MultimodalOCR/OCRBench/scripts/Genimi.py

import pathlib
import textwrap
from argparse import ArgumentParser
import google.generativeai as genai
import json
from PIL import Image
from IPython.display import display
from IPython.display import Markdown
from tqdm import tqdm
import os
import sys
OCRBench_score = {"Regular Text Recognition":0,"Irregular Text Recognition":0,"Artistic Text Recognition":0,"Handwriting Recognition":0,
"Digit String Recognition":0,"Non-Semantic Text Recognition":0,"Scene Text-centric VQA":0,"Doc-oriented VQA":0,"Doc-oriented VQA":0,
"Key Information Extraction":0,"Handwritten Mathematical Expression Recognition":0}
AllDataset_score = {"IIIT5K":0,"svt":0,"IC13_857":0,"IC15_1811":0,"svtp":0,"ct80":0,"cocotext":0,"ctw":0,"totaltext":0,"HOST":0,"WOST":0,"WordArt":0,"IAM":0,"ReCTS":0,"ORAND":0,"NonSemanticText":0,"SemanticText":0,
"STVQA":0,"textVQA":0,"ocrVQA":0,"ESTVQA":0,"ESTVQA_cn":0,"docVQA":0,"infographicVQA":0,"ChartQA":0,"ChartQA_Human":0,"FUNSD":0,"SROIE":0,"POIE":0,"HME100k":0}
num_all = {"IIIT5K":0,"svt":0,"IC13_857":0,"IC15_1811":0,"svtp":0,"ct80":0,"cocotext":0,"ctw":0,"totaltext":0,"HOST":0,"WOST":0,"WordArt":0,"IAM":0,"ReCTS":0,"ORAND":0,"NonSemanticText":0,"SemanticText":0,
"STVQA":0,"textVQA":0,"ocrVQA":0,"ESTVQA":0,"ESTVQA_cn":0,"docVQA":0,"infographicVQA":0,"ChartQA":0,"ChartQA_Human":0,"FUNSD":0,"SROIE":0,"POIE":0,"HME100k":0}
def save_json(json_list,save_path):
    with open(save_path, 'w') as file:
        json.dump(json_list, file,indent=4)
def _get_args():
    parser = ArgumentParser()
    parser.add_argument("--image_folder", type=str, default="./OCRBench_Images")
    parser.add_argument("--output_path", type=str, default="./results")
    parser.add_argument("--OCRBench_file", type=str, default="./OCRBench/OCRBench.json")
    parser.add_argument("--GOOGLE_API_KEY", type=str, default="")
    parser.add_argument("--model", type=str, default="gemini-pro-vision")
    args = parser.parse_args()
    return args


if __name__ == "__main__":
    args = _get_args()
    genai.configure(api_key=args.GOOGLE_API_KEY)
    model = genai.GenerativeModel(args.model)

    if os.path.exists(os.path.join(args.output_path,f"{args.model}.json")):
        data_path = os.path.join(args.output_path,f"{args.model}.json")
    else:
        data_path = args.OCRBench_file
    
    with open(data_path, "r") as f:
        data = json.load(f)
    for i in tqdm(range(len(data))):
        img_path = os.path.join(args.image_folder, data[i]['image_path'])
        question = data[i]['question']
        if data[i].get("predict", 0)!=0:
            print(f"{img_path} predict exist, continue.")
            continue
        try:
            img = Image.open(img_path).convert("RGB")
            response = model.generate_content([question, img])
            data[i]['predict'] = response.text
            save_json(data, os.path.join(args.output_path,f"{args.model}.json"))
        except:
            print(f"{img_path}: API call failed.")
    for i in range(len(data)):
        data_type = data[i]["type"]
        dataset_name = data[i]["dataset_name"]
        answers = data[i]["answers"]
        if data[i].get('predict',0)==0:
            continue
        predict = data[i]['predict']
        data[i]['result'] = 0
        if dataset_name == "HME100k":
            if type(answers)==list:
                for j in range(len(answers)):
                    answer = answers[j].strip().replace("\n"," ").replace(" ","")
                    predict = predict.strip().replace("\n"," ").replace(" ","")
                    if answer in predict:
                        data[i]['result'] = 1
            else:
                answers = answers.strip().replace("\n"," ").replace(" ","")
                predict = predict.strip().replace("\n"," ").replace(" ","")
                if answers in predict:
                    data[i]['result'] = 1
        else:
            if type(answers)==list:
                for j in range(len(answers)):
                    answer = answers[j].lower().strip().replace("\n"," ")
                    predict = predict.lower().strip().replace("\n"," ")
                    if answer in predict:
                        data[i]['result'] = 1
            else:
                answers = answers.lower().strip().replace("\n"," ")
                predict = predict.lower().strip().replace("\n"," ")
                if answers in predict:
                    data[i]['result'] = 1
    save_json(data, os.path.join(args.output_path,f"{args.model}.json"))
    for i in range(len(data)):
        if data[i].get("result",100)==100:
            continue
        OCRBench_score[data[i]['type']] += data[i]['result']
    recognition_score = OCRBench_score['Regular Text Recognition']+OCRBench_score['Irregular Text Recognition']+OCRBench_score['Artistic Text Recognition']+OCRBench_score['Handwriting Recognition']+OCRBench_score['Digit String Recognition']+OCRBench_score['Non-Semantic Text Recognition']
    Final_score = recognition_score+OCRBench_score['Scene Text-centric VQA']+OCRBench_score['Doc-oriented VQA']+OCRBench_score['Key Information Extraction']+OCRBench_score['Handwritten Mathematical Expression Recognition']
    print("###########################OCRBench##############################")
    print(f"Text Recognition(Total 300):{recognition_score}")
    print("------------------Details of Recognition Score-------------------")
    print(f"Regular Text Recognition(Total 50): {OCRBench_score['Regular Text Recognition']}")
    print(f"Irregular Text Recognition(Total 50): {OCRBench_score['Irregular Text Recognition']}")
    print(f"Artistic Text Recognition(Total 50): {OCRBench_score['Artistic Text Recognition']}")
    print(f"Handwriting Recognition(Total 50): {OCRBench_score['Handwriting Recognition']}")
    print(f"Digit String Recognition(Total 50): {OCRBench_score['Digit String Recognition']}")
    print(f"Non-Semantic Text Recognition(Total 50): {OCRBench_score['Non-Semantic Text Recognition']}")
    print("----------------------------------------------------------------")
    print(f"Scene Text-centric VQA(Total 200): {OCRBench_score['Scene Text-centric VQA']}")
    print("----------------------------------------------------------------")
    print(f"Doc-oriented VQA(Total 200): {OCRBench_score['Doc-oriented VQA']}")
    print("----------------------------------------------------------------")
    print(f"Key Information Extraction(Total 200): {OCRBench_score['Key Information Extraction']}")
    print("----------------------------------------------------------------")
    print(f"Handwritten Mathematical Expression Recognition(Total 100): {OCRBench_score['Handwritten Mathematical Expression Recognition']}")
    print("----------------------Final Score-------------------------------")
    print(f"Final Score(Total 1000): {Final_score}")