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# --------------------------------------------------------
# Based on yolov10
# https://github.com/THU-MIG/yolov10/app.py
# --------------------------------------------------------'# flash_attn-2.7.3+cu11torch2.2cxx11abiFALSE-cp311-cp311-linux_x86_64.whlimport gradio as gr
import cv2
import tempfile
from ultralytics import YOLOdef yolov12_inference(image, video, model_id, image_size, conf_threshold):model = YOLO(model_id)if image:results = model.predict(source=image, imgsz=image_size, conf=conf_threshold)annotated_image = results[0].plot()return annotated_image[:, :, ::-1], Noneelse:video_path = tempfile.mktemp(suffix=".webm")with open(video_path, "wb") as f:with open(video, "rb") as g:f.write(g.read())cap = cv2.VideoCapture(video_path)fps = cap.get(cv2.CAP_PROP_FPS)frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))output_video_path = tempfile.mktemp(suffix=".webm")out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'vp80'), fps, (frame_width, frame_height))while cap.isOpened():ret, frame = cap.read()if not ret:breakresults = model.predict(source=frame, imgsz=image_size, conf=conf_threshold)annotated_frame = results[0].plot()out.write(annotated_frame)cap.release()out.release()return None, output_video_pathdef yolov12_inference_for_examples(image, model_path, image_size, conf_threshold):annotated_image, _ = yolov12_inference(image, None, model_path, image_size, conf_threshold)return annotated_imagedef app():with gr.Blocks():with gr.Row():with gr.Column():image = gr.Image(type="pil", label="Image", visible=True)video = gr.Video(label="Video", visible=False)input_type = gr.Radio(choices=["Image", "Video"],value="Image",label="Input Type",)model_id = gr.Dropdown(label="Model",choices=["yolov12n.pt","yolov12s.pt","yolov12m.pt","yolov12l.pt","yolov12x.pt",],value="yolov12m.pt",)image_size = gr.Slider(label="Image Size",minimum=320,maximum=1280,step=32,value=640,)conf_threshold = gr.Slider(label="Confidence Threshold",minimum=0.0,maximum=1.0,step=0.05,value=0.25,)yolov12_infer = gr.Button(value="Detect Objects")with gr.Column():output_image = gr.Image(type="numpy", label="Annotated Image", visible=True)output_video = gr.Video(label="Annotated Video", visible=False)def update_visibility(input_type):image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)output_image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)output_video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)return image, video, output_image, output_videoinput_type.change(fn=update_visibility,inputs=[input_type],outputs=[image, video, output_image, output_video],)def run_inference(image, video, model_id, image_size, conf_threshold, input_type):if input_type == "Image":return yolov12_inference(image, None, model_id, image_size, conf_threshold)else:return yolov12_inference(None, video, model_id, image_size, conf_threshold)yolov12_infer.click(fn=run_inference,inputs=[image, video, model_id, image_size, conf_threshold, input_type],outputs=[output_image, output_video],)gr.Examples(examples=[["ultralytics/assets/bus.jpg","yolov12s.pt",640,0.25,],["ultralytics/assets/zidane.jpg","yolov12x.pt",640,0.25,],],fn=yolov12_inference_for_examples,inputs=[image,model_id,image_size,conf_threshold,],outputs=[output_image],cache_examples='lazy',)gradio_app = gr.Blocks()
with gradio_app:gr.HTML("""<h1 style='text-align: center'>YOLOv12: Attention-Centric Real-Time Object Detectors</h1>""")gr.HTML("""<h3 style='text-align: center'><a href='https://arxiv.org/abs/2502.12524' target='_blank'>arXiv</a> | <a href='https://github.com/sunsmarterjie/yolov12' target='_blank'>github</a></h3>""")with gr.Row():with gr.Column():app()
if __name__ == '__main__':gradio_app.launch()
import gradio as gr
import cv2
import numpy as np
from ultralytics import YOLO# 全局变量
model = None
current_config = {}
running = False
target_classes = None
class_names = {} # 存储类别名称映射# 中文到英文类别映射
