max(w, h)
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resized_w = int(w * ratio)
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resized_h = int(h * ratio)
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return input_raw.resize((resized_w, resized_h), Image.Resampling.LANCZOS)
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if __name__ == "__main__":
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# load SAM checkpoint
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gpu = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
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sam_predictor = sam_init(SAM_CKPT_PATH, gpu)
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print("load sam ckpt done.")
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parser = argparse.ArgumentParser()
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parser.add_argument("--image_path", required=True)
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parser.add_argument("--save_path", required=True)
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args = parser.parse_args()
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input_raw = Image.open(args.image_path)
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# input_raw.thumbnail([512, 512], Image.Resampling.LANCZOS)
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input_raw = resize_image(input_raw, 512)
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image_sam = sam_out_nosave(
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sam_predictor, input_raw.convert("RGB"), pred_bbox(input_raw)
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)
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# save_path = os.path.join(args.save_path, "input_rgba.png")
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image_preprocess(image_sam, args.save_path, lower_contrast=False, rescale=True)
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