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adaptive bbox width for asthetics

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yadonglu
2024-11-04 21:42:18 -08:00
parent a804a33bee
commit 6b4828ba66
3 changed files with 17 additions and 30 deletions
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__pycache__/utils.cpython-312.pyc
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14
demo.ipynb
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@@ -90,16 +90,18 @@
"image_path = 'imgs/google_page.png'\n",
"# image_path = 'imgs/windows_home.png'\n",
"image_path = 'imgs/windows_multitab.png'\n",
"draw_bbox_config = {\n",
" 'text_scale': 0.8,\n",
" 'text_thickness': 2,\n",
" 'text_padding': 3,\n",
" 'thickness': 3,\n",
"}\n",
"\n",
"BOX_TRESHOLD = 0.03\n",
"\n",
"image = Image.open(image_path)\n",
"image_rgb = image.convert('RGB')\n",
"box_overlay_ratio = image.size[0] / 3200\n",
"draw_bbox_config = {\n",
" 'text_scale': 0.8 * box_overlay_ratio,\n",
" 'text_thickness': max(int(2 * box_overlay_ratio), 1),\n",
" 'text_padding': max(int(3 * box_overlay_ratio), 1),\n",
" 'thickness': max(int(3 * box_overlay_ratio), 1),\n",
"}\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}, use_paddleocr=True)\n",
"text, ocr_bbox = ocr_bbox_rslt\n",

33
gradio_demo.py
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@@ -16,29 +16,6 @@ yolo_model = get_yolo_model(model_path='weights/icon_detect/best.pt')
caption_model_processor = get_caption_model_processor(model_name="florence2", model_name_or_path="weights/icon_caption_florence")
# caption_model_processor = get_caption_model_processor(model_name="blip2", model_name_or_path="weights/icon_caption_blip2")
platform = 'pc'
if platform == 'pc':
draw_bbox_config = {
'text_scale': 0.8,
'text_thickness': 2,
'text_padding': 2,
'thickness': 2,
}
elif platform == 'web':
draw_bbox_config = {
'text_scale': 0.8,
'text_thickness': 2,
'text_padding': 3,
'thickness': 3,
}
elif platform == 'mobile':
draw_bbox_config = {
'text_scale': 0.8,
'text_thickness': 2,
'text_padding': 3,
'thickness': 3,
}
MARKDOWN = """
@@ -67,12 +44,20 @@ def process(
image_save_path = 'imgs/saved_image_demo.png'
image_input.save(image_save_path)
image = Image.open(image_save_path)
box_overlay_ratio = image.size[0] / 3200
draw_bbox_config = {
'text_scale': 0.8 * box_overlay_ratio,
'text_thickness': max(int(2 * box_overlay_ratio), 1),
'text_padding': max(int(3 * box_overlay_ratio), 1),
'thickness': max(int(3 * box_overlay_ratio), 1),
}
# import pdb; pdb.set_trace()
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}, use_paddleocr=use_paddleocr)
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_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=iou_threshold, imgsz=imgsz)
dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_save_path, yolo_model, BOX_TRESHOLD = 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=iou_threshold, imgsz=imgsz)
image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
print('finish processing')
parsed_content_list = '\n'.join(parsed_content_list)