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This commit is contained in:
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commit
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import gradio as gr
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import numpy as np
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import torch
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import os
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from PIL import Image
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from perspective_fields import pano_utils as pu
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from diffusers import (
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AutoencoderKL,
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ControlNetModel,
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FluxControlNetPipeline,
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FlowMatchEulerDiscreteScheduler,
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FluxControlNetModel,
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)
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from transformers import CLIPTextModel, CLIPTokenizer, CLIPImageProcessor, CLIPVisionModelWithProjection
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from transformers import T5EncoderModel, T5TokenizerFast
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from safetensors.torch import load_file
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# 加载本地模型文件路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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project_dir = os.path.dirname(current_dir)
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# 修正路径格式，使用os.path.join确保路径分隔符正确
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flux_model_dir = os.path.join(project_dir, "models", "diffusion_models")
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print(f"Loading models from:")
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print(f"Flux dir: {flux_model_dir}")
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# 创建缓存目录（如果不存在）
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os.makedirs(os.path.join(project_dir, "cache"), exist_ok=True)
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# 加载所有需要的组件
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print("Loading components...")
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# 1. 加载调度器
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scheduler = FlowMatchEulerDiscreteScheduler.from_config(
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{
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"num_train_timesteps": 1000
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}
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)
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# 5. 加载ControlNet - 使用与FLUX.1-dev兼容的模型
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print("Loading ControlNet compatible with FLUX.1-dev...")
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controlnet_model = "InstantX/FLUX.1-dev-controlnet-canny"
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controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16,cache_dir=os.path.join(project_dir, "cache"))
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# 2. 加载VAE
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try:
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vae = AutoencoderKL.from_pretrained(
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"diffusers/FLUX.1-vae",
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torch_dtype=torch.bfloat16, # 使用bfloat16匹配controlnet
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cache_dir=os.path.join(project_dir, "cache")
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)
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except Exception as e:
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print(f"Error loading VAE: {e}")
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print("Loading default VAE...")
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vae = AutoencoderKL.from_pretrained(
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"diffusers/FLUX.1-vae",
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torch_dtype=torch.bfloat16, # 使用bfloat16匹配controlnet
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cache_dir=os.path.join(project_dir, "cache")
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)
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# 3. 加载文本编码器和标记器
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text_encoder = CLIPTextModel.from_pretrained(
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"openai/clip-vit-large-patch14",
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torch_dtype=torch.bfloat16, # 使用bfloat16匹配controlnet
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cache_dir=os.path.join(project_dir, "cache")
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)
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tokenizer = CLIPTokenizer.from_pretrained(
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"openai/clip-vit-large-patch14",
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cache_dir=os.path.join(project_dir, "cache")
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)
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# 4. 加载第二个文本编码器和标记器
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print("从本地加载预先下载的T5模型...")
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# 指定本地模型路径 - 假设已经手动下载了魔搭的模型
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t5_model_path = os.path.join(project_dir, "models", "t5_xxl") # 根据实际路径调整
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# 检查模型路径是否存在
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if not os.path.exists(t5_model_path):
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print(f"警告: 本地模型路径 {t5_model_path} 不存在。请先手动下载魔搭的T5-XXL模型到此路径。")
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print("继续尝试从HuggingFace下载，但这可能会很慢...")
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t5_model_path = "google/t5-v1_1-xxl"
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text_encoder_2 = T5EncoderModel.from_pretrained(
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t5_model_path, # 使用本地路径
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torch_dtype=torch.bfloat16,
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cache_dir=os.path.join(project_dir, "cache")
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)
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tokenizer_2 = T5TokenizerFast.from_pretrained(
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t5_model_path, # 使用本地路径
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cache_dir=os.path.join(project_dir, "cache")
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)
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# 6. 加载特征提取器和图像编码器
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feature_extractor = CLIPImageProcessor.from_pretrained(
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"openai/clip-vit-large-patch14",
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cache_dir=os.path.join(project_dir, "cache")
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)
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image_encoder = CLIPVisionModelWithProjection.from_pretrained(
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"openai/clip-vit-large-patch14",
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torch_dtype=torch.bfloat16, # 使用bfloat16匹配controlnet
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cache_dir=os.path.join(project_dir, "cache")
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)
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# 7. 加载PipelineImageProcessor (transformer)
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base_model = "black-forest-labs/FLUX.1-dev"
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# 不加载transformer组件，因为这会导致类型错误
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# pipeline会自动初始化正确类型的transformer
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# 8. 加载Pipeline - 直接从HuggingFace加载
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try:
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print("Loading FLUX model from HuggingFace...")
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# 设置缓存相关环境变量
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os.environ["HF_HOME"] = os.path.join(project_dir, "cache")
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os.environ["TRANSFORMERS_CACHE"] = os.path.join(project_dir, "cache", "transformers")
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os.environ["DIFFUSERS_CACHE"] = os.path.join(project_dir, "cache", "diffusers")
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# 设置缓存路径参数
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cache_path = os.path.join(project_dir, "cache")
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pipe = FluxControlNetPipeline.from_pretrained(
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base_model,
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scheduler=scheduler,
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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text_encoder_2=text_encoder_2,
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tokenizer_2=tokenizer_2,
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feature_extractor=feature_extractor,
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image_encoder=image_encoder,
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controlnet=controlnet,
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torch_dtype=torch.bfloat16, # 使用bfloat16匹配controlnet
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low_cpu_mem_usage=False,
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cache_dir=cache_path,
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resume_download=True, # 如果下载中断，尝试继续下载
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force_download=False # 如果已缓存，不重新下载
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)
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except Exception as e:
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print(f"Error loading from HuggingFace: {e}")
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print("Trying alternative configuration...")
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# 启用CPU卸载以减少GPU内存使用
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pipe.enable_model_cpu_offload()
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def inference(prompt, pf_image, n_steps=2, seed=13):
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"""Generates an image based on the given prompt and perspective field image."""
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pf = Image.fromarray(
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np.concatenate(
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[np.expand_dims(pf_image[:, :, i], axis=-1) for i in [2, 1, 0]], axis=2
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)
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)
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pf_condition = pf.resize((1024, 1024))
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generator = torch.manual_seed(seed)
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print("Control image size:", pf_condition.size) # 应该是 (1024, 1024)
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return pipe(
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prompt=prompt,
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num_inference_steps=n_steps,
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generator=generator,
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control_image=pf_condition, # 只保留ControlNet的输入参数
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# 移除不支持的参数
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# image=pf_condition, # 需要为img2img提供一个基础图像
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# strength=0.8, # 控制处理强度
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).images[0]
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def obtain_pf(xi, roll, pitch, vfov):
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"""Computes perspective fields given camera parameters."""
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w, h = (1024, 1024)
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equi_img = np.zeros((h, w, 3), dtype=np.uint8)
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x = -np.sin(np.radians(vfov / 2))
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z = np.sqrt(1 - x**2)
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f_px_effective = -0.5 * (w / 2) * (xi + z) / x
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crop, _, _, _, up, lat, _ = pu.crop_distortion(
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equi_img, f=f_px_effective, xi=xi, H=h, W=w, az=10, el=-pitch, roll=roll
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)
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gravity = (up + 1) * 127.5
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latitude = np.expand_dims((np.degrees(lat) + 90) * (255 / 180), axis=-1)
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pf_image = np.concatenate([gravity, latitude], axis=2).astype(np.uint8)
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# 尝试生成blend图像，如果失败则创建一个替代的blend图像
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try:
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blend = pu.draw_perspective_fields(crop, up, np.radians(np.degrees(lat)))
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except Exception as e:
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print(f"Error generating blend image: {e}")
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# 创建一个替代的blend图像
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blend = np.zeros_like(pf_image)
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# 添加一些基本的可视化
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blend[:,:,0] = (np.degrees(lat) + 90) * (255 / 180) # 使用latitude作为红色通道
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blend[:,:,1] = (up + 1) * 127.5 # 使用up作为绿色通道
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blend[:,:,2] = 128 # 蓝色通道设置为中性值
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return pf_image, blend
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# Gradio UI
