deepseek-ocr 구동 테스트

config/model_settings.py

@@ -22,8 +22,8 @@ MODEL_PATH = "deepseek-ai/DeepSeek-OCR"  # change to your model path
 # Omnidocbench images path: run_dpsk_ocr_eval_batch.py
 
 
-INPUT_PATH = "/workspace/input"
-OUTPUT_PATH = "/workspace/output"
+INPUT_PATH = "/workspace/test/input"
+OUTPUT_PATH = "/workspace/test/output"
 # PROMPT = f"{PROMPT_TEXT.strip()}"
 PROMPT = "<image>\n<|grounding|>Convert the document to markdown."
 # PROMPT = '<image>\nFree OCR.'

test/output/result/2016-08556-211156.json

@@ -0,0 +1,12 @@
+{
+    "filename": "2016-08556-211156.pdf",
+    "model": {
+        "ocr_model": "deepseek-ocr"
+    },
+    "time": {
+        "duration_sec": "6.59",
+        "started_at": 1762395627.3137395,
+        "ended_at": 1762395633.9023185
+    },
+    "parsed": "\n수신자 한국수출입은행장 \n\n참조 EDCF Operations Department 2 \n\n제 목 방글라데시 반다주리 상수도 사업 컨설턴트 고용을 위한 문제유발 행위 불개입 확약서 \n\n1. 귀 은행의 무궁한 발전을 기원합니다. \n\n2. 표제 사업 컨설턴트 고용을 위한 제안요청서 조항에 따라 입찰 참여를 위한 \"문제유발행위 불개입 확약 서\"를 \n\n첨부와 같이 제출하오니, 참조해주시기 바랍니다. \n\n* 첨부: 문제유발행위 불개입 확약서 원본 1부. \n\n주식회사 삼안 대표이사 \n\n![](images/0_0.jpg)\n\n \n\n수신처 : Ms. Jiyoon Park, Sr. Loan Officer \n\n문서번호 201609-4495 (2016-09-22) \n\n서울 광진구 광나루로56실 85 프라임센터 34층 해외사업실 \n\n전화 02)6488-8095 \n\n담당 : 김관영 \n\nFAX 02)6488-8080 \n\n/ http://www.samaneng.com \n\n이메일 shkim5@samaneng.com<｜end▁of▁sentence｜>\n<--- Page Split --->\n"
+}

