160 lines
5.1 KiB
Python
160 lines
5.1 KiB
Python
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from detecttracking.stream_pipeline import stream_pipeline
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from PIL import Image
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from io import BytesIO
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import torch
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import ast
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import requests
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# default: Load the model on the available device(s)
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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"Qwen/Qwen2-VL-7B-Instruct",
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torch_dtype="auto",
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device_map="auto"
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)
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# default processer
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", attn_implementation="flash_attention_2")
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def qwen_prompt(img_list, messages):
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# Preparation for inference
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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inputs = processor(
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text=[text],
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images=img_list,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to("cuda")
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# Inference: Generation of the output
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generated_ids = model.generate(**inputs, max_new_tokens=256)
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generated_ids_trimmed = [
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, add_special_tokens=False, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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return output_text[0]
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def get_best_image(track_imgs):
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img_frames = []
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for i in range(len(track_imgs)):
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content = {}
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content['type'] = 'image'
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content['min_pixels'] = 224 * 224
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content['max_pixels'] = 1280 * 28 * 28
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img_frames.append(content)
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messages = [
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{
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"role": "system",
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"content": "你是一个在超市工作的chatbot，你现在需要帮助顾客找到一张质量最好的商品图像。一个好的商品图像需要满足以下条件: \
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1. 文字清晰且连贯。\
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2. 商品图案清晰可识别。\
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3. 商品可提取的描述信息丰富。\
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基于以上条件，从多张图像中筛选出最好的图像，然后以dict输出该图像的索引信息，key为'index'。"
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},
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{
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"role": "system",
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"content": img_frames,
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},
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]
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output_text = qwen_prompt(track_imgs, messages)
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output_dict = ast.literal_eval(output_text.strip('```python\n'))
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best_img = track_imgs[output_dict['index'] - 1]
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return best_img
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def get_product_description(std_img, track_imgs):
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messages = [
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{
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"role": "system",
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"content": "你是一个在超市工作的chatbot，你现在需要提取商品的信息，信息需要按照以下python dict的格式输出: \
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{\
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'Text': 商品中提取出的文字信息, \
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'Color': 商品的颜色, \
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'Shape': 商品的形状, \
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'Material': 商品的材质, \
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'Category': 商品的类别, \
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'is_Same': 如果比对的两件商品的['Text', 'Color', 'Shape', 'Material', 'Category']属性中至少有3个相同则输出True，\
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否则输出False, \
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} \
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"
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},
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{
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"role": "system",
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"content": [
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{
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"type": "image",
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"min_pixels": 224 * 224,
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"max_pixels": 1280 * 28 * 28,
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},
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],
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},
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{
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"role": "system",
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"content": [
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{
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"type": "image",
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"min_pixels": 224 * 224,
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"max_pixels": 1280 * 28 * 28,
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},
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],
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},
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{
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"role": "user",
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"content": "以python dict的形式输出第二张图像的比对信息。"
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}
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]
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best_img = get_best_image(track_imgs)
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if std_img is not None:
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img_list = [std_img, best_img]
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else:
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img_list = [best_img, best_img]
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output_text = qwen_prompt(img_list, messages)
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contrast_pair = ast.literal_eval(output_text.strip('```python\n'))
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return contrast_pair
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def item_analysis(stream_dict):
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track_imgs = stream_pipeline(stream_dict)
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std_img = None
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if stream_dict['goodsPic'] is not None:
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response = requests.get(stream_dict['goodsPic'])
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std_img = Image.open(BytesIO(response.content))
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description_dict = get_product_description(std_img, track_imgs)
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return description_dict
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def main():
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# sample input dict
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stream_dict = {
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"goodsName": "优诺优丝黄桃果粒风味发酵乳",
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"measureProperty": 0,
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"qty": 1,
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"price": 25.9,
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"weight": 560, # 单位克
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"barcode": "6931806801024",
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"video": "https://ieemoo-ai.obs.cn-east-3.myhuaweicloud.com/videos/20231009/04/04_20231009-082149_21f2ca35-f2c2-4386-8497-3e7a3b407f03_4901872831197.mp4",
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"goodsPic": "https://ieemoo-storage.obs.cn-east-3.myhuaweicloud.com/lhpic/6931806801024.jpg",
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"measureUnit": "组",
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"goodsSpec": "405g"
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}
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result = item_analysis(stream_dict)
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print(result)
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if __name__ == "__main__":
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main() |