如何打印输出def MEAN_Recog_TL(model_spot, opt, emotion_class): for layer in model_spot.layers: layer.trainable = False # Until last convolutional later merged = model_spot.layers[-6].output merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(merged) merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv) flat = layers.Flatten()(merged_pool) flat_do = layers.Dropout(0.2)(flat) outputs = layers.Dense(emotion_class, activation = "softmax", name='recog')(flat_do) model = keras.models.Model(inputs = model_spot.input, outputs = outputs) model.compile( loss={'recog':'categorical_crossentropy'}, optimizer=opt, metrics={'recog':tf.keras.metrics.CategoricalAccuracy()} ) return model

recognize.zip.rar_recog_不变矩_不变矩 matlab_中心位置

读取 "辨识文章" 影像文件，寻找出影像中之对象，储存对象影像以及其位置数据 (供辨识程序输出文字时使用) 并且计算不变矩，依位置数据由左至右，由上而下，将影像对象排序，并标定出影像对象之中心位置。...

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本资源——"12456Classification_toolbox.rar_dslvq.m_matlab模式识别_toolbox recog"，正是一个专为模式识别设计的MATLAB工具箱，旨在帮助用户更高效地进行模式分类工作。该工具箱中的核心文件"dslvq.m"是基于...

def MEAN_Spot_Recog_TL(model_spot, model_recog, opt): outputs1 = model_spot.layers[-1].output outputs2 = model_recog.layers[-1].output model = keras.models.Model(inputs = [model_spot.input], outputs = [outputs1, outputs2]) model.compile( loss={'spot':'mse', 'recog':'categorical_crossentropy'}, optimizer=opt, metrics={'spot':tf.keras.metrics.MeanAbsoluteError(), 'recog':tf.keras.metrics.CategoricalAccuracy()} ) return model

这段代码看起来是一个函数，函数名是 MEAN_Spot_Recog_TL。它的作用是将两个模型（model_spot 和 model_recog）拼接在一起，形成一个新的模型，并对新模型进行编译。新模型的输入是 model_spot 的输入，输出是 model...

为什么model = MEAN_Recog_TL(model_spot, opt, emotion_class) plot_model(model, to_file='/home/xin/LR/ymodel/my_model.png', show_shapes=True) 没有输出

如果您调用了 plot_model() 函数但没有看到任何输出，通常有以下几个可能的原因： 1. 您没有正确安装 pydot 和 graphviz 库。这些库是 plot_model() 函数需要的依赖项，用于生成模型结构图。请确保您已经...

def train_test(X, y, X1, y1, X2, y2, dataset_name, emotion_class, groupsLabel, groupsLabel1, spot_multiple, final_subjects, final_emotions, final_samples, final_dataset_spotting, k, k_p, expression_type, epochs_spot=10, epochs_recog=100, spot_lr=0.0005, recog_lr=0.0005, batch_size=32, ratio=5, p=0.55, spot_attempt=1, recog_attempt=1, train=False): start = time.time() loso = LeaveOneGroupOut() subject_count = 0 total_gt_spot = 0 metric_final = MeanAveragePrecision2d(num_classes=1) adam_spot = keras.optimizers.Adam(lr=spot_lr) adam_recog = keras.optimizers.Adam(lr=recog_lr) model_spot = MEAN_Spot(adam_spot) weight_reset_spot = model_spot.get_weights() #Initial weights

这段代码是一个用 Keras 训练模型的函数。其中，它的参数包括输入数据 X 和标签 y，测试数据 X1 和标签 y1...最后，函数返回了模型的训练时间、总共正确识别的样本数以及 MEAN_Spot 模型的初始权重 weight_reset_spot。

if train: model_spot_recog.save_weights(path) # Save Weights else: model_spot_recog.load_weights(path) # Load Pretrained Weights results = model_spot_recog.predict( X_test, verbose=1 报错AttributeError: 'NoneType' object has no attribute 'save_weights'

请确保在调用 model_spot_recog.save_weights() 之前，你已经正确地创建了一个模型对象，并将其赋值给了 model_spot_recog。 2. 在之前的代码中，可能存在错误导致 model_spot_recog 被重新赋值为 None。请...