test/test.py

@@ -0,0 +1,397 @@
+import io
+import json
+import os
+import re
+import time
+
+import config.model_settings as config
+import fitz
+import img2pdf
+import numpy as np
+from config.env_setup import setup_environment
+from PIL import Image, ImageDraw, ImageFont, ImageOps
+from services.deepseek_ocr import DeepseekOCRForCausalLM
+from services.process.image_process import DeepseekOCRProcessor
+from services.process.ngram_norepeat import NoRepeatNGramLogitsProcessor
+from tqdm import tqdm
+from vllm import LLM, SamplingParams
+from vllm.model_executor.models.registry import ModelRegistry
+
+setup_environment()
+
+ModelRegistry.register_model("DeepseekOCRForCausalLM", DeepseekOCRForCausalLM)
+
+
+class Colors:
+    RED = "\033[31m"
+    GREEN = "\033[32m"
+    YELLOW = "\033[33m"
+    BLUE = "\033[34m"
+    RESET = "\033[0m"
+
+
+# --- PDF/Image Processing Functions (from run_dpsk_ocr_*.py) ---
+
+
+def pdf_to_images_high_quality(pdf_path, dpi=144):
+    images = []
+    pdf_document = fitz.open(pdf_path)
+    zoom = dpi / 72.0
+    matrix = fitz.Matrix(zoom, zoom)
+    for page_num in range(pdf_document.page_count):
+        page = pdf_document[page_num]
+        pixmap = page.get_pixmap(matrix=matrix, alpha=False)
+        Image.MAX_IMAGE_PIXELS = None
+        img_data = pixmap.tobytes("png")
+        img = Image.open(io.BytesIO(img_data))
+        if img.mode in ("RGBA", "LA"):
+            background = Image.new("RGB", img.size, (255, 255, 255))
+            background.paste(img, mask=img.split()[-1] if img.mode == "RGBA" else None)
+            img = background
+        images.append(img)
+    pdf_document.close()
+    return images
+
+
+def pil_to_pdf_img2pdf(pil_images, output_path):
+    if not pil_images:
+        return
+    image_bytes_list = []
+    for img in pil_images:
+        if img.mode != "RGB":
+            img = img.convert("RGB")
+        img_buffer = io.BytesIO()
+        img.save(img_buffer, format="JPEG", quality=95)
+        image_bytes_list.append(img_buffer.getvalue())
+    try:
+        pdf_bytes = img2pdf.convert(image_bytes_list)
+        with open(output_path, "wb") as f:
+            f.write(pdf_bytes)
+    except Exception as e:
+        print(f"Error creating PDF: {e}")
+
+
+def re_match(text):
+    pattern = r"(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)"
+    matches = re.findall(pattern, text, re.DOTALL)
+    mathes_image = [m[0] for m in matches if "<|ref|>image<|/ref|>" in m[0]]
+    mathes_other = [m[0] for m in matches if "<|ref|>image<|/ref|>" not in m[0]]
+    return matches, mathes_image, mathes_other
+
+
+def extract_coordinates_and_label(ref_text, image_width, image_height):
+    try:
+        label_type = ref_text[1]
+        cor_list = eval(ref_text[2])
+        return (label_type, cor_list)
+    except Exception as e:
+        print(f"Error extracting coordinates: {e}")
+        return None
+
+
+def draw_bounding_boxes(image, refs, jdx=None):
+    image_width, image_height = image.size
+    img_draw = image.copy()
+    draw = ImageDraw.Draw(img_draw)
+    overlay = Image.new("RGBA", img_draw.size, (0, 0, 0, 0))
+    draw2 = ImageDraw.Draw(overlay)
+    font = ImageFont.load_default()
+    img_idx = 0
+
+    for i, ref in enumerate(refs):
+        result = extract_coordinates_and_label(ref, image_width, image_height)
+        if not result:
+            continue
+
+        label_type, points_list = result
+        color = (
+            np.random.randint(0, 200),
+            np.random.randint(0, 200),
+            np.random.randint(0, 255),
+        )
+        color_a = color + (20,)
+
+        for points in points_list:
+            x1, y1, x2, y2 = [
+                int(p / 999 * (image_width if i % 2 == 0 else image_height))
+                for i, p in enumerate(points)
+            ]
+
+            if label_type == "image":
+                try:
+                    cropped = image.crop((x1, y1, x2, y2))
+                    img_filename = (
+                        f"{jdx}_{img_idx}.jpg" if jdx is not None else f"{img_idx}.jpg"
+                    )
+                    cropped.save(
+                        os.path.join(config.OUTPUT_PATH, "images", img_filename)
+                    )
+                    img_idx += 1
+                except Exception as e:
+                    print(f"Error cropping image: {e}")
+
+            width = 4 if label_type == "title" else 2
+            draw.rectangle([x1, y1, x2, y2], outline=color, width=width)
+            draw2.rectangle(
+                [x1, y1, x2, y2], fill=color_a, outline=(0, 0, 0, 0), width=1
+            )
+
+            text_x, text_y = x1, max(0, y1 - 15)
+            text_bbox = draw.textbbox((0, 0), label_type, font=font)
+            text_width, text_height = (
+                text_bbox[2] - text_bbox[0],
+                text_bbox[3] - text_bbox[1],
+            )
+            draw.rectangle(
+                [text_x, text_y, text_x + text_width, text_y + text_height],
+                fill=(255, 255, 255, 30),
+            )
+            draw.text((text_x, text_y), label_type, font=font, fill=color)
+
+    img_draw.paste(overlay, (0, 0), overlay)
+    return img_draw
+
+
+def process_image_with_refs(image, ref_texts, jdx=None):
+    return draw_bounding_boxes(image, ref_texts, jdx)
+
+
+def load_image(image_path):
+    try:
+        image = Image.open(image_path).convert("RGB")
+        return ImageOps.exif_transpose(image)
+    except Exception as e:
+        print(f"Error loading image {image_path}: {e}")
+        return None
+
+
+# --- Main OCR Processing Logic ---
+
+
+def process_pdf(llm, sampling_params, pdf_path):
+    print(f"{Colors.GREEN}Processing PDF: {pdf_path}{Colors.RESET}")
+    base_name = os.path.basename(pdf_path)
+    file_name_without_ext = os.path.splitext(base_name)[0]
+
+    images = pdf_to_images_high_quality(pdf_path)
+    if not images:
+        print(
+            f"{Colors.YELLOW}Could not extract images from {pdf_path}. Skipping.{Colors.RESET}"
+        )
+        return
+
+    batch_inputs = []
+    processor = DeepseekOCRProcessor()
+    for image in tqdm(images, desc="Pre-processing PDF pages"):
+        batch_inputs.append(
+            {
+                "prompt": config.PROMPT,
+                "multi_modal_data": {
+                    "image": processor.tokenize_with_images(
+                        images=[image], bos=True, eos=True, cropping=config.CROP_MODE
+                    )
+                },
+            }
+        )
+
+    start_time = time.time()
+    outputs_list = llm.generate(batch_inputs, sampling_params=sampling_params)
+    end_time = time.time()
+
+    contents_det = ""
+    contents = ""
+    draw_images = []
+    for i, (output, img) in enumerate(zip(outputs_list, images)):
+        content = output.outputs[0].text
+        if "<｜end of sentence｜>" in content:
+            content = content.replace("<｜end of sentence｜>", "")
+        elif config.SKIP_REPEAT:
+            continue
+
+        page_num_separator = "\n<--- Page Split --->\n"
+        contents_det += content + page_num_separator
+
+        matches_ref, matches_images, mathes_other = re_match(content)
+        result_image = process_image_with_refs(img.copy(), matches_ref, jdx=i)
+        draw_images.append(result_image)
+
+        for idx, match in enumerate(matches_images):
+            content = content.replace(match, f"![](images/{i}_{idx}.jpg)\n")
+        for match in mathes_other:
+            content = (
+                content.replace(match, "")
+                .replace("\\coloneqq", ":=")
+                .replace("\\eqqcolon", "=:")
+                .replace("\n\n\n", "\n\n")
+            )
+
+        contents += content + page_num_separator
+
+    # Save results
+    json_path = os.path.join(
+        f"{config.OUTPUT_PATH}/result", f"{file_name_without_ext}.json"
+    )
+    pdf_out_path = os.path.join(
+        config.OUTPUT_PATH, f"{file_name_without_ext}_layouts.pdf"
+    )
+
+    duration = end_time - start_time
+    output_data = {
+        "filename": base_name,
+        "model": {"ocr_model": "deepseek-ocr"},
+        "time": {
+            "duration_sec": f"{duration:.2f}",
+            "started_at": start_time,
+            "ended_at": end_time,
+        },
+        "parsed": contents,
+    }
+
+    with open(json_path, "w", encoding="utf-8") as f:
+        json.dump(output_data, f, ensure_ascii=False, indent=4)
+
+    pil_to_pdf_img2pdf(draw_images, pdf_out_path)
+    print(
+        f"{Colors.GREEN}Finished processing {pdf_path}. Results saved in {config.OUTPUT_PATH}{Colors.RESET}"
+    )
+
+
+def process_image(llm, sampling_params, image_path):
+    print(f"{Colors.GREEN}Processing Image: {image_path}{Colors.RESET}")
+    base_name = os.path.basename(image_path)
+    file_name_without_ext = os.path.splitext(base_name)[0]