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demo = gr.Blocks(theme=gr.themes.Soft())
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with demo:
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gr.Markdown("""---""")
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gr.Markdown("""# PreciseCam: Precise Camera Control for Text-to-Image Generation""")
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gr.Markdown("""1. Set the camera parameters (Roll, Pitch, Vertical FOV, ξ)""")
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gr.Markdown("""2. Click "Compute PF-US" to generate the perspective field image""")
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gr.Markdown("""3. Enter a prompt for the image generation""")
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gr.Markdown("""4. Click "Generate Image" to create the final image""")
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gr.Markdown("""---""")
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with gr.Row():
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with gr.Column():
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roll = gr.Slider(-90, 90, 0, label="Roll")
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pitch = gr.Slider(-90, 90, 1, label="Pitch")
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vfov = gr.Slider(15, 140, 50, label="Vertical FOV")
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xi = gr.Slider(0.0, 1, 0.2, label="ξ")
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prompt = gr.Textbox(
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lines=4,
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label="Prompt",
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show_copy_button=True,
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value="A colorful autumn park with leaves of orange, red, and yellow scattered across a winding path.",
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)
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pf_btn = gr.Button("Compute PF-US", variant="primary")
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with gr.Row():
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pf_img = gr.Image(height=1024 // 4, width=1024 // 4, label="PF-US")
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condition_img = gr.Image(
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height=1024 // 4, width=1024 // 4, label="Internal PF-US (RGB)"
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)
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with gr.Column(scale=2):
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result_img = gr.Image(label="Generated Image", height=1024 // 2)
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inf_btn = gr.Button("Generate Image", variant="primary")
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gr.Markdown("""---""")
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pf_btn.click(
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obtain_pf, inputs=[xi, roll, pitch, vfov], outputs=[condition_img, pf_img]
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)
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inf_btn.click(inference, inputs=[prompt, condition_img], outputs=[result_img])
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# 设置环境变量，禁用代理
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os.environ["HTTP_PROXY"] = ""
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os.environ["HTTPS_PROXY"] = ""
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os.environ["NO_PROXY"] = "localhost,127.0.0.1"
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# 启动Gradio应用，指定内联选项和服务器名称
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demo.launch(
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server_name="0.0.0.0",
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server_port=9946,
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share=False,
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favicon_path=None,
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show_api=False,
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prevent_thread_lock=True
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)
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Code based on the original implementation from [jinlinyi/PerspectiveFields](https://github.com/jinlinyi/PerspectiveFields).
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**Citation:**
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```bibtex
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@inproceedings{jin2023perspective,
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title={Perspective Fields for Single Image Camera Calibration},
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author={Linyi Jin and Jianming Zhang and Yannick Hold-Geoffroy and Oliver Wang and Kevin Matzen and Matthew Sticha and David F. Fouhey},
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booktitle = {CVPR},
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year={2023}
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}
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```
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#######################################################################
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# Adapted code from https://github.com/jinlinyi/PerspectiveFields
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#######################################################################
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import torch
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import numpy as np
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from equilib import grid_sample
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from sklearn.preprocessing import normalize
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from perspective_fields.visualizer import VisualizerPerspective
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from numpy.lib.scimath import sqrt as csqrt
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def deg2rad(deg):
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"""convert degrees to radians"""