我需要理解下面代码，请详细的分析逻辑： class FaceProcessor: def init(self): self.face_model = YOLO(CONFIG[“yolo_model”]) self.recog_model = cv2.dnn.readNetFromONNX(CONFIG[“recog_model”]) self.frame_queue = Queue(maxsize=9) self.detect_queue = Queue(maxsize=6) self.feature_mgr = FeatureManager() self.attendance_log = [] threading.Thread(target=self._capture_thread, daemon=True).start() threading.Thread(target=self._detect_thread, daemon=True).start() def _capture_thread(self): cap = cv2.VideoCapture(CONFIG["camera_id"]) cap.set(3, CONFIG["frame_size"][0]) cap.set(4, CONFIG["frame_size"][1]) while True: ret, frame = cap.read() if not ret: continue if self.frame_queue.qsize() < 3: self.frame_queue.put(frame) def _detect_thread(self): while True: if self.frame_queue.empty(): time.sleep(1) continue frame = self.frame_queue.get() results = self.face_model(frame, imgsz=640, conf=CONFIG["detect_thresh"]) boxes = results[0].boxes.xyxy.cpu().numpy() self.detect_queue.put((frame, boxes)) def process_frame(self): if self.detect_queue.empty(): return None frame, boxes = self.detect_queue.get() for box in boxes: x1, y1, x2, y2 = map(int, box) face_img = frame[y1:y2, x1:x2] aligned_face = cv2.resize(face_img, (112, 112)) blob = cv2.dnn.blobFromImage(aligned_face, 1 / 128.0, (112, 112), (127.5, 127.5, 127.5), swapRB=True) self.recog_model.setInput(blob) feature = self.recog_model.forward().flatten() max_sim = 0 matched_id = -1 matched_name = "" for emp_id, name, db_feat in self.feature_mgr.get_all_features(): similarity = np.dot(db_feat, feature) if similarity > max_sim and similarity > CONFIG["match_thresh"]: max_sim = similarity matched_id = emp_id matched_name = name if matched_id != -1: self._record_attendance(matched_id) frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) pil_img = Image.fromarray(frame_rgb) draw = ImageDraw.Draw(pil_img) try: font = ImageFont.truetype(FONT_PATH, 20) # Windows系统字体 except: font = ImageFont.load_default() # 备选方案（不支持中文） draw.rectangle([x1, y1, x2, y2], outline=(0, 255, 0), width=2) draw.text((x1, max(y1 - 30, 10)), # 防止顶部越界 f"姓名:{matched_name}", font=font, fill=(0, 255, 0)) frame = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR) return frame def _record_attendance(self, user_id): timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") new_record = {"user_id": user_id, "timestamp": timestamp} with threading.Lock(): try: with open(CONFIG["attendance_file"], "r+") as f: records = json.load(f) records.append(new_record) f.seek(0) json.dump(records, f, indent=2) except FileNotFoundError: with open(CONFIG["attendance_file"], "w") as f: json.dump([new_record], f, indent=2)

self.recog_model = cv2.dnn.readNetFromONNX(CONFIG["recog_model"]) # ONNX格式识别模型 # 缓冲队列 self.frame_queue = Queue(maxsize=9) # 原始帧缓冲区 self.detect_queue = Queue(maxsize=6) # 检测结果...

