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from turtle import title
import gradio as gr
from transformers import pipeline
import numpy as np
from PIL import Image
import torch
import cv2
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation,AutoProcessor,AutoConfig
processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
classes = list()
def create_rgb_mask(mask):
color = tuple(np.random.choice(range(0,256), size=3))
gray_3_channel = cv2.merge((mask, mask, mask))
gray_3_channel[mask==255] = color
return gray_3_channel.astype(np.uint8)
def detect_using_clip(image,prompts=[],threshould=0.4):
predicted_masks = list()
inputs = processor(
text=prompts,
images=[image] * len(prompts),
padding="max_length",
return_tensors="pt",
)
with torch.no_grad(): # Use 'torch.no_grad()' to disable gradient computation
outputs = model(**inputs)
preds = outputs.logits.unsqueeze(1)
for i,prompt in enumerate(prompts):
predicted_image = torch.sigmoid(preds[i][0]).detach().cpu().numpy()
predicted_image = np.where(predicted_image>threshould,255,0)
predicted_masks.append(create_rgb_mask(predicted_image))
return predicted_masks
def visualize_images(image,predicted_images):
alpha = 0.7
image_resize = cv2.resize(image,(352,352))
resize_image_copy = image_resize.copy()
for mask_image in predicted_images:
resize_image_copy = cv2.addWeighted(resize_image_copy,alpha,mask_image,1-alpha,10)
return cv2.convertScaleAbs(resize_image_copy, alpha=1.8, beta=15)
def shot(image, labels_text):
if "," in labels_text:
prompts = labels_text.split(',')
else:
prompts = [labels_text]
prompts = list(map(lambda x: x.strip(),prompts))
predicted_images = detect_using_clip(image,prompts=prompts)
category_image = visualize_images(image=image,predicted_images=predicted_images)
return category_image
iface = gr.Interface(fn=shot,
inputs = [gr.Slider(5, 50, value=15, label="Brightness", info="Choose between 5 and 50"),"image","text"],
outputs = "image",
description ="Add an Image and lists of category to be detected separated by commas(atleast 2 )",
title = "Zero-shot Image Segmentation with Prompt ",
examples=[
[15,"images/room.jpg","bed, table, plant, light, window,light"],
[10,"images/image2.png","banner, building,door, sign,"],
[19,"images/seats.jpg","door,table,chairs"],
[20,"images/vegetables.jpg","carrot,radish,beans,potato,brnjal,basket"],
[17,"images/room2.jpg","door,platns,dog,coffe table,mug,pillow,table lamp,carpet,pictures,door,clock"]
],
# allow_flagging=False,
# analytics_enabled=False,
)
iface.launch()
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