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version 1.5

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This commit is contained in:
yadonglu
2024-11-26 12:48:03 -08:00
parent a14c4010eb a135e471f8
commit 7021ad8917
15 changed files with 432 additions and 169 deletions
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179
utils.py
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@@ -75,10 +75,12 @@ def get_yolo_model(model_path):
@torch.inference_mode()
def get_parsed_content_icon(filtered_boxes, ocr_bbox, image_source, caption_model_processor, prompt=None):
def get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=None, batch_size=32):
# Number of samples per batch, --> 256 roughly takes 23 GB of GPU memory for florence model
to_pil = ToPILImage()
if ocr_bbox:
non_ocr_boxes = filtered_boxes[len(ocr_bbox):]
if starting_idx:
non_ocr_boxes = filtered_boxes[starting_idx:]
else:
non_ocr_boxes = filtered_boxes
croped_pil_image = []
@@ -94,25 +96,24 @@ def get_parsed_content_icon(filtered_boxes, ocr_bbox, image_source, caption_mode
prompt = "<CAPTION>"
else:
prompt = "The image shows"
batch_size = 10 # Number of samples per batch
generated_texts = []
device = model.device
for i in range(0, len(croped_pil_image), batch_size):
start = time.time()
batch = croped_pil_image[i:i+batch_size]
if model.device.type == 'cuda':
inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device, dtype=torch.float16)
else:
inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device)
if 'florence' in model.config.name_or_path:
generated_ids = model.generate(input_ids=inputs["input_ids"],pixel_values=inputs["pixel_values"],max_new_tokens=1024,num_beams=3, do_sample=False)
generated_ids = model.generate(input_ids=inputs["input_ids"],pixel_values=inputs["pixel_values"],max_new_tokens=100,num_beams=3, do_sample=False)
else:
generated_ids = model.generate(**inputs, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=True, num_return_sequences=1) # temperature=0.01, do_sample=True,
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
generated_text = [gen.strip() for gen in generated_text]
generated_texts.extend(generated_text)
return generated_texts
@@ -192,6 +193,12 @@ def remove_overlap(boxes, iou_threshold, ocr_bbox=None):
ratio1, ratio2 = 0, 0
return max(intersection / union, ratio1, ratio2)
def is_inside(box1, box2):
# return box1[0] >= box2[0] and box1[1] >= box2[1] and box1[2] <= box2[2] and box1[3] <= box2[3]
intersection = intersection_area(box1, box2)
ratio1 = intersection / box_area(box1)
return ratio1 > 0.95
boxes = boxes.tolist()
filtered_boxes = []
if ocr_bbox:
@@ -201,18 +208,104 @@ def remove_overlap(boxes, iou_threshold, ocr_bbox=None):
# if not any(IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2) for j, box2 in enumerate(boxes) if i != j):
is_valid_box = True
for j, box2 in enumerate(boxes):
# keep the smaller box
if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2):
is_valid_box = False
break
if is_valid_box:
# add the following 2 lines to include ocr bbox
if ocr_bbox:
if not any(IoU(box1, box3) > iou_threshold for k, box3 in enumerate(ocr_bbox)):
# only add the box if it does not overlap with any ocr bbox
if not any(IoU(box1, box3) > iou_threshold and not is_inside(box1, box3) for k, box3 in enumerate(ocr_bbox)):
filtered_boxes.append(box1)
else:
filtered_boxes.append(box1)
return torch.tensor(filtered_boxes)
def remove_overlap_new(boxes, iou_threshold, ocr_bbox=None):
'''
ocr_bbox format: [{'type': 'text', 'bbox':[x,y], 'interactivity':False, 'content':str }, ...]
boxes format: [{'type': 'icon', 'bbox':[x,y], 'interactivity':True, 'content':None }, ...]