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return deg * np.pi / 180
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def diskradius(xi, f): # compute the disk radius when the image is catadioptric
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return np.sqrt(-(f * f) / (1 - xi * xi))
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def crop_distortion(image360_path, f, xi, H, W, az, el, roll):
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"""
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Reference: https://github.com/dompm/spherical-distortion-dataset/blob/main/spherical_distortion/spherical_distortion.py
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Crop distorted image with specified camera parameters
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Args:
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image360_path (str): path to image which to crop from
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f (float): focal_length of cropped image
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xi:
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H (int): height of cropped image
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W (int): width of cropped image
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az: camera rotation about camera frame y-axis of cropped image (degrees)
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el: camera rotation about camera frame x-axis of cropped image (degrees)
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roll: camera rotation about camera frame z-axis of cropped image (degrees)
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Returns:
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im (np.ndarray): cropped, distorted image
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"""
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u0 = W / 2.0
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v0 = H / 2.0
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grid_x, grid_y = np.meshgrid(list(range(W)), list(range(H)))
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image360 = image360_path.copy()
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ImPano_W = np.shape(image360)[1]
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ImPano_H = np.shape(image360)[0]
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x_ref = 1
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y_ref = 1
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fmin = minfocal(
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u0, v0, xi, x_ref, y_ref
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) # compute minimal focal length for the image to ve catadioptric with given xi
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# 1. Projection on the camera plane
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X_Cam = np.divide(grid_x - u0, f)
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Y_Cam = -np.divide(grid_y - v0, f)
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# 2. Projection on the sphere
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AuxVal = np.multiply(X_Cam, X_Cam) + np.multiply(Y_Cam, Y_Cam)
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alpha_cam = np.real(xi + csqrt(1 + np.multiply((1 - xi * xi), AuxVal)))
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alpha_div = AuxVal + 1
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alpha_cam_div = np.divide(alpha_cam, alpha_div)
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X_Sph = np.multiply(X_Cam, alpha_cam_div)
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Y_Sph = np.multiply(Y_Cam, alpha_cam_div)
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Z_Sph = alpha_cam_div - xi
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# 3. Rotation of the sphere
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coords = np.vstack((X_Sph.ravel(), Y_Sph.ravel(), Z_Sph.ravel()))
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rot_el = np.array(
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[
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1.0,
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0.0,
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0.0,
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0.0,
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np.cos(deg2rad(el)),
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-np.sin(deg2rad(el)),
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0.0,
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np.sin(deg2rad(el)),
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np.cos(deg2rad(el)),
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]
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).reshape((3, 3))
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rot_az = np.array(
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[
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np.cos(deg2rad(az)),
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0.0,
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-np.sin(deg2rad(az)),
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0.0,
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1.0,
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0.0,
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np.sin(deg2rad(az)),
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0.0,
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np.cos(deg2rad(az)),
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]
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).reshape((3, 3))
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rot_roll = np.array(
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[
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np.cos(deg2rad(roll)),
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np.sin(deg2rad(roll)),
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0.0,
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-np.sin(deg2rad(roll)),
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np.cos(deg2rad(roll)),
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0.0,
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0.0,
