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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 | |
| from torch import nn | |
| import cv2 | |
| from matplotlib import pyplot as plt | |
| from segmentation_mask_overlay import overlay_masks | |
| 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) | |
| preds = nn.functional.interpolate( | |
| outputs.logits.unsqueeze(1), | |
| size=(image.shape[0], image.shape[1]), | |
| mode="bilinear" | |
| ) | |
| threshold = 0.1 | |
| flat_preds = torch.sigmoid(preds.squeeze()).reshape((preds.shape[0], -1)) | |
| # Initialize a dummy "unlabeled" mask with the threshold | |
| flat_preds_with_treshold = torch.full((preds.shape[0] + 1, flat_preds.shape[-1]), threshold) | |
| flat_preds_with_treshold[1:preds.shape[0]+1,:] = flat_preds | |
| # Get the top mask index for each pixel | |
| inds = torch.topk(flat_preds_with_treshold, 1, dim=0).indices.reshape((preds.shape[-2], preds.shape[-1])) | |
| predicted_masks = [] | |
| for i in range(1, len(prompts)+1): | |
| mask = np.where(inds==i,255,0) | |
| predicted_masks.append(mask) | |
| return predicted_masks | |
| def visualize_images(image,predicted_images,brightness=15,contrast=1.8): | |
| 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=contrast, beta=brightness) | |
| def shot(alpha,beta,image,labels_text): | |
| print(labels_text) | |
| if "," in labels_text: | |
| prompts = labels_text.split(',') | |
| else: | |
| prompts = [labels_text] | |
| print(prompts) | |
| prompts = list(map(lambda x: x.strip(),prompts)) | |
| mask_labels = [f"{prompt}_{i}" for i,prompt in enumerate(prompts)] | |
| cmap = plt.cm.tab20(np.arange(len(mask_labels)))[..., :-1] | |
| predicted_masks = detect_using_clip(image,prompts=prompts) | |
| bool_masks = [predicted_mask.astype('bool') for predicted_mask in predicted_masks] | |
| category_image = overlay_masks(image,np.stack(bool_masks,-1),labels=mask_labels,colors=cmap,alpha=alpha,beta=beta) | |
| return category_image | |
| iface = gr.Interface(fn=shot, | |
| inputs = [ | |
| gr.Slider(0.1, 1, value=0.3, step=0.1 , label="alpha", info="Choose between 0.1 to 1"), | |
| gr.Slider(0.1, 1, value=0.7, step=0.1, label="beta", info="Choose between 0.1 to 1"), | |
| "image", | |
| "text" | |
| ], | |
| outputs = "image", | |
| description ="Add an Image and labels to be detected separated by commas(atleast 2)", | |
| title = "Zero-shot Image Segmentation with Prompt", | |
| examples=[ | |
| [0.4,0.7,"images/room.jpg","chair, plant , flower pot , white cabinet , paintings , decorative plates , books"], | |
| [0.4,0.7,"images/seats.jpg","door,table,chairs"], | |
| [0.3,0.8,"images/vegetables.jpg","carrot,white radish,brinjal,basket,potato"], | |
| [0.4,0.7,"images/dashcam.jpeg","car,sky,road,grassland,trees"] | |
| ], | |
| # allow_flagging=False, | |
| # analytics_enabled=False, | |
| ) | |
| iface.launch() | |