chinese_class_mapping = {"人": "person","人类": "person","电脑": "laptop","笔记本": "laptop","汽车": "car","车辆": "car","狗": "dog","猫": "cat","手机": "cell phone","杯子": "cup","瓶子": "bottle","椅子": "chair","桌子": "dining table","电视": "tv","飞机": "airplane","自行车": "bicycle","船": "boat","公交车": "bus","火车": "train","摩托车": "motorcycle","鸟": "bird","羊": "sheep","牛": "cow","大象": "elephant","熊": "bear","斑马": "zebra","长颈鹿": "giraffe","背包": "backpack","雨伞": "umbrella","手提包": "handbag","领带": "tie","行李箱": "suitcase","飞盘": "frisbee","滑雪板": "skis","雪板": "snowboard","运动球": "sports ball","风筝": "kite","棒球棒": "baseball bat","棒球手套": "baseball glove","滑板": "skateboard","冲浪板": "surfboard","网球拍": "tennis racket","酒瓶": "wine glass","杯子": "cup","叉子": "fork","刀": "knife","勺子": "spoon","碗": "bowl","香蕉": "banana","苹果": "apple","三明治": "sandwich","橙子": "orange","西兰花": "broccoli","胡萝卜": "carrot","热狗": "hot dog","披萨": "pizza","甜甜圈": "donut","蛋糕": "cake","沙发": "couch","盆栽": "potted plant","床": "bed","餐桌": "dining table","马桶": "toilet","显示器": "monitor","鼠标": "mouse","键盘": "keyboard","遥控器": "remote","微波炉": "microwave","烤箱": "oven","烤面包机": "toaster","水槽": "sink","冰箱": "refrigerator","书": "book","时钟": "clock","花瓶": "vase","剪刀": "scissors","泰迪熊": "teddy bear","吹风机": "hair drier","牙刷": "toothbrush"
}def initialize_model(model_id, image_size, conf_threshold):global model, current_config, class_namesif model is None or current_config != (model_id, image_size, conf_threshold):model = YOLO(model_id)current_config = (model_id, image_size, conf_threshold)# 获取模型的类别名称映射class_names = model.namesdef process_frame(frame):global model, target_classesif model is None:return frame# 如果有指定类别，则只检测这些类别classes_to_detect = Noneif target_classes:classes_to_detect = [class_id for class_id, name in class_names.items()if name.lower() in [t.lower().strip() for t in target_classes]]results = model.predict(source=frame,imgsz=current_config[1],conf=current_config[2],classes=classes_to_detect)annotated_frame = results[0].plot()return annotated_frame[:, :, ::-1]def camera_stream():cap = cv2.VideoCapture(0)while running:ret, frame = cap.read()if not ret:breakframe = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)yield framecap.release()def detect_frames():for frame in camera_stream():processed = process_frame(frame)yield cv2.cvtColor(processed, cv2.COLOR_RGB2BGR)def toggle_detection(start_btn):global runningrunning = not runningreturn "停止检测" if running else "开始检测"def update_target_classes(class_input):global target_classes, chinese_class_mappingif class_input:# 处理中文输入input_classes = [c.strip() for c in class_input.split(",")]target_classes = []for cls in input_classes:if cls in chinese_class_mapping:target_classes.append(chinese_class_mapping[cls])else:# 如果不是中文，直接使用输入target_classes.append(cls)return f"检测目标已设置为: {', '.join(input_classes)} ({', '.join(target_classes)})"else:target_classes = Nonereturn "将检测所有类别"def app():# 使用Soft主题并设置最大宽度with gr.Blocks(theme=gr.themes.Soft(), css=".gradio-container {max-width: 1200px !important}") as demo:# 自定义CSS样式css = """.title {text-align: center;font-size: 2.5em;font-weight: bold;color: #2b5876;margin-bottom: 20px;background: linear-gradient(135deg, #2b5876 0%, #4e4376 100%);-webkit-background-clip: text;-webkit-text-fill-color: transparent;}.panel {background: white;border-radius: 10px;padding: 20px;box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);margin-bottom: 20px;}.button-primary {background: linear-gradient(135deg, #2b5876 0%, #4e4376 100%) !important;color: white !important;border: none !important;font-weight: bold !important;}.button-primary:hover {box-shadow: 0 4px 8px rgba(43, 88, 118, 0.3) !important;}.output-container {border-radius: 10px;overflow: hidden;box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15);margin-top: -50px; /* 将检测框上移 */}.status-box {background: #f8f9fa;border-radius: 8px;padding: 15px;margin-top: 10px;}.class-input {margin-bottom: 15px;}.example-list {font-size: 0.9em;color: #666;margin-top: 10px;}"""# 应用CSSgr.HTML(f"<style>{css}</style>")# 标题区域with gr.Column(elem_classes=["panel"]):gr.HTML('<div class="title">YOLOv12 智能目标检测系统</div>')gr.Markdown("""**使用说明**: 1. 选择模型和设置参数 2. 输入要检测的目标类别(中英文均可，如: 人, car, 电脑) 3. 点击"开始检测"按钮 4. 