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0.0,
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1.0,
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]
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).reshape((3, 3))
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sph = rot_el.dot(rot_roll.T.dot(coords))
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sph = rot_az.dot(sph)
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sph = sph.reshape((3, H, W)).transpose((1, 2, 0))
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X_Sph, Y_Sph, Z_Sph = sph[:, :, 0], sph[:, :, 1], sph[:, :, 2]
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# 4. cart 2 sph
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ntheta = np.arctan2(X_Sph, Z_Sph)
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nphi = np.arctan2(Y_Sph, np.sqrt(Z_Sph**2 + X_Sph**2))
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pi = np.pi
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# 5. Sphere to pano
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min_theta = -pi
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max_theta = pi
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min_phi = -pi / 2.0
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max_phi = pi / 2.0
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min_x = 0
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max_x = ImPano_W - 1.0
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min_y = 0
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max_y = ImPano_H - 1.0
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## for x
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a = (max_theta - min_theta) / (max_x - min_x)
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b = max_theta - a * max_x # from y=ax+b %% -a;
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nx = (1.0 / a) * (ntheta - b)
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## for y
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a = (min_phi - max_phi) / (max_y - min_y)
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b = max_phi - a * min_y # from y=ax+b %% -a;
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ny = (1.0 / a) * (nphi - b)
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lat = nphi.copy()
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xy_map = np.stack((nx, ny)).transpose(1, 2, 0)
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# 6. Final step interpolation and mapping
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# im = np.array(my_interpol.interp2linear(image360, nx, ny), dtype=np.uint8)
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||||
im = grid_sample.numpy_grid_sample.default(
|
||||
image360.transpose(2, 0, 1), np.stack((ny, nx))
|
||||
).transpose(1, 2, 0)
|
||||
if (
|
||||
f < fmin
|
||||
): # if it is a catadioptric image, apply mask and a disk in the middle
|
||||
r = diskradius(xi, f)
|
||||
DIM = im.shape
|
||||
ci = (np.round(DIM[0] / 2), np.round(DIM[1] / 2))
|
||||
xx, yy = np.meshgrid(
|
||||
list(range(DIM[0])) - ci[0], list(range(DIM[1])) - ci[1]
|
||||
)
|
||||
mask = np.double((np.multiply(xx, xx) + np.multiply(yy, yy)) < r * r)
|
||||
mask_3channel = np.stack([mask, mask, mask], axis=-1).transpose((1, 0, 2))
|
||||
im = np.array(np.multiply(im, mask_3channel), dtype=np.uint8)
|
||||
|
||||
col = nphi[:, W // 2]
|
||||
zero_crossings_rows = np.where(np.diff(np.sign(col)))[0]
|
||||
if len(zero_crossings_rows) >= 2:
|
||||
print("WARNING | Number of zero crossings:", len(zero_crossings_rows))
|
||||
zero_crossings_rows = [zero_crossings_rows[0]]
|
||||
|
||||
if len(zero_crossings_rows) == 0:
|
||||
offset = np.nan
|
||||
else:
|
||||
assert col[zero_crossings_rows[0]] >= 0
|
||||
assert col[zero_crossings_rows[0] + 1] <= 0
|
||||
dy = col[zero_crossings_rows[0] + 1] - col[zero_crossings_rows[0]]
|
||||
offset = zero_crossings_rows[0] - col[zero_crossings_rows[0]] / dy
|
||||
assert col[zero_crossings_rows[0]] / dy <= 1.0
|
||||
# Reproject [nx, ny+epsilon] back
|
||||
epsilon = 1e-5
|
||||
end_vector_x = nx.copy()
|
||||
end_vector_y = ny.copy() - epsilon
|
||||
# -5. pano to Sphere
|
||||
a = (max_theta - min_theta) / (max_x - min_x)
|
||||
b = max_theta - a * max_x # from y=ax+b %% -a;
|
||||
ntheta_end = end_vector_x * a + b
|
||||
## for y
|
||||
a = (min_phi - max_phi) / (max_y - min_y)
|
||||
b = max_phi - a * min_y
|
||||
nphi_end = end_vector_y * a + b
|
||||
# -4. sph 2 cart
|
||||
Y_Sph = np.sin(nphi)
|
||||
X_Sph = np.cos(nphi_end) * np.sin(ntheta_end)
|
||||
Z_Sph = np.cos(nphi_end) * np.cos(ntheta_end)
|
||||
# -3. Reverse Rotation of the sphere
|
||||
coords = np.vstack((X_Sph.ravel(), Y_Sph.ravel(), Z_Sph.ravel()))
|
||||
sph = rot_roll.dot(rot_el.T.dot(rot_az.T.dot(coords)))
|
||||
sph = sph.reshape((3, H, W)).transpose((1, 2, 0))
|
||||
X_Sph, Y_Sph, Z_Sph = sph[:, :, 0], sph[:, :, 1], sph[:, :, 2]
|
||||
|
||||
# -1. Projection on the image plane
|
||||
|
||||
X_Cam = X_Sph * f / (xi * csqrt(X_Sph**2 + Y_Sph**2 + Z_Sph**2) + Z_Sph) + u0
|
||||
Y_Cam = -Y_Sph * f / (xi * csqrt(X_Sph**2 + Y_Sph**2 + Z_Sph**2) + Z_Sph) + v0
|
||||
up = np.stack((X_Cam - grid_x, Y_Cam - grid_y)).transpose(1, 2, 0)
|
||||
up = normalize(up.reshape(-1, 2)).reshape(up.shape)
|
||||
|
||||
return im, ntheta, nphi, offset, up, lat, xy_map
|
||||
|
||||
def minfocal(u0, v0, xi, xref=1, yref=1):
|
||||
"""compute the minimum focal for the image to be catadioptric given xi"""
|
||||
value = -(1 - xi * xi) * ((xref - u0) * (xref - u0) + (yref - v0) * (yref - v0))
|
||||
|
||||
if value < 0:
|
||||
return 0
|
||||
else:
|
||||
return np.sqrt(value) * 1.0001
|
||||
|
||||
#------------------------------------------------
|
||||
|
||||
def draw_perspective_fields(
|
||||
img_rgb, up, latimap, color=None, density=10, arrow_inv_len=20, return_img=True
|
||||
):
|
||||
"""draw perspective field on top of input image
|
||||
|
||||
Args:
|
||||
img_rgb (np.ndarray): input image
|
||||
up (np.ndarray): gravity field (h, w, 2)
|
||||
latimap (np.ndarray): latitude map (h, w) (radians)
|
||||
color ((float, float, float), optional): RGB color for up vectors. [0, 1]
|
||||
Defaults to None.
