Spaces:
Sleeping
Sleeping
File size: 3,301 Bytes
0e78cbf 9b2e4f0 0e78cbf 7fef6fd 0e78cbf 7fef6fd 0e78cbf 7fef6fd 0e78cbf 7fef6fd 0e78cbf 7fef6fd 0e78cbf 7fef6fd 0e78cbf 7fef6fd 0e78cbf 7fef6fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import cv2
from matplotlib import pyplot as plt
import torch
import numpy as np
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
from segmentation_mask_overlay import overlay_masks
from typing import List
import logging
class CLIPSEG:
def __init__(self,model_name = "CIDAS/clipseg-rd64-refined",threshould=0.60):
self.clip_processor = CLIPSegProcessor.from_pretrained(model_name)
self.clip_model = CLIPSegForImageSegmentation.from_pretrained(model_name)
self.threshould = threshould
self.clip_model.to('cpu')
@ staticmethod
def create_single_mask(predicted_masks , color = None ):
if len(predicted_masks)>0:
mask_image = np.zeros_like(predicted_masks[0])
else:
mask_image = np.zeros(shape=(352,352),dtype=np.unit8)
for masks in predicted_masks:
mask_image = np.bitwise_or(mask_image,masks)
return mask_image
@staticmethod
def create_rgb_mask(mask,color=None):
color = tuple(np.random.choice(range(128,255), size=3))
gray_3_channel = cv2.merge((mask, mask, mask))
gray_3_channel[mask==255] = 255 # for orignial color
return gray_3_channel.astype(np.uint8)
def get_segmentation_mask(self,image_path:str,object_prompts:List):
image = cv2.cvtColor(cv2.imread(image_path),cv2.COLOR_BGR2RGB)
logging.info("objects found out from the image :{}".format(object_prompts))
predicted_masks = []
inputs = self.clip_processor(
text=object_prompts,
images=[image] * len(object_prompts),
padding="max_length",
return_tensors="pt",
)
with torch.no_grad(): # Use 'torch.no_grad()' to disable gradient computation
outputs = self.clip_model(**inputs)
preds = outputs.logits.unsqueeze(1)
# detections = outputs.logits[0] # Assuming class index 0
for i in range(preds.shape[0]):
predicted_mask = torch.sigmoid(preds[i][0]).detach().cpu().numpy()
predicted_mask = np.where(predicted_mask>self.threshould, 255,0)
predicted_masks.append(predicted_mask)
final_mask = self.create_single_mask(predicted_masks)
rgb_predicted_mask = self.create_rgb_mask(final_mask)
resize_image = cv2.resize(image,(352,352))
rgb_mask_img = cv2.bitwise_and(resize_image,rgb_predicted_mask )
# mask_labels = [f"{prompt}_{i}" for i,prompt in enumerate(object_prompts)]
# cmap = plt.cm.tab20(np.arange(len(mask_labels)))[..., :-1]
# bool_masks = [predicted_mask.astype('bool') for predicted_mask in predicted_masks]
# final_mask = overlay_masks(resize_image,np.stack(bool_masks,-1),labels=mask_labels,colors=cmap,alpha=0.5,beta=0.7)
try:
cv2.imwrite('final_mask.png',rgb_mask_img)
return 'Segmentation image created : final_mask.png'
except Exception as e:
logging.error("Error while saving the final mask :",e)
return "unable to create a mask image "
if __name__=="__main__":
clip = CLIPSEG()
obj = clip.get_segmentation_mask(image_path="../image_store/demo.jpg",object_prompts=['sand','dog'])
|