radxa-d6a
145 lines
6.0 KiB
Python
145 lines
6.0 KiB
Python
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import os
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import sys
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os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
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import cv2
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# Ensure benchmark uses local workspace code, not site-packages installation.
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_local_src = os.path.join(os.path.dirname(__file__), "src")
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if _local_src not in sys.path:
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sys.path.insert(0, _local_src)
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import orisys
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import numpy as np
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import time
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import argparse
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--video","-v", type=str, default="0", help="Camera ID / Video file path")
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parser.add_argument("--config","-c", type=str, default="./config/ddjx01.json", help="Configuration file path")
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parser.add_argument("--cal","-cal", type=str, default="./config/ddjx01.npy", help="Calibration file path")
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parser.add_argument("--verbose","-verbose", type=bool, default=True, help="Print detailed information")
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parser.add_argument("--debug","-debug", type=bool, default=True, help="Print detailed information")
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args = parser.parse_args()
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print(f"Using orisys module: {orisys.__file__}")
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# 步骤1：创建传感器对象 (必要步骤)，
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# config_name为配置文件名（内置配置文件名）或路径（自定义配置文件路径）
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# cal_path为校准文件路径, 每一个设备需要创建/使用一个校准文件
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# 方式1: 从视频文件读取
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try:
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input = int(args.video)
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is_camera = True
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sensor = orisys.Sensor(input, config_name=args.config, cal_path= args.cal, verbose=args.verbose, debug=args.debug)
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except:
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is_camera = False
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sensor = orisys.Sensor(args.video, config_name=args.config, cal_path= args.cal, verbose=args.verbose, debug=args.debug)
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# sensor = orisys.Sensor(2, config_name="./config/ddjx01_q6a.json", cal_path= "./config/ddjx01.npy", isstitch=True, cuda=False, verbose=True, backend='cpu')
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# 方式2: 从摄像头读取（取消注释使用）
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# sensor = orisys.Sensor(0, config_name="./config/prototype_of3.json", cal_path= "./config/cal.npy", isstitch=True, cuda=True, verbose=True)
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print("\n按 'q' 退出程序，按 'r' 重置光流追踪器")
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while True:
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# 步骤2：信息提取
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# 步骤 2.1: 获取并拼接图像 (必要步骤)
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start_time = time.perf_counter()
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sensor.get_img()
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end_time = time.perf_counter()
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get_img_time = end_time - start_time
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# if sensor.detect_motion(threshold=2):
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# print("motion detected")
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# 步骤 2.2: 计算形变场 (必要步骤)
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start_time = time.perf_counter()
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sensor.compute_flow()
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end_time = time.perf_counter()
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compute_flow_time = end_time - start_time
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start_time = time.perf_counter()
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sensor.compute_hhd()
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end_time = time.perf_counter()
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compute_hhd_time = end_time - start_time
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# 步骤 2.3: 计算触碰点
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start_time = time.perf_counter()
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# sensor.compute_contact()
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end_time = time.perf_counter()
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compute_contact_time = end_time - start_time
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#步骤 2.4: 读取信息
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start_time = time.perf_counter()
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fps, flow, vnormal, img, contour, centroid, depth_map = sensor.read_info(
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sensor.info.FPS,
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sensor.info.VRAW,
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sensor.info.VNORMAL,
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sensor.info.IMG,
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sensor.info.CONTOUR,
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sensor.info.CENTROID,
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sensor.info.DEPTH
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)
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end_time = time.perf_counter()
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read_info_time = end_time - start_time
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#步骤 3: 可视化显示
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# 步骤 3.1: 绘制形变场
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start_time = time.perf_counter()
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# arrows = orisys.util.draw_arrows(
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# img,
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# flow,
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# threshold=4,
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# grid_spacing=40,
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# arrow_scale=1.0
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# )
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# cv2.imshow("Deformation", arrows)
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# # 步骤 3.2: 在图像上触碰点和区域
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# image_with_foe = orisys.util.draw_contact(img, contour, centroid)
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# cv2.imshow("Contact Centroid", image_with_foe)
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# # 步骤 3.3: 绘制深度图
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# # 归一化散度值到0-255范围（使用绝对值，然后归一化）
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# div_abs = depth_map
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# if div_abs.max() > div_abs.min():
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# div_normalized = ((div_abs - div_abs.min()) / (div_abs.max() - div_abs.min()) * 255).astype(np.uint8)
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# else:
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# div_normalized = np.zeros_like(div_abs, dtype=np.uint8)
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# # 应用颜色映射（使用JET：蓝色=低散度，红色=高散度）
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# cv2.imshow("Depth Map", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
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#步骤 3.4: 打印资料
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# print(f"fps: {fps}")
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end_time = time.perf_counter()
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display_time = end_time - start_time
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# Get detailed timing from sensor (capture vs processing)
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capture_time = sensor._last_capture_time
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processing_time = sensor._last_processing_time
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total_time = get_img_time + compute_flow_time + compute_hhd_time + compute_contact_time + read_info_time + display_time
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print(f"get_img: {get_img_time*1000:.2f}ms (capture: {capture_time*1000:.2f}ms, processing: {processing_time*1000:.2f}ms), flow: {compute_flow_time*1000:.2f}ms,hhd: {compute_hhd_time*1000:.2f}ms, contact: {compute_contact_time*1000:.2f}ms, read: {read_info_time*1000:.2f}ms, display: {display_time*1000:.2f}ms, total: {total_time*1000:.2f}ms")
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#步骤 3.5: 键盘控制
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key = cv2.waitKey(1) & 0xFF
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if key == ord("q"):
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print("\n退出程序...")
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break
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if key == ord("r"):
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# 重置光流追踪器（当追踪漂移时使用）
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sensor.reset()
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print("传感器已重置")
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# =========================================================================
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# 4. 清理资源
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# =========================================================================
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sensor.disconnect() # 释放视频源和相关资源
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if __name__ == '__main__':
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main()
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