下面人脸识别的代码中，我需要进行修改，显示name而不是显示id，并且这个name是中文需要正确显示。 class FaceProcessor: def init(self): # 初始化模型 self.face_model = YOLO(CONFIG["yolo_model"]) self.recog_model = cv2.dnn.readNetFromONNX(CONFIG["recog_model"]) # 初始化队列 self.frame_queue = Queue(maxsize=3) self.detect_queue = Queue(maxsize=2) # 功能模块 self.feature_mgr = FeatureManager() self.attendance_log = [] # 启动线程 threading.Thread(target=self._capture_thread, daemon=True).start() threading.Thread(target=self._detect_thread, daemon=True).start() def _capture_thread(self): """摄像头采集线程""" cap = cv2.VideoCapture(CONFIG["camera_id"]) cap.set(3, CONFIG["frame_size"][0]) cap.set(4, CONFIG["frame_size"][1]) while True: ret, frame = cap.read() if not ret: continue if self.frame_queue.qsize() < 3: self.frame_queue.put(frame) def _detect_thread(self): """人脸检测线程""" while True: if self.frame_queue.empty(): time.sleep(0.01) continue frame = self.frame_queue.get() results = self.face_model(frame, imgsz=640, conf=CONFIG["detect_thresh"]) boxes = results[0].boxes.xyxy.cpu().numpy() self.detect_queue.put((frame, boxes)) def process_frame(self): """主处理循环（在UI线程执行）""" if self.detect_queue.empty(): return None frame, boxes = self.detect_queue.get() for box in boxes: # 人脸对齐 x1, y1, x2, y2 = map(int, box) face_img = frame[y1:y2, x1:x2] aligned_face = cv2.resize(face_img, (112, 112)) # 特征提取 blob = cv2.dnn.blobFromImage(aligned_face, 1 / 128.0, (112, 112), (127.5, 127.5, 127.5), swapRB=True) self.recog_model.setInput(blob) feature = self.recog_model.forward().flatten() # 特征比对 max_sim = 0 matched_id = -1 for emp_id, name, db_feat in self.feature_mgr.get_all_features(): similarity = np.dot(db_feat, feature) if similarity > max_sim and similarity > CONFIG["match_thresh"]: max_sim = similarity matched_id = emp_id # 考勤记录 if matched_id != -1: self._record_attendance(matched_id) cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.putText(frame, f"ID:{matched_id}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2) return frame def _record_attendance(self, user_id): """记录考勤到JSON""" timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") new_record = {"user_id": user_id, "timestamp": timestamp} with threading.Lock(): # 保证线程安全 try: with open(CONFIG["attendance_file"], "r+") as f: records = json.load(f) records.append(new_record) f.seek(0) json.dump(records, f, indent=2) except FileNotFoundError: with open(CONFIG["attendance_file"], "w") as f: json.dump([new_record], f, indent=2)

for emp_id, name, db_feat in self.feature_mgr.get_all_features(): similarity = np.dot(db_feat, feature) if similarity > max_sim and similarity > CONFIG["match_thresh"]: max_sim = similarity ...

可以仔细讲讲人脸检测线程吗，我这边结果发现检测结果有问题，不同人识别出来都是同一个人，希望找到解决方案： def process_frame(self): if self.detect_queue.empty(): return None # 无数据快速返回 frame, boxes = self.detect_queue.get() for box in boxes: # 人脸区域提取 x1, y1, x2, y2 = map(int, box) face_img = frame[y1:y2, x1:x2] # 预处理 aligned_face = cv2.resize(face_img, (112, 112)) # 标准化尺寸 blob = cv2.dnn.blobFromImage(...) # 归一化+通道转换 # 特征提取 self.recog_model.setInput(blob) feature = self.recog_model.forward().flatten() # 特征比对 max_sim = 0 for emp_id, name, db_feat in self.feature_mgr.get_all_features(): similarity = np.dot(db_feat, feature) # 余弦相似度简化计算 if similarity > max_sim and similarity > CONFIG["match_thresh"]: max_sim = similarity matched_id = emp_id matched_name = name # 考勤记录 if matched_id != -1: self._record_attendance(matched_id) # 绘制界面 draw.rectangle(...) # 绘制边界框 draw.text(...) # 显示识别结果

for case in test_cases: compute_cross_similarity(case) --- ### 五、推荐改进路线 1. **立即实施**： - 添加L2归一化（代码改动最小，效果最直接） - 验证预处理参数与模型匹配 2. **中期优化**： - ...

"xstream_workflow_file": "./video_box/configs/body_detection.json", "xstream_workflow_file_pic": "./video_box/configs/recog_flow.json", "xstream_workflow_file_feature": "./video_box/configs/feature_flow.json", "enable_recog": false, "enable_profile": 0, "profile_log_path": "", "enable_result_to_json": false, "box_face_thr": 0.95, "box_head_thr": 0.95, "box_body_thr": 0.95, "lmk_thr": 0.0, "kps_thr": 0.50, "box_veh_thr": 0.995, "plot_fps": false, "rtsp_config_file": "./video_box/configs/rtsp.json", "display_config_file": "./video_box/configs/display.json", "drop_frame_config_file": "./video_box/configs/drop_frames.json", "run_smart": true, "save_dec_video": false解释下

这段代码看起来像是一个配置文件，其中包含了一些参数的设置。...此外，还有一些功能的开启（enable_recog, enable_profile, enable_result_to_json, run_smart）和一些保存视频的设置（save_dec_video）等。

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