HuangMason320
ccd7cdd6b9
- Move test scripts to tests/ directory for better organization - Add improved YOLOv5 postprocessing with reference implementation - Update gitignore to exclude *.mflow files and include main.spec - Add debug capabilities and coordinate scaling improvements - Enhance multi-series support with proper validation - Add AGENTS.md documentation and example utilities 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
202 lines
6.9 KiB
Python
202 lines
6.9 KiB
Python
"""
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fire_detection_inference.py
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此模組提供火災檢測推論介面函式：
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inference(frame, params={})
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當作為主程式執行時，也可以使用命令列參數測試推論。
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"""
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import os
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import sys
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import time
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import argparse
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import cv2
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import numpy as np
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import kp
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# 固定路徑設定
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# SCPU_FW_PATH = r'external\res\firmware\KL520\fw_scpu.bin'
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# NCPU_FW_PATH = r'external\res\firmware\KL520\fw_ncpu.bin'
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# MODEL_FILE_PATH = r'src\utils\models\fire_detection_520.nef'
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# 若作為測試使用，預設的圖片檔案路徑（請根據實際環境調整）
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# IMAGE_FILE_PATH = r'test_images\fire4.jpeg'
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def preprocess_frame(frame):
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"""
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將輸入的 numpy 陣列進行預處理：
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1. 調整大小至 (128, 128)
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2. 轉換為 BGR565 格式（KL520 常用格式）
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"""
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if frame is None:
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raise Exception("輸入的 frame 為 None")
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print("預處理步驟：")
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print(f" - 原始 frame 大小: {frame.shape}")
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# 調整大小
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frame_resized = cv2.resize(frame, (128, 128))
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print(f" - 調整後大小: {frame_resized.shape}")
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# 轉換為 BGR565 格式
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# 注意：cv2.cvtColor 直接轉換到 BGR565 並非 OpenCV 標準用法，但假設此方法在 kneron SDK 下有效
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frame_bgr565 = cv2.cvtColor(frame_resized, cv2.COLOR_BGR2BGR565)
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print(" - 轉換為 BGR565 格式")
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return frame_bgr565
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def postprocess(pre_output):
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"""
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後處理函式：將模型輸出轉換為二元分類結果（這裡假設輸出為單一數值）
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"""
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probability = pre_output[0] # 假設模型輸出僅一個數值
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return probability
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def inference(frame, params={}):
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"""
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推論介面函式
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- frame: numpy 陣列（BGR 格式），輸入的原始影像
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- params: dict，包含額外參數，例如:
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'port_id': (int) 預設 0
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'model': (str) 模型檔案路徑，預設 MODEL_FILE_PATH
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回傳一個 dict，內容包含：
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- result: "Fire" 或 "No Fire"
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- probability: 推論信心分數
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- inference_time_ms: 推論耗時 (毫秒)
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"""
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# 取得參數（若未提供則使用預設值）
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port_id = params.get('usb_port_id', 0)
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model_path = params.get('model')
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IMAGE_FILE_PATH = params.get('file_path')
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SCPU_FW_PATH = params.get('scpu_path')
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NCPU_FW_PATH = params.get('ncpu_path')
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print("Parameters received from main app:", params)
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try:
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# 1. 設備連接與初始化
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print('[連接設備]')
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device_group = kp.core.connect_devices(usb_port_ids=[port_id])
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print(' - 成功')
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print('[設置超時]')
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kp.core.set_timeout(device_group=device_group, milliseconds=5000)
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print(' - 成功')
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print('[上傳韌體]')
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kp.core.load_firmware_from_file(device_group=device_group,
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scpu_fw_path=SCPU_FW_PATH,
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ncpu_fw_path=NCPU_FW_PATH)
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print(' - 成功')
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print('[上傳模型]')
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model_descriptor = kp.core.load_model_from_file(device_group=device_group,
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file_path=model_path)
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print(' - 成功')
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# 2. 圖像預處理：從 frame 轉換到符合 KL520 格式的輸入
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print('[預處理影像]')
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img_processed = preprocess_frame(frame)
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# 3. 建立推論描述物件
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inference_input_descriptor = kp.GenericImageInferenceDescriptor(
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model_id=model_descriptor.models[0].id,
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inference_number=0,
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input_node_image_list=[
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kp.GenericInputNodeImage(
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image=img_processed,
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image_format=kp.ImageFormat.KP_IMAGE_FORMAT_RGB565,
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resize_mode=kp.ResizeMode.KP_RESIZE_ENABLE,
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padding_mode=kp.PaddingMode.KP_PADDING_CORNER,
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normalize_mode=kp.NormalizeMode.KP_NORMALIZE_KNERON
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)
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]
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)
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# 4. 執行推論
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print('[執行推論]')
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start_time = time.time()
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kp.inference.generic_image_inference_send(
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device_group=device_group,
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generic_inference_input_descriptor=inference_input_descriptor
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)
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generic_raw_result = kp.inference.generic_image_inference_receive(
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device_group=device_group
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)
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inference_time = (time.time() - start_time) * 1000 # 毫秒
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print(f' - 推論耗時: {inference_time:.2f} ms')
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# 5. 處理推論結果
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print('[處理結果]')
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inf_node_output_list = []
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for node_idx in range(generic_raw_result.header.num_output_node):
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inference_float_node_output = kp.inference.generic_inference_retrieve_float_node(
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node_idx=node_idx,
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generic_raw_result=generic_raw_result,
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channels_ordering=kp.ChannelOrdering.KP_CHANNEL_ORDERING_CHW
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)
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inf_node_output_list.append(inference_float_node_output.ndarray.copy())
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# 整理成一維陣列並後處理
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probability = postprocess(np.array(inf_node_output_list).flatten())
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result_str = "Fire" if probability > 0.5 else "No Fire"
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# 6. 斷開設備連接
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kp.core.disconnect_devices(device_group=device_group)
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print('[已斷開設備連接]')
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# 回傳結果
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return {
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"result": result_str,
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"probability": probability,
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"inference_time_ms": inference_time
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}
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except Exception as e:
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print(f"錯誤: {str(e)}")
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# 嘗試斷開設備（若有連線）
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try:
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kp.core.disconnect_devices(device_group=device_group)
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except Exception:
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pass
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raise
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# 若作為主程式執行，支援從命令列讀取圖片檔案並測試推論
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# if __name__ == '__main__':
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# parser = argparse.ArgumentParser(
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# description='KL520 Fire Detection Model Inference'
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# )
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# parser.add_argument(
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# '-p', '--port_id', help='Port ID (Default: 0)', default=0, type=int
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# )
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# parser.add_argument(
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# '-m', '--model', help='NEF model path', default=model_path, type=str
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# )
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# parser.add_argument(
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# '-i', '--img', help='Image path', default=IMAGE_FILE_PATH, type=str
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# )
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# args = parser.parse_args()
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# # 讀取圖片（使用 cv2 讀取）
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# test_image = cv2.imread(args.img)
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# if test_image is None:
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# print(f"無法讀取圖片: {args.img}")
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# sys.exit(1)
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# # 構造參數字典
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# params = {
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# "port_id": args.port_id,
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# "model": args.model
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# }
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# # 呼叫推論介面函式
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# result = inference(test_image, params)
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# print("\n結果摘要:")
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# print(f"預測結果: {result['result']}")
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# print(f"信心分數: {result['probability']:.4f}")
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# print(f"推論時間: {result['inference_time_ms']:.2f} ms")
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