| import os |
| import torch |
| from tqdm import tqdm |
| from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
| from qwen_vl_utils import process_vision_info |
| from PIL import Image |
|
|
|
|
| model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
| "Qwen/Qwen2.5-VL-3B-Instruct", torch_dtype=torch.bfloat16, device_map="auto", cache_dir="." |
| ).eval() |
|
|
| processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct", cache_dir=".") |
|
|
|
|
| question = ''' |
| You are an agricultural expert. Analyze the image and answer the following questions: |
| 1. Identify the type of fruit or crop shown in the image. |
| 2. Determine its current growth stage. (Options: unripe, mature, pest-damaged, rotten) |
| 3. Recommend the farmer’s next action. (Options: keep for further growth, try to recover it, discard it) |
| 4. Evaluate the consumer’s willingness to consume this fruit, from 1 (very unlikely) to 100 (very likely). |
| |
| Please respond in the following format, and do not include explanations: |
| |
| - Type: [Fruit/Crop Name] |
| - Growth Stage: [unripe / mature / pest-damaged / rotten] |
| - Recommendation: [keep for further growth / try to recover it / discard it] |
| - Consumer Score: [1-100] |
| ''' |
|
|
|
|
| root_folder = "../data" |
| output_root = "result" |
| os.makedirs(output_root, exist_ok=True) |
|
|
|
|
| for fruit in os.listdir(root_folder): |
| fruit_path = os.path.join(root_folder, fruit) |
| if not os.path.isdir(fruit_path): continue |
|
|
| for subfolder in os.listdir(fruit_path): |
| subfolder_path = os.path.join(fruit_path, subfolder) |
| if not os.path.isdir(subfolder_path): continue |
|
|
| image_files = [f for f in os.listdir(subfolder_path) if f.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp'))] |
| if not image_files: continue |
|
|
| output_file = os.path.join(output_root, f"{fruit}_{subfolder}.txt") |
| |
| with open(output_file, "w", encoding="utf-8") as fout: |
| for img_file in tqdm(image_files, desc=f"{fruit}/{subfolder}"): |
| try: |
| image_path = os.path.join(subfolder_path, img_file) |
| messages = [ |
| { |
| "role": "user", |
| "content": [ |
| {"type": "image", "image": image_path}, |
| {"type": "text", "text": question}, |
| ] |
| } |
| ] |
|
|
| |
| text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
| image_inputs, video_inputs = process_vision_info(messages) |
| inputs = processor( |
| text=[text], |
| images=image_inputs, |
| videos=video_inputs, |
| padding=True, |
| return_tensors="pt", |
| ).to("cuda") |
|
|
| |
| with torch.no_grad(): |
| generated_ids = model.generate(**inputs, max_new_tokens=128) |
| generated_ids_trimmed = [ |
| out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
| ] |
| output_text = processor.batch_decode( |
| generated_ids_trimmed, |
| skip_special_tokens=True, |
| clean_up_tokenization_spaces=False |
| )[0].strip() |
|
|
| |
| fout.write(f"{'=' * 25} IMAGE START {'=' * 25}\n") |
| fout.write(f"🖼️ Image Name: {img_file}\n") |
| fout.write(f"📝 Answer:\n{output_text}\n") |
| fout.write(f"{'=' * 25} IMAGE END {'=' * 25}\n\n") |
| except Exception as e: |
| print(f"[ERROR] {fruit}/{subfolder}/{img_file}: {e}") |
| fout.write(f"{'=' * 25} IMAGE START {'=' * 25}\n") |
| fout.write(f"🖼️ Image Name: {img_file}\n") |
| fout.write(f"❌ ERROR: {e}\n") |
| fout.write(f"{'=' * 25} IMAGE END {'=' * 25}\n\n") |
|
|