|
||||
density (int, optional): Value to control density of up vectors.
|
||||
Each row has (width // density) vectors.
|
||||
Each column has (height // density) vectors.
|
||||
Defaults to 10.
|
||||
arrow_inv_len (int, optional): Value to control vector length
|
||||
Vector length set to (image plane diagonal // arrow_inv_len).
|
||||
Defaults to 20.
|
||||
return_img (bool, optional): bool to control if to return np array or VisImage
|
||||
|
||||
Returns:
|
||||
image blended with perspective fields.
|
||||
"""
|
||||
visualizer = VisualizerPerspective(img_rgb.copy())
|
||||
vis_output = visualizer.draw_lati(latimap)
|
||||
if torch.is_tensor(up):
|
||||
up = up.numpy().transpose(1, 2, 0)
|
||||
im_h, im_w, _ = img_rgb.shape
|
||||
x, y = np.meshgrid(
|
||||
np.arange(0, im_w, im_w // density), np.arange(0, im_h, im_h // density)
|
||||
)
|
||||
x, y = x.ravel(), y.ravel()
|
||||
arrow_len = np.sqrt(im_w**2 + im_h**2) // arrow_inv_len
|
||||
end = up[y, x, :] * arrow_len
|
||||
if color is None:
|
||||
color = (0, 1, 0)
|
||||
vis_output = visualizer.draw_arrow(x, y, end[:, 0], -end[:, 1], color=color)
|
||||
if return_img:
|
||||
return vis_output.get_image()
|
||||
else:
|
||||
return vis_output
|
||||
|
|
@ -0,0 +1,283 @@
|
|||
#######################################################################
|
||||
# Adapted code from https://github.com/jinlinyi/PerspectiveFields
|
||||
#######################################################################
|
||||
# Modified from https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/visualizer.py
|
||||
import matplotlib.colors as mplc
|
||||
import matplotlib.figure as mplfigure
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import torch
|
||||
from matplotlib.backends.backend_agg import FigureCanvasAgg
|
||||
|
||||
|
||||
class VisImage:
|
||||
def __init__(self, img, scale=1.0):
|
||||
"""
|
||||
Args:
|
||||
img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255].
|
||||
scale (float): scale the input image
|
||||
"""
|
||||
self.img = img
|
||||
self.scale = scale
|
||||
self.width, self.height = img.shape[1], img.shape[0]
|
||||
self._setup_figure(img)
|
||||
|
||||
def _setup_figure(self, img):
|
||||
"""
|
||||
Args:
|
||||
Same as in :meth:`__init__()`.
|
||||
|
||||
Returns:
|
||||
fig (matplotlib.pyplot.figure): top level container for all the image plot elements.
|
||||
ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system.
|
||||
"""
|
||||
fig = mplfigure.Figure(frameon=False)
|
||||
self.dpi = fig.get_dpi()
|
||||
# add a small 1e-2 to avoid precision lost due to matplotlib's truncation
|
||||
# (https://github.com/matplotlib/matplotlib/issues/15363)
|
||||
fig.set_size_inches(
|
||||
(self.width * self.scale + 1e-2) / self.dpi,
|
||||
(self.height * self.scale + 1e-2) / self.dpi,
|
||||
)
|
||||
self.canvas = FigureCanvasAgg(fig)
|
||||
# self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
|
||||
ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
|
||||
ax.axis("off")
|
||||
self.fig = fig
|
||||
self.ax = ax
|
||||
self.reset_image(img)
|
||||
|
||||
def reset_image(self, img):
|
||||
"""
|
||||
Args:
|
||||
img: same as in __init__
|
||||
"""
|
||||
img = img.astype("uint8")
|
||||
self.ax.imshow(
|
||||
img, extent=(0, self.width, self.height, 0), interpolation="nearest"
|
||||
)
|
||||
|
||||
def save(self, filepath):
|
||||
"""
|
||||
Args:
|
||||
filepath (str): a string that contains the absolute path, including the file name, where
|
||||
the visualized image will be saved.
