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nit: update spelling #13

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8 changes: 4 additions & 4 deletions demo.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -458,30 +458,30 @@
" 'text_padding': 3,\n",
" 'thickness': 3,\n",
" }\n",
" BOX_TRESHOLD = 0.05\n",
" BOX_THRESHOLD = 0.05\n",
"elif platform == 'web':\n",
" draw_bbox_config = {\n",
" 'text_scale': 0.8,\n",
" 'text_thickness': 2,\n",
" 'text_padding': 3,\n",
" 'thickness': 3,\n",
" }\n",
" BOX_TRESHOLD = 0.05\n",
" BOX_THRESHOLD = 0.05\n",
"elif platform == 'mobile':\n",
" draw_bbox_config = {\n",
" 'text_scale': 0.8,\n",
" 'text_thickness': 2,\n",
" 'text_padding': 3,\n",
" 'thickness': 3,\n",
" }\n",
" BOX_TRESHOLD = 0.05\n",
" BOX_THRESHOLD = 0.05\n",
"image = Image.open(image_path)\n",
"image_rgb = image.convert('RGB')\n",
"\n",
"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':0.9})\n",
"text, ocr_bbox = ocr_bbox_rslt\n",
"\n",
"dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=False, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,use_local_semantics=False)\n",
"dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, som_model, BOX_THRESHOLD = BOX_THRESHOLD, output_coord_in_ratio=False, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,use_local_semantics=False)\n",
"\n",
"\n"
]
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8 changes: 4 additions & 4 deletions gradio_demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,23 +22,23 @@
'text_padding': 2,
'thickness': 2,
}
BOX_TRESHOLD = 0.05
BOX_THRESHOLD = 0.05
elif platform == 'web':
draw_bbox_config = {
'text_scale': 0.8,
'text_thickness': 2,
'text_padding': 3,
'thickness': 3,
}
BOX_TRESHOLD = 0.05
BOX_THRESHOLD = 0.05
elif platform == 'mobile':
draw_bbox_config = {
'text_scale': 0.8,
'text_thickness': 2,
'text_padding': 3,
'thickness': 3,
}
BOX_TRESHOLD = 0.05
BOX_THRESHOLD = 0.05



Expand Down Expand Up @@ -70,7 +70,7 @@ def process(
ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_save_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9})
text, ocr_bbox = ocr_bbox_rslt
print('prompt:', prompt)
dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_save_path, yolo_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,iou_threshold=0.3,prompt=prompt)
dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_save_path, yolo_model, BOX_THRESHOLD = BOX_THRESHOLD, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,iou_threshold=0.3,prompt=prompt)
image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
print('finish processing')
parsed_content_list = '\n'.join(parsed_content_list)
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10 changes: 5 additions & 5 deletions omniparser.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
'text_padding': 3,
'thickness': 3,
},
'BOX_TRESHOLD': 0.05
'BOX_THRESHOLD': 0.05
}


Expand All @@ -35,11 +35,11 @@ def parse(self, image_path: str):
text, ocr_bbox = ocr_bbox_rslt

draw_bbox_config = self.config['draw_bbox_config']
BOX_TRESHOLD = self.config['BOX_TRESHOLD']
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=False, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=None, ocr_text=text,use_local_semantics=False)
BOX_THRESHOLD = self.config['BOX_THRESHOLD']
dino_labeled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_THRESHOLD = BOX_THRESHOLD, output_coord_in_ratio=False, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=None, ocr_text=text,use_local_semantics=False)

image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
# formating output
image = Image.open(io.BytesIO(base64.b64decode(dino_labeled_img)))
# formatting output
return_list = [{'from': 'omniparser', 'shape': {'x':coord[0], 'y':coord[1], 'width':coord[2], 'height':coord[3]},
'text': parsed_content_list[i].split(': ')[1], 'type':'text'} for i, (k, coord) in enumerate(label_coordinates.items()) if i < len(parsed_content_list)]
return_list.extend(
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10 changes: 5 additions & 5 deletions utils.py
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Expand Up @@ -278,19 +278,19 @@ def predict_yolo(model, image_path, box_threshold):
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):
def get_som_labeled_img(img_path, model=None, BOX_THRESHOLD = 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):
""" ocr_bbox: list of xyxy format bbox
"""
TEXT_PROMPT = "clickable buttons on the screen"
# BOX_TRESHOLD = 0.02 # 0.05/0.02 for web and 0.1 for mobile
TEXT_TRESHOLD = 0.01 # 0.9 # 0.01
# BOX_THRESHOLD = 0.02 # 0.05/0.02 for web and 0.1 for mobile
TEXT_THRESHOLD = 0.01 # 0.9 # 0.01
image_source = Image.open(img_path).convert("RGB")
w, h = image_source.size
# import pdb; pdb.set_trace()
if False: # TODO
xyxy, logits, phrases = predict(model=model, image=image_source, caption=TEXT_PROMPT, box_threshold=BOX_TRESHOLD, text_threshold=TEXT_TRESHOLD)
xyxy, logits, phrases = predict(model=model, image=image_source, caption=TEXT_PROMPT, box_threshold=BOX_THRESHOLD, text_threshold=TEXT_THRESHOLD)
else:
xyxy, logits, phrases = predict_yolo(model=model, image_path=img_path, box_threshold=BOX_TRESHOLD)
xyxy, logits, phrases = predict_yolo(model=model, image_path=img_path, box_threshold=BOX_THRESHOLD)
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))]
Expand Down