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深度图渲染效果提交修改

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This commit is contained in:
zhaozilong12 2025-08-19 18:15:56 +08:00
parent a83df57987
commit 640d14c57a
2 changed files with 111 additions and 82 deletions
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99
backend/devices/femtobolt_manager.py
View File

@ -16,6 +16,7 @@ from typing import Optional, Dict, Any, Tuple
import logging import logging
from collections import deque from collections import deque
import gc import gc
from matplotlib.colors import LinearSegmentedColormap
try: try:
from .base_device import BaseDevice from .base_device import BaseDevice
@ -115,6 +116,11 @@ class FemtoBoltManager(BaseDevice):
self._grid_bg = None self._grid_bg = None
self._grid_size = (480, 640) # 默认尺寸 self._grid_size = (480, 640) # 默认尺寸
# 自定义彩虹色 colormap（参考testfemtobolt.py）
colors = ['fuchsia', 'red', 'yellow', 'lime', 'cyan', 'blue',
'fuchsia', 'red', 'yellow', 'lime', 'cyan', 'blue']
self.custom_cmap = LinearSegmentedColormap.from_list("custom_cmap", colors)
self.logger.info("FemtoBolt管理器初始化完成") self.logger.info("FemtoBolt管理器初始化完成")
def _update_gamma_lut(self): def _update_gamma_lut(self):
@ -520,32 +526,23 @@ class FemtoBoltManager(BaseDevice):
self._grid_bg = bg self._grid_bg = bg
self._grid_size = (rows, cols) self._grid_size = (rows, cols)
# 深度范围过滤 + 归一化
depth_clipped = np.where(
(depth_image >= self.depth_range_min) & (depth_image <= self.depth_range_max),
depth_image, 0
)
if np.max(depth_clipped) > 0:
depth_normalized = ((depth_clipped - self.depth_range_min) / (self.depth_range_max - self.depth_range_min) * 255).astype(np.uint8)
else:
depth_normalized = np.zeros((rows, cols), dtype=np.uint8)
# 对比度与伽马
depth_normalized = cv2.convertScaleAbs(depth_normalized, alpha=1.5, beta=0)
if self._gamma_lut is None or self._current_gamma != self.gamma_value:
self._update_gamma_lut()
depth_gamma = cv2.LUT(depth_normalized, self._gamma_lut)
# 伪彩色
depth_colored = cv2.applyColorMap(depth_gamma, cv2.COLORMAP_JET)
# 合成到背景（避免copy，使用背景副本）
background = self._grid_bg.copy() background = self._grid_bg.copy()
mask_valid = (depth_clipped > 0)
for c in range(3): # 生成深度掩码，仅保留指定范围内的像素
channel = background[:, :, c] mask_valid = (depth_image >= self.depth_range_min) & (depth_image <= self.depth_range_max)
channel[mask_valid] = depth_colored[:, :, c][mask_valid] depth_clipped = np.clip(depth_image, self.depth_range_min, self.depth_range_max)
depth_colored_final = background normed = (depth_clipped.astype(np.float32) - self.depth_range_min) / (self.depth_range_max - self.depth_range_min)
# 反转映射，保证颜色方向与之前一致
normed = 1.0 - normed
# 应用自定义 colormap，将深度值映射到 RGB
rgba = self.custom_cmap(normed)
rgb = (rgba[..., :3] * 255).astype(np.uint8)
# 叠加：在背景上覆盖彩色深度图（掩码处不覆盖，保留灰色背景+网格）
depth_colored_final = background.copy()
depth_colored_final[mask_valid] = rgb[mask_valid]
# 裁剪宽度 # 裁剪宽度
height, width = depth_colored_final.shape[:2] height, width = depth_colored_final.shape[:2]
@ -605,33 +602,53 @@ class FemtoBoltManager(BaseDevice):
def _process_depth_image(self, depth_image) -> np.ndarray: def _process_depth_image(self, depth_image) -> np.ndarray:
""" """
处理深度图像 处理深度图像（采用testfemtobolt.py的渲染方式）
""" """
try: try:
if not isinstance(depth_image, np.ndarray): if not isinstance(depth_image, np.ndarray):
self.logger.error(f"输入的深度图像不是numpy数组: {type(depth_image)}") self.logger.error(f"输入的深度图像不是numpy数组: {type(depth_image)}")
return np.zeros((480, 640, 3), dtype=np.uint8) return np.zeros((480, 640, 3), dtype=np.uint8)
mask = (depth_image >= self.depth_range_min) & (depth_image <= self.depth_range_max) # 确保二维数据
filtered_depth = np.where(mask, depth_image, 0) if depth_image.ndim == 3 and depth_image.shape[2] == 1:
depth_image = depth_image[:, :, 0]