+
+    image = load_image(image_path)
+    if image is None:
+        return
+
+    processor = DeepseekOCRProcessor()
+    image_features = processor.tokenize_with_images(
+        images=[image], bos=True, eos=True, cropping=config.CROP_MODE
+    )
+
+    request = {
+        "prompt": config.PROMPT,
+        "multi_modal_data": {"image": image_features},
+    }
+
+    start_time = time.time()
+    outputs = llm.generate([request], sampling_params)
+    end_time = time.time()
+    result_out = outputs[0].outputs[0].text
+
+    print(result_out)
+
+    # Save results
+    output_json_path = os.path.join(
+        f"{config.OUTPUT_PATH}/result", f"{file_name_without_ext}.json"
+    )
+    result_image_path = os.path.join(
+        config.OUTPUT_PATH, f"{file_name_without_ext}_result_with_boxes.jpg"
+    )
+
+    matches_ref, matches_images, mathes_other = re_match(result_out)
+    result_image = process_image_with_refs(image.copy(), matches_ref)
+
+    processed_text = result_out
+    for idx, match in enumerate(matches_images):
+        processed_text = processed_text.replace(match, f"![](images/{idx}.jpg)\n")
+    for match in mathes_other:
+        processed_text = (
+            processed_text.replace(match, "")
+            .replace("\\coloneqq", ":=")
+            .replace("\\eqqcolon", "=:")
+            .replace("\n\n\n", "\n\n")
+        )
+
+    duration = end_time - start_time
+    output_data = {
+        "filename": base_name,
+        "model": {"ocr_model": "deepseek-ocr"},
+        "time": {
+            "duration_sec": f"{duration:.2f}",
+            "started_at": start_time,
+            "ended_at": end_time,
+        },
+        "parsed": processed_text,
+    }
+
+    with open(output_json_path, "w", encoding="utf-8") as f:
+        json.dump(output_data, f, ensure_ascii=False, indent=4)
+
+    result_image.save(result_image_path)
+    print(
+        f"{Colors.GREEN}Finished processing {image_path}. Results saved in {config.OUTPUT_PATH}{Colors.RESET}"
+    )
+
+
+def main():
+    # --- Model Initialization ---
+    print(f"{Colors.BLUE}Initializing model...{Colors.RESET}")
+    llm = LLM(
+        model=config.MODEL_PATH,
+        hf_overrides={"architectures": ["DeepseekOCRForCausalLM"]},
+        block_size=256,
+        enforce_eager=False,
+        trust_remote_code=True,
+        max_model_len=8192,
+        swap_space=0,
+        max_num_seqs=config.MAX_CONCURRENCY,
+        tensor_parallel_size=1,
+        gpu_memory_utilization=0.9,
+        disable_mm_preprocessor_cache=True,
+    )
+
+    logits_processors = [
+        NoRepeatNGramLogitsProcessor(
+            ngram_size=20, window_size=50, whitelist_token_ids={128821, 128822}
+        )
+    ]
+
+    sampling_params = SamplingParams(
+        temperature=0.0,
+        max_tokens=8192,
+        logits_processors=logits_processors,
+        skip_special_tokens=False,
+        include_stop_str_in_output=True,
+    )
+    print(f"{Colors.BLUE}Model initialized successfully.{Colors.RESET}")
+
+    # --- File Processing ---
+    input_dir = config.INPUT_PATH
+    output_dir = config.OUTPUT_PATH
+    os.makedirs(output_dir, exist_ok=True)
+    os.makedirs(os.path.join(output_dir, "images"), exist_ok=True)
+    os.makedirs(os.path.join(output_dir, "result"), exist_ok=True)
+
+    if not os.path.isdir(input_dir):
+        print(
+            f"{Colors.RED}Error: Input directory not found at '{input_dir}'{Colors.RESET}"
+        )
+        return
+
+    print(f"Scanning for files in '{input_dir}'...")
+    for filename in sorted(os.listdir(input_dir)):
+        input_path = os.path.join(input_dir, filename)
+        if not os.path.isfile(input_path):
+            continue
+
+        file_extension = os.path.splitext(filename)[1].lower()
+
+        try:
+            if file_extension == ".pdf":
+                process_pdf(llm, sampling_params, input_path)
+            elif file_extension in [".jpg", ".jpeg", ".png", ".bmp", ".gif", ".webp"]:
+                process_image(llm, sampling_params, input_path)
+            else:
+                print(
+                    f"{Colors.YELLOW}Skipping unsupported file type: {filename}{Colors.RESET}"
+                )
+        except Exception as e:
+            print(
+                f"{Colors.RED}An error occurred while processing {filename}: {e}{Colors.RESET}"
+            )
+
+
+if __name__ == "__main__":
+    main()