| import gradio as gr | |
| import mediapipe as mp | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| from transformers import AutoTokenizer, AutoProcessor, AutoModel | |
| model_id = "Qwen/Qwen2-VL-7B" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) | |
| processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) | |
| model = AutoModel.from_pretrained(model_id, trust_remote_code=True, device_map="auto").eval() | |
| mp_pose = mp.solutions.pose | |
| def analyze_posture_by_keypoints(landmarks): | |
| left_shoulder = landmarks.landmark[11] | |
| right_shoulder = landmarks.landmark[12] | |
| left_ear = landmarks.landmark[7] | |
| right_ear = landmarks.landmark[8] | |
| shoulder_x = (left_shoulder.x + right_shoulder.x) / 2 | |
| ear_x = (left_ear.x + right_ear.x) / 2 | |
| delta = ear_x - shoulder_x | |
| if abs(delta) > 0.06: | |
| return "该用户存在驼背或低头倾向，头部明显前倾。" | |
| else: | |
| return "该用户坐姿较为端正，头部与肩部对齐。" | |
| def process(image: Image): | |
| np_image = np.array(image) | |
| with mp_pose.Pose(static_image_mode=True) as pose: | |
| results = pose.process(cv2.cvtColor(np_image, cv2.COLOR_RGB2BGR)) | |
| if not results.pose_landmarks: | |
| return "❗ 无法检测到人体，请上传包含上半身的清晰坐姿照片。" | |
| posture_analysis = analyze_posture_by_keypoints(results.pose_landmarks) | |
| prompt = f"请根据以下坐姿描述生成中英文提醒：\n{posture_analysis}" | |
| inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device) | |
| outputs = model.generate(**inputs, max_new_tokens=512) | |
| result = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return result | |
| demo = gr.Interface( | |
| fn=process, | |
| inputs=gr.Image(type="pil", label="上传你的坐姿照片"), | |
| outputs=gr.Textbox(label="中英文坐姿分析结果"), | |
| title="🪑 Qwen2-VL 坐姿识别助手（修复版）", | |
| description="融合 Mediapipe 与 Qwen2-VL 模型，判断是否驼背并生成中英文提醒。", | |
| theme="soft", | |
| allow_flagging="never" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |