test_rect.py

# %%
import matplotlib.style
from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
import torch
from pathlib import Path
from PIL import Image
from PIL import ImageDraw
import numpy as np
from collections import namedtuple
import sys
print(sys.version_info)
#%%
class Florence:
    def __init__(self, model_id:str, hack=False):
        if hack:
            return
        self.model = (
            AutoModelForCausalLM.from_pretrained(
                model_id, trust_remote_code=True, torch_dtype="auto"
            )
            .eval()
            .cuda()
        )
        self.processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
        self.model_id = model_id
    def run(self, img:Image, task_prompt:str, extra_text:str|None=None):
        model, processor = self.model, self.processor
        prompt = task_prompt + (extra_text if extra_text else "")
        inputs = processor(text=prompt, images=img, return_tensors="pt").to(
            "cuda", torch.float16
        )
        generated_ids = model.generate(
            input_ids=inputs["input_ids"],
            pixel_values=inputs["pixel_values"],
            max_new_tokens=1024,
            early_stopping=False,
            do_sample=False,
            num_beams=3,
        )
        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
        parsed_answer = processor.post_process_generation(
            generated_text,
            task=task_prompt,
            image_size=(img.width, img.height),
        )
        return parsed_answer
def model_init():
    fl = Florence("microsoft/Florence-2-large", hack=False)
    fl_ft = Florence("microsoft/Florence-2-large-ft", hack=False)
    return fl, fl_ft
# florence-2 tasks
TASK_OD = "<OD>"
TASK_SEGMENTATION = '<REFERRING_EXPRESSION_SEGMENTATION>'
TASK_CAPTION = "<CAPTION_TO_PHRASE_GROUNDING>"
TASK_OCR = "<OCR_WITH_REGION>"
TASK_GROUNDING = "<CAPTION_TO_PHRASE_GROUNDING>"

#%%
from skimage.measure import LineModelND, ransac
def get_polygons(fl:Florence, img2:Image, prompt):
    parsed_answer = fl.run(img2, TASK_SEGMENTATION, prompt)
    assert len(parsed_answer) == 1
    k,v = parsed_answer.popitem()
    assert 'polygons' in v
    assert len(v['polygons']) == 1
    polygons = v['polygons'][0]
    return polygons

def get_ocr(fl:Florence, img2:Image):
    parsed_answer = fl.run(img2, TASK_OCR)
    assert len(parsed_answer)==1
    k,v = parsed_answer.popitem()
    return v
imgs = list(Path('images/other').glob('*.jpg'))
meter_labels = list(map(str, range(0, 600, 100)))

def read_meter(img, fl:Florence, fl_ft:Florence):
    if isinstance(img, str) or isinstance(img, Path):
        print(img)
        img = Image.open(img)
    red_polygons = get_polygons(fl, img, 'red triangle pointer')
    # draw the rectangle
    draw = ImageDraw.Draw(img)
    ocr_text = {}
    ocr1 = get_ocr(fl, img)
    ocr2 = get_ocr(fl_ft, img)
    quad_boxes = ocr1['quad_boxes']+ocr2['quad_boxes']
    labels = ocr1['labels']+ocr2['labels']
    for quad_box, label in zip(quad_boxes, labels):
        if label in meter_labels:
            ocr_text[int(label)] = quad_box
    for label, quad_box in ocr_text.items():
        draw.polygon(quad_box, outline='green', width=3)
        draw.text((quad_box[0], quad_box[1]-10), str(label), fill='green', anchor='ls')
    text_centers = np.array(list(ocr_text.values())).reshape(-1, 4, 2).mean(axis=1)
    lm = LineModelND()
    lm.estimate(text_centers)
    orign, direction = lm.params
    # project text centers to the line
    text_centers_shifted = text_centers - orign
    text_centers_norm = text_centers_shifted @ direction
    lm2 = LineModelND()
    I = np.array(list(ocr_text.keys()))
    L = text_centers_norm
    data = np.stack([I, L], axis=1)
    lm2.estimate(data)
    ls = lm2.predict(list(range(0, 600, 100)))[:, 1]
    x0, y0 = ls[0] * direction + orign
    x1, y1 = ls[-1] * direction + orign
    draw.line((x0, y0, x1, y1), fill='yellow', width=3)
    for l in ls:
        x, y = l * direction + orign
        draw.ellipse((x-5, y-5, x+5, y+5), outline='yellow', width=3)
    red_coords = np.concatenate(red_polygons).reshape(-1, 2)
    red_shifted = red_coords - orign
    red_norm = red_shifted @ direction
    red_l = red_norm.mean()
    red_i = np.clip(lm2.predict_x([red_l]), 0, 500)
    red_l = lm2.predict_y(red_i)[0]    
    red_center = red_l * direction + orign
    draw.ellipse((red_center[0]-5, red_center[1]-5, red_center[0]+5, red_center[1]+5), outline='red', width=3)
    return red_i[0], img



def main():
    fl, fl_ft = model_init()
    for img_fn in imgs:        
        print(img_fn)
        img = Image.open(img_fn)
        red_i, img2 = read_meater(img, fl, fl_ft)
        print(red_i)
        display(img2)
if __name__ == '__main__':
    from IPython.display import display
    main()

#%%