remove need to write to disk

demo/remote_request.py

@@ -4,11 +4,12 @@ import sys
 import os
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
-from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model
+from utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, get_ocr_bbox
 import torch
 from PIL import Image
 from typing import Dict, Tuple, List
 import base64
+import io
 
 
 config = {
@@ -30,12 +31,9 @@ class Omniparser(object):
         print('Omniparser initialized!!!')
 
     def parse(self, image_base64: str):
-        image_path = '../imgs/demo_image.jpg'
-        with open(image_path, "wb") as fh:
-            fh.write(base64.b64decode(image_base64))
-        print('Parsing image:', image_path)
-
-        image = Image.open(image_path)
+        # Convert base64 to image directly without saving to disk
+        image_bytes = base64.b64decode(image_base64)
+        image = Image.open(io.BytesIO(image_bytes))
         print('image size:', image.size)
         
         box_overlay_ratio = max(image.size) / 3200
@@ -47,11 +45,8 @@ class Omniparser(object):
         }
         BOX_TRESHOLD = config['BOX_TRESHOLD']
 
-        ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.8}, use_paddleocr=True)
-        text, ocr_bbox = ocr_bbox_rslt
-        dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128)
-        with open('../imgs/demo_image_som.jpg', "wb") as fh:
-            fh.write(base64.b64decode(dino_labled_img))
+        text, ocr_bbox = get_ocr_bbox(image)
+        dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128)
 
         return dino_labled_img, parsed_content_list
     

utils.py

@@ -35,7 +35,7 @@ import base64
 import os
 import ast
 import torch
-from typing import Tuple, List
+from typing import Tuple, List, Union
 from torchvision.ops import box_convert
 import re
 from torchvision.transforms import ToPILImage
@@ -384,20 +384,20 @@ def predict(model, image, caption, box_threshold, text_threshold):
     return boxes, logits, phrases
 
 
-def predict_yolo(model, image_path, box_threshold, imgsz, scale_img, iou_threshold=0.7):
+def predict_yolo(model, image, box_threshold, imgsz, scale_img, iou_threshold=0.7):
     """ Use huggingface model to replace the original model
     """
     # model = model['model']
     if scale_img:
         result = model.predict(
-        source=image_path,
+        source=image,
         conf=box_threshold,
         imgsz=imgsz,
         iou=iou_threshold, # default 0.7
         )
     else:
         result = model.predict(
-        source=image_path,
+        source=image,
         conf=box_threshold,
         iou=iou_threshold, # default 0.7
         )
@@ -408,15 +408,21 @@ def predict_yolo(model, image_path, box_threshold, imgsz, scale_img, iou_thresho
     return boxes, conf, phrases
 
 
-def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=64):
-    """ ocr_bbox: list of xyxy format bbox
+def get_som_labeled_img(image_source: Union[str, Image.Image], model=None, BOX_TRESHOLD=0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=64):
+    """Process either an image path or Image object
+    
+    Args:
+        image_source: Either a file path (str) or PIL Image object
+        ...
     """
-    image_source = Image.open(img_path).convert("RGB")
+    if isinstance(image_source, str):
+        image_source = Image.open(image_source).convert("RGB")
+    
     w, h = image_source.size
     if not imgsz:
         imgsz = (h, w)
     # print('image size:', w, h)
-    xyxy, logits, phrases = predict_yolo(model=model, image_path=img_path, box_threshold=BOX_TRESHOLD, imgsz=imgsz, scale_img=scale_img, iou_threshold=0.1)
+    xyxy, logits, phrases = predict_yolo(model=model, image=image_source, box_threshold=BOX_TRESHOLD, imgsz=imgsz, scale_img=scale_img, iou_threshold=0.1)
     xyxy = xyxy / torch.Tensor([w, h, w, h]).to(xyxy.device)
     image_source = np.asarray(image_source)
     phrases = [str(i) for i in range(len(phrases))]
@@ -545,5 +551,13 @@ def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_
         # print('bounding box!!!', bb)
     return (text, bb), goal_filtering
 
+def get_ocr_bbox(image):
+    text_threshold = 0.8
+    result = paddle_ocr.ocr(image, cls=False)[0]
+    coord = [item[0] for item in result if item[1][1] > text_threshold]
+    text = [item[1][0] for item in result if item[1][1] > text_threshold]
+    bb = [get_xyxy(item) for item in coord]
+    return text, bb
+