多个类别用逗号分隔(如: 人, 汽车, dog) """)with gr.Row():# 控制面板with gr.Column(scale=1, elem_classes=["panel"]):model_selector = gr.Dropdown(label="🛠️ 选择检测模型",choices=["yolov12n.pt","yolov12s.pt","yolov12m.pt","yolov12l.pt","yolov12x.pt",],value="yolov12m.pt")with gr.Accordion("⚙️ 高级设置", open=False):image_size = gr.Slider(label="📏 推理尺寸",minimum=320,maximum=1280,step=32,value=640,info="较大的尺寸提高精度但降低速度")conf_threshold = gr.Slider(label="🎯 置信度阈值",minimum=0.0,maximum=1.0,step=0.05,value=0.4,info="值越高检测结果越可靠但可能漏检")with gr.Column(elem_classes=["class-input"]):class_input = gr.Textbox(label="🎯 检测目标 (中英文)",placeholder="输入要检测的类别，如: 人, car, 电脑",info="多个类别用逗号分隔，留空则检测所有类别")gr.Markdown("""<div class="example-list"><b>常用类别示例</b>:<br>人, 汽车, 狗, 猫, 手机, 电脑, 椅子, 桌子, 电视, 飞机, 自行车</div>""")class_status = gr.Textbox(label="🔄 状态",interactive=False,elem_classes=["status-box"])start_btn = gr.Button("▶️ 开始检测",variant="primary",elem_classes=["button-primary"])# 输出区域with gr.Column(scale=2, elem_classes=["output-container"]):output_video = gr.Image(label="🔍 实时检测结果",height=500)gr.Markdown("""**检测统计**: - 支持80种常见目标检测 - 实时处理速度: 30-60 FPS (取决于模型和硬件) - 最佳实践: 使用yolov12s平衡速度和精度 """)# 事件处理class_input.change(fn=update_target_classes,inputs=class_input,outputs=class_status)start_btn.click(fn=toggle_detection,inputs=[start_btn],outputs=[start_btn],queue=False).then(fn=lambda: gr.Image(interactive=True),outputs=output_video).then(fn=initialize_model,inputs=[model_selector, image_size, conf_threshold],queue=False).then(fn=detect_frames,outputs=output_video)return demoif __name__ == "__main__":# 忽略FlashAttention警告import warningswarnings.filterwarnings("ignore", message="FlashAttention is not available")gradio_app = app()gradio_app.queue().launch(server_name="0.0.0.0",server_port=7861,share=True,inbrowser=True,)# "172.17.173.38"
import gradio as gr
import cv2
import numpy as np
from ultralytics import YOLO
import speech_recognition as sr# 全局变量
model = None
current_config = {}
running = False # 控制检测状态的标志
target_class = None # 控制目标类别，初始为空def initialize_model(model_id, image_size, conf_threshold):global model, current_configif model is None or current_config != (model_id, image_size, conf_threshold):model = YOLO(model_id)current_config = (model_id, image_size, conf_threshold)def process_frame(frame):global model, target_classif model is None:return frame# 如果target_class已设置，使用该类别过滤目标results = model.predict(source=frame, imgsz=current_config[1], conf=current_config[2])# 根据目标类别进行筛选if target_class is not None:results = [result[result.names.index(target_class)] for result in results if target_class in result.names]annotated_frame = results[0].plot()return annotated_frame[:, :, ::-1]def camera_stream():cap = cv2.VideoCapture(0)while running:ret, frame = cap.read()if not ret:breakframe = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)yield framecap.release()def detect_frames():for frame in camera_stream():processed = process_frame(frame)yield cv2.cvtColor(processed, cv2.COLOR_RGB2BGR)def toggle_detection(start_btn):global runningrunning = not runningreturn "停止检测" if running else "开始检测"def recognize_speech():# 使用语音识别库来获取用户的指令recognizer = sr.Recognizer()with sr.Microphone() as source:print("请说话...")audio = recognizer.listen(source)try:command = recognizer.recognize_google(audio, language="zh-CN")print("你说的是: " + command)return commandexcept sr.UnknownValueError:return "无法识别语音"except sr.RequestError:return "语音识别服务不可用"def set_target_class(command):global target_classif "人" in command: # 例如，用户说“只识别人”target_class = "person"else:target_class = None # 默认识别所有目标def app():with gr.Blocks() as demo:gr.HTML("""<h1 style='text-align: center'>YOLOv12 实时摄像头检测</h1>""")with gr.Row():with gr.Column():model_selector = gr.Dropdown(label="选择模型",choices=["yolov12n.pt","yolov12s.pt","yolov12m.pt","yolov12l.pt","yolov12x.pt",],value="yolov12m.pt")with gr.Accordion("高级设置", open=False):image_size = gr.Slider(label="推理尺寸",minimum=320,maximum=1280,step=32,value=640)conf_threshold = gr.Slider(label="置信度阈值",minimum=0.0,maximum=1.0,step=0.05,value=0.25)start_btn = gr.Button("开始检测", variant="primary")with gr.Column():output_video = gr.Image(label="检测结果")# 事件处理start_btn.click(fn=toggle_detection,inputs=[start_btn],outputs=[start_btn],queue=False).then(fn=lambda: gr.Image(interactive=True),outputs=output_video).then(fn=initialize_model,inputs=[model_selector, image_size, conf_threshold],queue=False).then(fn=detect_frames,outputs=output_video)# 语音识别处理gr.Button("说话控制", variant="secondary").click(fn=recognize_speech,outputs=None).then(fn=set_target_class,inputs=[recognize_speech],queue=False)return demoif __name__ == "__main__":gradio_app = app()gradio_app.queue().launch()