|
||||
"""
|
||||
self.fig.savefig(filepath)
|
||||
|
||||
def get_image(self):
|
||||
"""
|
||||
Returns:
|
||||
ndarray:
|
||||
the visualized image of shape (H, W, 3) (RGB) in uint8 type.
|
||||
The shape is scaled w.r.t the input image using the given `scale` argument.
|
||||
"""
|
||||
canvas = self.canvas
|
||||
s, (width, height) = canvas.print_to_buffer()
|
||||
# buf = io.BytesIO() # works for cairo backend
|
||||
# canvas.print_rgba(buf)
|
||||
# width, height = self.width, self.height
|
||||
# s = buf.getvalue()
|
||||
|
||||
buffer = np.frombuffer(s, dtype="uint8")
|
||||
|
||||
img_rgba = buffer.reshape(height, width, 4)
|
||||
rgb, alpha = np.split(img_rgba, [3], axis=2)
|
||||
return rgb.astype("uint8")
|
||||
|
||||
|
||||
class Visualizer:
|
||||
"""
|
||||
Visualizer that draws data about detection/segmentation on images.
|
||||
|
||||
It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`
|
||||
that draw primitive objects to images, as well as high-level wrappers like
|
||||
`draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`
|
||||
that draw composite data in some pre-defined style.
|
||||
|
||||
Note that the exact visualization style for the high-level wrappers are subject to change.
|
||||
Style such as color, opacity, label contents, visibility of labels, or even the visibility
|
||||
of objects themselves (e.g. when the object is too small) may change according
|
||||
to different heuristics, as long as the results still look visually reasonable.
|
||||
|
||||
To obtain a consistent style, you can implement custom drawing functions with the
|
||||
abovementioned primitive methods instead. If you need more customized visualization
|
||||
styles, you can process the data yourself following their format documented in
|
||||
tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not
|
||||
intend to satisfy everyone's preference on drawing styles.
|
||||
|
||||
This visualizer focuses on high rendering quality rather than performance. It is not
|
||||
designed to be used for real-time applications.
|
||||
"""
|
||||
|
||||
def __init__(self, img_rgb, scale=1.0, font_size_scale=1.0):
|
||||
"""
|
||||
Args:
|
||||
img_rgb: a numpy array of shape (H, W, C), where H and W correspond to
|
||||
the height and width of the image respectively. C is the number of
|
||||
color channels. The image is required to be in RGB format since that
|
||||
is a requirement of the Matplotlib library. The image is also expected
|
||||
to be in the range [0, 255].
|
||||
font_size_scale: extra scaling of font size on top of default font size
|
||||
"""
|
||||
self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
|
||||
self.output = VisImage(self.img, scale=scale)
|
||||
self.cpu_device = torch.device("cpu")
|
||||
|
||||
# too small texts are useless, therefore clamp to 9
|
||||
self._default_font_size = (
|
||||
max(np.sqrt(self.output.height * self.output.width) // 90, 10 // scale)
|
||||
* font_size_scale
|
||||
)
|
||||
|
||||
"""
|
||||
Primitive drawing functions:
|
||||
"""
|
||||
|
||||
def draw_text(
|
||||
self,
|
||||
text,
|
||||
position,
|
||||
*,
|
||||
font_size=None,
|
||||
color="g",
|
||||
horizontal_alignment="center",
|
||||
rotation=0,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
text (str): class label
|
||||
position (tuple): a tuple of the x and y coordinates to place text on image.
|
||||
font_size (int, optional): font of the text. If not provided, a font size
|
||||
proportional to the image width is calculated and used.
|
||||
color: color of the text. Refer to `matplotlib.colors` for full list
|
||||
of formats that are accepted.
|
||||
horizontal_alignment (str): see `matplotlib.text.Text`
|
||||
rotation: rotation angle in degrees CCW
|
||||
|
||||
Returns:
|
||||
output (VisImage): image object with text drawn.
