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import gradio as gr
import numpy as np
import torch
from PIL import Image
from segment_anything import SamPredictor, sam_model_registry, SamAutomaticMaskGenerator
from transformers import pipeline
import colorsys
sam_checkpoint = "sam_vit_h_4b8939.pth"
model_type = "vit_h"
device = "cuda" if torch.cuda.is_available() else "cpu"
#sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
#sam.to(device=device)
#predictor = SamPredictor(sam)
#mask_generator = SamAutomaticMaskGenerator(sam)
generator = pipeline(model="facebook/sam-vit-base", task="mask-generation", points_per_batch=256)
#image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
# controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
# "SAMControlNet/sd-controlnet-sam-seg", dtype=jnp.float32
# )
# pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
# "runwayml/stable-diffusion-v1-5",
# controlnet=controlnet,
# revision="flax",
# dtype=jnp.bfloat16,
# )
# params["controlnet"] = controlnet_params
# p_params = replicate(params)
with gr.Blocks() as demo:
gr.Markdown("# Ahsans version WildSynth: Synthetic Wildlife Data Generation")
gr.Markdown(
"""
## Work in Progress
### About
### How To Use
"""
)
with gr.Row():
input_img = gr.Image(label="Input", type="pil")
mask_img = gr.Image(label="Mask", interactive=False)
output_img = gr.Image(label="Output", interactive=False)
with gr.Row():
submit = gr.Button("Submit")
clear = gr.Button("Clear")
def generate_mask(image):
outputs = generator(image, points_per_batch=256)
mask_images = []
for mask in outputs["masks"]:
color = np.concatenate([np.random.random(3), np.array([1.0])], axis=0)
h, w = mask.shape[-2:]
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
np_img = mask_image;
np_img = np.squeeze(np_img, axis=2) # axis=2 is channel dimension
pil_img = Image.fromarray(np_img, 'RGB')
mask_images.append(pil_img)
#return np.stack(mask_images)
return image
# def infer(
# image, prompts, negative_prompts, num_inference_steps=50, seed=4, num_samples=4
# ):
# try:
# rng = jax.random.PRNGKey(int(seed))
# num_inference_steps = int(num_inference_steps)
# image = Image.fromarray(image, mode="RGB")
# num_samples = max(jax.device_count(), int(num_samples))
# p_rng = jax.random.split(rng, jax.device_count())
# prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
# negative_prompt_ids = pipe.prepare_text_inputs(
# [negative_prompts] * num_samples
# )
# processed_image = pipe.prepare_image_inputs([image] * num_samples)
# prompt_ids = shard(prompt_ids)
# negative_prompt_ids = shard(negative_prompt_ids)
# processed_image = shard(processed_image)
# output = pipe(
# prompt_ids=prompt_ids,
# image=processed_image,
# params=p_params,
# prng_seed=p_rng,
# num_inference_steps=num_inference_steps,
# neg_prompt_ids=negative_prompt_ids,
# jit=True,
# ).images
# del negative_prompt_ids
# del processed_image
# del prompt_ids
# output = output.reshape((num_samples,) + output.shape[-3:])
# final_image = [np.array(x * 255, dtype=np.uint8) for x in output]
# print(output.shape)
# del output
# except Exception as e:
# print("Error: " + str(e))
# final_image = [np.zeros((512, 512, 3), dtype=np.uint8)] * num_samples
# finally:
# gc.collect()
# return final_image
# def _clear(sel_pix, img, mask, seg, out, prompt, neg_prompt, bg):
# img = None
# mask = None
# seg = None
# out = None
# prompt = ""
# neg_prompt = ""
# bg = False
# return img, mask, seg, out, prompt, neg_prompt, bg
input_img.change(
generate_mask,
inputs=[input_img],
outputs=[mask_img],
)
# submit.click(
# infer,
# inputs=[mask_img, prompt_text, negative_prompt_text],
# outputs=[output_img],
# )
# clear.click(
# _clear,
# inputs=[
# input_img,
# mask_img,
# output_img,
# prompt_text,
# negative_prompt_text,
# ],
# outputs=[
# input_img,
# mask_img,
# output_img,
# prompt_text,
# negative_prompt_text,
# ],
# )
if __name__ == "__main__":
demo.queue()
demo.launch() |