if np.max(filtered_depth) > 0: h, w = depth_image.shape
normalized = ((filtered_depth - self.depth_range_min) / (self.depth_range_max - self.depth_range_min) * 255).astype(np.uint8)
else:
normalized = np.zeros_like(filtered_depth, dtype=np.uint8)
enhanced = cv2.convertScaleAbs(normalized, alpha=self.contrast_factor, beta=0) # 生成灰色背景和白色网格（参考testfemtobolt.py）
background = np.full((h, w, 3), 128, dtype=np.uint8) # 灰色背景
# 绘制网格线
for x in range(0, w, 50): # 每50像素一条竖线
cv2.line(background, (x, 0), (x, h-1), (255, 255, 255), 1)
for y in range(0, h, 50): # 每50像素一条横线
cv2.line(background, (0, y), (w-1, y), (255, 255, 255), 1)
if self._gamma_lut is None or self._current_gamma != self.gamma_value: # 生成深度掩码，仅保留指定范围内的像素
self._update_gamma_lut() mask_valid = (depth_image >= self.depth_range_min) & (depth_image <= self.depth_range_max)
gamma_corrected = cv2.LUT(enhanced, self._gamma_lut) depth_clipped = np.clip(depth_image, self.depth_range_min, self.depth_range_max)
normed = (depth_clipped.astype(np.float32) - self.depth_range_min) / (self.depth_range_max - self.depth_range_min)
if self.use_pseudo_color: # 反转映射，保证颜色方向与之前一致
colored = cv2.applyColorMap(gamma_corrected, cv2.COLORMAP_JET) normed = 1.0 - normed
else:
colored = cv2.cvtColor(gamma_corrected, cv2.COLOR_GRAY2BGR) # 应用自定义 colormap，将深度值映射到 RGB
rgba = self.custom_cmap(normed)
rgb = (rgba[..., :3] * 255).astype(np.uint8)
# 叠加：在背景上覆盖彩色深度图（掩码处不覆盖，保留灰色背景+网格）
final_img = background.copy()
final_img[mask_valid] = rgb[mask_valid]
# 裁剪宽度（保持原有功能）
height, width = final_img.shape[:2]
target_width = height // 2
if width > target_width:
left = (width - target_width) // 2
right = left + target_width
final_img = final_img[:, left:right]
return final_img
return colored
except Exception as e: except Exception as e:
self.logger.error(f"处理深度图像失败: {e}") self.logger.error(f"处理深度图像失败: {e}")
return np.zeros((480, 640, 3), dtype=np.uint8) return np.zeros((480, 640, 3), dtype=np.uint8)

94
backend/tests/testfemtobolt.py
View File

@ -1,47 +1,45 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os import os
import time
import numpy as np import numpy as np
import matplotlib.pyplot as plt import cv2
from matplotlib.colors import LinearSegmentedColormap from matplotlib.colors import LinearSegmentedColormap
class FemtoBoltViewer: class FemtoBoltViewer:
def __init__(self, depth_min=900, depth_max=1300): def __init__(self, depth_min=900, depth_max=1300):
self.depth_min = depth_min self.depth_min = depth_min
self.depth_max = depth_max self.depth_max = depth_max
# 自定义colormap # 自定义彩虹色 colormap
colors = ['fuchsia', 'red', 'yellow', 'lime', 'cyan', 'blue', colors = ['fuchsia', 'red', 'yellow', 'lime', 'cyan', 'blue',
'fuchsia', 'red', 'yellow', 'lime', 'cyan', 'blue'] 'fuchsia', 'red', 'yellow', 'lime', 'cyan', 'blue']
self.cmap = LinearSegmentedColormap.from_list("custom_cmap", colors) self.cmap = LinearSegmentedColormap.from_list("custom_cmap", colors)
# SDK设备句柄 # SDK 设备句柄和配置
self.device_handle = None self.device_handle = None
self.pykinect = None self.pykinect = None
self.config = None self.config = None
# 初始化matplotlib figure # 缓存背景+网格图像（仅生成一次）
plt.ion() self.background = None
self.fig, self.ax = plt.subplots(figsize=(7, 7))
# OpenCV 窗口
cv2.namedWindow("Depth CV")
def _load_sdk(self): def _load_sdk(self):
"""加载FemtoBolt SDK""" """加载并初始化 FemtoBolt SDK"""
try: try:
import pykinect_azure as pykinect import pykinect_azure as pykinect
self.pykinect = pykinect self.pykinect = pykinect
# 配置DLL路径
base_dir = os.path.dirname(os.path.abspath(__file__)) base_dir = os.path.dirname(os.path.abspath(__file__))
dll_path = os.path.join(base_dir, "..", "dll", "femtobolt", "bin", "k4a.dll") dll_path = os.path.join(base_dir, "..", "dll", "femtobolt", "bin", "k4a.dll")
self.pykinect.initialize_libraries(track_body=False, module_k4a_path=dll_path) self.pykinect.initialize_libraries(track_body=False, module_k4a_path=dll_path)