|
||||
"""
|
||||
if not font_size:
|
||||
font_size = self._default_font_size
|
||||
|
||||
# since the text background is dark, we don't want the text to be dark
|
||||
color = np.maximum(list(mplc.to_rgb(color)), 0.2)
|
||||
color[np.argmax(color)] = max(0.8, np.max(color))
|
||||
|
||||
x, y = position
|
||||
self.output.ax.text(
|
||||
x,
|
||||
y,
|
||||
text,
|
||||
size=font_size * self.output.scale,
|
||||
family="sans-serif",
|
||||
bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
|
||||
verticalalignment="top",
|
||||
horizontalalignment=horizontal_alignment,
|
||||
color=color,
|
||||
zorder=10,
|
||||
rotation=rotation,
|
||||
)
|
||||
return self.output
|
||||
|
||||
def get_output(self):
|
||||
"""
|
||||
Returns:
|
||||
output (VisImage): the image output containing the visualizations added
|
||||
to the image.
|
||||
"""
|
||||
return self.output
|
||||
|
||||
|
||||
class VisualizerPerspective(Visualizer):
|
||||
def draw_arrow(
|
||||
self,
|
||||
x_pos,
|
||||
y_pos,
|
||||
x_direct,
|
||||
y_direct,
|
||||
color=None,
|
||||
linestyle="-",
|
||||
linewidth=None,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
x_data (list[int]): a list containing x values of all the points being drawn.
|
||||
Length of list should match the length of y_data.
|
||||
y_data (list[int]): a list containing y values of all the points being drawn.
|
||||
Length of list should match the length of x_data.
|
||||
color: color of the line. Refer to `matplotlib.colors` for a full list of
|
||||
formats that are accepted.
|
||||
linestyle: style of the line. Refer to `matplotlib.lines.Line2D`
|
||||
for a full list of formats that are accepted.
|
||||
linewidth (float or None): width of the line. When it's None,
|
||||
a default value will be computed and used.
|
||||
|
||||
Returns:
|
||||
output (VisImage): image object with line drawn.
|
||||
"""
|
||||
if linewidth is None:
|
||||
linewidth = self._default_font_size / 3
|
||||
linewidth = max(linewidth, 1)
|
||||
self.output.ax.quiver(
|
||||
x_pos,
|
||||
y_pos,
|
||||
x_direct,
|
||||
y_direct,
|
||||
color=color,
|
||||
scale_units="xy",
|
||||
scale=1,
|
||||
antialiased=True,
|
||||
headaxislength=3.5,
|
||||
linewidths=0.1, # , width=0.01
|
||||
)
|
||||
return self.output
|
||||
|
||||
def draw_lati(
|
||||
self, latimap, alpha_contourf=0.4, alpha_contour=0.9, contour_only=False
|
||||
):
|
||||
"""Blend latitude map"""
|
||||
height, width = latimap.shape
|
||||
y, x = np.mgrid[0:height, 0:width]
|
||||
cmap = plt.get_cmap("seismic")
|
||||
bands = 20
|
||||
levels = np.linspace(-np.pi / 2, np.pi / 2, bands - 1)
|
||||
if not contour_only:
|
||||
pp = self.output.ax.contourf(
|
||||
x,
|
||||
y,
|
||||
latimap,
|
||||
levels=levels,
|
||||
cmap=cmap,
|
||||
alpha=alpha_contourf,
|
||||
antialiased=True,
|
||||
)
|
||||
pp2 = self.output.ax.contour(
|
||||
x,
|
||||
y,
|
||||
latimap,
|
||||
pp.levels,
|
||||
cmap=cmap,
|
||||
alpha=alpha_contour,
|
||||
antialiased=True,
|
||||
linewidths=5,
|
||||
)
|
||||
try:
|
||||
for c in pp2.collections:
|
||||
c.set_linestyle("solid")
|
||||
except AttributeError:
|
||||
pass
|
||||
else:
|
||||
# only plot central contour
|
||||
pp = self.output.ax.contour(
|
||||
x,
|
||||
y,
|
||||
latimap,
|
||||
levels=[0],
|
||||
cmap=cmap,
|
||||
alpha=alpha_contour,
|
||||
antialiased=True,
|
||||
linewidths=15,
|
||||
)
|
||||
return self.output
|
||||
Loading…
Reference in New Issue