'''
assert ocr_bbox is None or isinstance(ocr_bbox, List)
def box_area(box):
return (box[2] - box[0]) * (box[3] - box[1])
def intersection_area(box1, box2):
x1 = max(box1[0], box2[0])
y1 = max(box1[1], box2[1])
x2 = min(box1[2], box2[2])
y2 = min(box1[3], box2[3])
return max(0, x2 - x1) * max(0, y2 - y1)
def IoU(box1, box2):
intersection = intersection_area(box1, box2)
union = box_area(box1) + box_area(box2) - intersection + 1e-6
if box_area(box1) > 0 and box_area(box2) > 0:
ratio1 = intersection / box_area(box1)
ratio2 = intersection / box_area(box2)
else:
ratio1, ratio2 = 0, 0
return max(intersection / union, ratio1, ratio2)
def is_inside(box1, box2):
# return box1[0] >= box2[0] and box1[1] >= box2[1] and box1[2] <= box2[2] and box1[3] <= box2[3]
intersection = intersection_area(box1, box2)
ratio1 = intersection / box_area(box1)
return ratio1 > 0.95
# boxes = boxes.tolist()
filtered_boxes = []
if ocr_bbox:
filtered_boxes.extend(ocr_bbox)
# print('ocr_bbox!!!', ocr_bbox)
for i, box1_elem in enumerate(boxes):
box1 = box1_elem['bbox']
is_valid_box = True
for j, box2_elem in enumerate(boxes):
# keep the smaller box
box2 = box2_elem['bbox']
if i != j and IoU(box1, box2) > iou_threshold and box_area(box1) > box_area(box2):
is_valid_box = False
break
if is_valid_box:
# add the following 2 lines to include ocr bbox
if ocr_bbox:
# only add the box if it does not overlap with any ocr bbox
box_added = False
for box3_elem in ocr_bbox:
if not box_added:
box3 = box3_elem['bbox']
if is_inside(box3, box1): # ocr inside icon
box_added = True
# delete the box3_elem from ocr_bbox
try:
filtered_boxes.append({'type': 'text', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': box3_elem['content']})
filtered_boxes.remove(box3_elem)
except:
continue
break
elif is_inside(box1, box3): # icon inside ocr
box_added = True
try:
filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None})
filtered_boxes.remove(box3_elem)
except:
continue
break
else:
continue
if not box_added:
filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None})
else:
filtered_boxes.append(box1)
return filtered_boxes # torch.tensor(filtered_boxes)
def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
transform = T.Compose(
[
@@ -280,17 +373,23 @@ def predict(model, image, caption, box_threshold, text_threshold):
return boxes, logits, phrases
def predict_yolo(model, image_path, box_threshold, imgsz):
def predict_yolo(model, image_path, box_threshold, imgsz, scale_img, iou_threshold=0.7):
""" Use huggingface model to replace the original model
"""
# model = model['model']
result = model.predict(
source=image_path,
conf=box_threshold,
imgsz=imgsz
# iou=0.5, # default 0.7
)
if scale_img:
result = model.predict(
source=image_path,
conf=box_threshold,
imgsz=imgsz,
iou=iou_threshold, # default 0.7
)
else:
result = model.predict(
source=image_path,
conf=box_threshold,
iou=iou_threshold, # default 0.7
)
boxes = result[0].boxes.xyxy#.tolist() # in pixel space
conf = result[0].boxes.conf
phrases = [str(i) for i in range(len(boxes))]
@@ -298,19 +397,15 @@ def predict_yolo(model, image_path, box_threshold, imgsz):
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,imgsz=640):
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=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
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)
else:
xyxy, logits, phrases = predict_yolo(model=model, image_path=img_path, box_threshold=BOX_TRESHOLD, imgsz=imgsz)
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 = 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))]
@@ -323,7 +418,20 @@ def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_
else:
print('no ocr bbox!!!')
ocr_bbox = None
filtered_boxes = remove_overlap(boxes=xyxy, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox)
# filtered_boxes = remove_overlap(boxes=xyxy, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox)
# starting_idx = len(ocr_bbox)
# print('len(filtered_boxes):', len(filtered_boxes), starting_idx)
ocr_bbox_elem = [{'type': 'text', 'bbox':box, 'interactivity':False, 'content':txt} for box, txt in zip(ocr_bbox, ocr_text)]
xyxy_elem = [{'type': 'icon', 'bbox':box, 'interactivity':True, 'content':None} for box in xyxy.tolist()]
filtered_boxes = remove_overlap_new(boxes=xyxy_elem, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox_elem)
# sort the filtered_boxes so that the one with 'content': None is at the end, and get the index of the first 'content': None
filtered_boxes_elem = sorted(filtered_boxes, key=lambda x: x['content'] is None)
# get the index of the first 'content': None
starting_idx = next((i for i, box in enumerate(filtered_boxes_elem) if box['content'] is None), -1)
filtered_boxes = torch.tensor([box['bbox'] for box in filtered_boxes_elem])
# get parsed icon local semantics
if use_local_semantics:
@@ -331,10 +439,14 @@ def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_
if 'phi3_v' in caption_model.config.model_type:
parsed_content_icon = get_parsed_content_icon_phi3v(filtered_boxes, ocr_bbox, image_source, caption_model_processor)
else:
parsed_content_icon = get_parsed_content_icon(filtered_boxes, ocr_bbox, image_source, caption_model_processor, prompt=prompt)
parsed_content_icon = get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=prompt,batch_size=batch_size)
ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)]
icon_start = len(ocr_text)
parsed_content_icon_ls = []
# fill the filtered_boxes_elem None content with parsed_content_icon in order
for i, box in enumerate(filtered_boxes_elem):
if box['content'] is None:
box['content'] = parsed_content_icon.pop(0)
for i, txt in enumerate(parsed_content_icon):
parsed_content_icon_ls.append(f"Icon Box ID {str(i+icon_start)}: {txt}")
parsed_content_merged = ocr_text + parsed_content_icon_ls
@@ -361,7 +473,7 @@ def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_
label_coordinates = {k: [v[0]/w, v[1]/h, v[2]/w, v[3]/h] for k, v in label_coordinates.items()}
assert w == annotated_frame.shape[1] and h == annotated_frame.shape[0]
return encoded_image, label_coordinates, parsed_content_merged
return encoded_image, label_coordinates, filtered_boxes_elem
def get_xywh(input):
@@ -383,9 +495,14 @@ def get_xywh_yolo(input):
def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False):
if use_paddleocr:
if easyocr_args is None:
text_threshold = 0.5
else:
text_threshold = easyocr_args['text_threshold']
result = paddle_ocr.ocr(image_path, cls=False)[0]
coord = [item[0] for item in result]
text = [item[1][0] for item in result]
conf = [item[1] for item in result]
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]
else: # EasyOCR
if easyocr_args is None:
easyocr_args = {}