return True return True
except Exception as e: except Exception as e:
print(f"加载SDK失败: {e}") print(f"加载 SDK 失败: {e}")
return False return False
def _configure_device(self): def _configure_device(self):
"""配置FemtoBolt设备""" """配置 FemtoBolt 深度相机"""
self.config = self.pykinect.default_configuration self.config = self.pykinect.default_configuration
self.config.depth_mode = self.pykinect.K4A_DEPTH_MODE_NFOV_UNBINNED self.config.depth_mode = self.pykinect.K4A_DEPTH_MODE_NFOV_UNBINNED
self.config.camera_fps = self.pykinect.K4A_FRAMES_PER_SECOND_15 self.config.camera_fps = self.pykinect.K4A_FRAMES_PER_SECOND_15
@ -49,56 +47,70 @@ class FemtoBoltViewer:
self.config.color_resolution = 0 self.config.color_resolution = 0
self.device_handle = self.pykinect.start_device(config=self.config) self.device_handle = self.pykinect.start_device(config=self.config)
def _render_depth(self, depth_image: np.ndarray): def _get_color_image(self, depth_image):
"""使用matplotlib绘制深度图，带背景和自定义colormap""" """将原始深度图转换为叠加背景网格后的 RGB 彩色图像"""
# 过滤深度范围 h, w = depth_image.shape
depth = np.where((depth_image >= self.depth_min) & (depth_image <= self.depth_max), # 第一次调用时生成灰色背景和白色网格
depth_image, 0) if self.background is None:
self.background = np.full((h, w, 3), 128, dtype=np.uint8) # 灰色 (0.5 -> 128)
# 绘制网格线
for x in range(w):
cv2.line(self.background, (x, 0), (x, h-1), (255, 255, 255), 1)
for y in range(h):
cv2.line(self.background, (0, y), (w-1, y), (255, 255, 255), 1)
# 屏蔽深度为0的部分 # 生成深度掩码，仅保留指定范围内的像素
depth = np.ma.masked_equal(depth, 0) mask_valid = (depth_image >= self.depth_min) & (depth_image <= self.depth_max)
depth_clipped = np.clip(depth_image, self.depth_min, self.depth_max)
normed = (depth_clipped.astype(np.float32) - self.depth_min) / (self.depth_max - self.depth_min)
# 背景图（灰色） # 反转映射，保证颜色方向与之前一致
background = np.ones_like(depth) * 0.5 normed = 1.0 - normed
self.ax.clear() # 应用自定义 colormap，将深度值映射到 RGB
# 绘制背景 rgba = self.cmap(normed)
self.ax.imshow(background, origin='lower', cmap='gray', alpha=0.3) rgb = (rgba[..., :3] * 255).astype(np.uint8)
# 绘制白色栅格线
self.ax.grid(True, which='both', axis='both', color='white', linestyle='-', linewidth=1, zorder=0) # 叠加：在背景上覆盖彩色深度图（掩码处不覆盖，保留灰色背景+网格）
# 绘制深度等高线 final_img = self.background.copy()
self.ax.contourf(depth, levels=200, cmap=self.cmap, vmin=self.depth_min, vmax=self.depth_max, origin='upper', zorder=2) final_img[mask_valid] = rgb[mask_valid]
plt.pause(0.001) return final_img
plt.draw()
def run(self): def run(self):
if not self._load_sdk(): if not self._load_sdk():
print("SDK加载失败，退出") print("SDK 加载失败，程序退出")
return return
self._configure_device() self._configure_device()
print("FemtoBolt深度相机启动成功，按 Ctrl+C 退出") print("FemtoBolt 深度相机启动成功，按 Ctrl+C 或 ESC 退出")
try: try:
while True: while True:
capture = self.device_handle.update() capture = self.device_handle.update()
if capture is None: if capture is None:
continue continue
ret, depth_image = capture.get_depth_image() ret, depth_image = capture.get_depth_image()
if not ret or depth_image is None: if not ret or depth_image is None:
continue continue
self._render_depth(depth_image) # 转换并渲染当前帧
final_img = self._get_color_image(depth_image)
# OpenCV 显示
cv2.imshow("Depth CV", final_img)
# 按 ESC 键退出
if cv2.waitKey(1) & 0xFF == 27:
break
except KeyboardInterrupt: except KeyboardInterrupt:
print("退出程序") print("检测到退出信号，结束程序")
finally: finally:
self.device_handle.stop() if self.device_handle:
self.device_handle.close() self.device_handle.stop()
plt.close(self.fig) self.device_handle.close()
cv2.destroyAllWindows()
if __name__ == "__main__": if __name__ == "__main__":
viewer = FemtoBoltViewer(depth_min=900, depth_max=1300) viewer = FemtoBoltViewer(depth_min=900, depth_max=1100)
viewer.run() viewer.run()