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Add icon detect image size option

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This commit is contained in:
Billy Cao
2024-11-04 09:41:06 +08:00
parent 4ae782f044
commit f907013aa9
3 changed files with 12 additions and 7 deletions
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2
demo.ipynb
View File

@@ -104,7 +104,7 @@
"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",
"\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=True, iou_threshold=0.1)\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=True, iou_threshold=0.1, imgsz=640)\n",
"\n",
"\n"
]

10
gradio_demo.py
View File

@@ -61,7 +61,8 @@ def process(
image_input,
box_threshold,
iou_threshold,
use_paddleocr
use_paddleocr,
imgsz
) -> Optional[Image.Image]:
image_save_path = 'imgs/saved_image_demo.png'
@@ -71,7 +72,7 @@ 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}, 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)
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)
@@ -93,6 +94,8 @@ with gr.Blocks() as demo:
label='IOU Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.1)
use_paddleocr_component = gr.Checkbox(
label='Use PaddleOCR', value=True)
imgsz_component = gr.Slider(
label='Icon Detect Image Size', minimum=640, maximum=1920, step=32, value=640)
submit_button_component = gr.Button(
value='Submit', variant='primary')
with gr.Column():
@@ -105,7 +108,8 @@ with gr.Blocks() as demo:
image_input_component,
box_threshold_component,
iou_threshold_component,
use_paddleocr_component
use_paddleocr_component,
imgsz_component
],
outputs=[image_output_component, text_output_component]
)

7
utils.py
View File

@@ -280,7 +280,7 @@ def predict(model, image, caption, box_threshold, text_threshold):
return boxes, logits, phrases
def predict_yolo(model, image_path, box_threshold):
def predict_yolo(model, image_path, box_threshold, imgsz):
""" Use huggingface model to replace the original model
"""
# model = model['model']
@@ -288,6 +288,7 @@ def predict_yolo(model, image_path, box_threshold):
result = model.predict(
source=image_path,
conf=box_threshold,
imgsz=imgsz
# iou=0.5, # default 0.7
)
boxes = result[0].boxes.xyxy#.tolist() # in pixel space
@@ -297,7 +298,7 @@ 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_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,imgsz=640):
""" ocr_bbox: list of xyxy format bbox
"""
TEXT_PROMPT = "clickable buttons on the screen"
@@ -309,7 +310,7 @@ def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_
if False: # TODO
xyxy, logits, phrases = predict(model=model, image=image_source, caption=TEXT_PROMPT, box_threshold=BOX_TRESHOLD, text_threshold=TEXT_TRESHOLD)
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_TRESHOLD, imgsz=imgsz)
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))]