orisys-tech/orisys-sdk
3
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fixed async pipeline

Browse Source
This commit is contained in:
unknown
2025-12-30 17:17:57 +08:00
parent 157d7b1131
commit b4c3518ad5
5 changed files with 139 additions and 122 deletions
Download Patch File Download Diff File Expand all files Collapse all files
config/compound_eye.png
BIN
View File
Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 2.1 MiB

After

Width:  |  Height:  |  Size: 2.1 MiB

example1_basic_pipeline.py
+5 -3
View File
@@ -1,12 +1,13 @@
"""
示例 1: 基础用法 - 单传感器同步处理
本示例演示 OrisysSensor 的基本使用流程：
本示例演示 orisys 的基本使用流程：
1. 初始化传感器（视频文件或摄像头）
2. 循环获取图像并计算形变
2. 循环获取图像并计算形变和触碰点
3. 读取各类传感器数据
4. 可视化显示
适用场景：单传感器、简单应用、学习入门
"""
import os
os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
@@ -22,6 +23,7 @@ def main():
# 方式2: 从摄像头读取（取消注释使用）
sensor = orisys.Sensor(0, cuda=True, verbose=True, config_name="prototype")
sensor.export_config(path="./config/exported_config.json") # 导出配置文件供查看和修改
print("\n按 'q' 退出程序，按 'r' 重置光流追踪器")
example5_async.py → example2_async_pipeline.py
+16 -3
View File
@@ -31,13 +31,13 @@ os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" # Windows 兼容
import cv2
import orisys
import numpy as np
def main():
# =========================================================================
# 1. 初始化传感器
# =========================================================================
sensor = orisys.Sensor("./s1.mp4",isstitch=True,cuda=True,verbose=True)
sensor = orisys.Sensor(0, cuda=True, verbose=True, config_name="prototype")
# =========================================================================
# 2. 启用异步流水线
@@ -45,7 +45,7 @@ def main():
# queue_size: 每个阶段间队列的大小
# - 较小的值（1-2）：延迟低，但可能丢帧
# - 较大的值（3-5）：吞吐稳定，但延迟增加
sensor.enable_async(queue_size=2)
sensor.enable_async(queue_size=2, compute_contact=True)
print("\n异步模式已启用")
print("按 'q' 退出，按 'r' 重置光流追踪器")
@@ -79,7 +79,20 @@ def main():
threshold=5
)
cv2.imshow("异步模式 - 形变", arrows)
if data['centroid'] is not None:
# print(f"触碰点: {data['centroid']}")
image_with_foe = orisys.util.draw_contact(data['img'].copy(), data['contour'], data['centroid'])
cv2.imshow("Contact Centroid", image_with_foe)
if data['depth_map'] is not None:
# 归一化散度值到0-255范围（使用绝对值，然后归一化）
div_abs = data['depth_map']
if div_abs.max() > div_abs.min():
div_normalized = ((div_abs - div_abs.min()) / (div_abs.max() - div_abs.min()) * 255).astype(np.uint8)
else:
div_normalized = np.zeros_like(div_abs, dtype=np.uint8)
cv2.imshow("Depth Map", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
# 键盘控制
key = cv2.waitKey(1) & 0xFF
example2_config.py
-39
View File
@@ -1,39 +0,0 @@
import os
os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
import cv2
import orisys
def main():
global start_time
# 打开视频, config_name 可以根据传感型号选择
sensor1 = orisys.Sensor("./s1.mp4",isstitch=True,cuda = True,verbose = True, config_name="prototype",cal_path="./config/cal.npy")
# sensor1 = Sensor(0,isstitch=True,cuda = True,verbose = True, config_name="prototype")
# 打开摄像头
# sensor1 = Sensor(0,isstitch=True,cuda = True,verbose = True)
while True:
sensor1.get_img() # 提取并拼接复眼图像
sensor1.compute_deformation() # 计算形变和分解
# 提取信息：
# 向量场: VRAW, VNORMAL, VSHEAR: 原始，法向，切向
# 帧率，时间戳: FPS, TIMESTAMP
# 变形量总和: FNORMAL , FSHEARX, FSHEARY : 法向，切向（X)，切向（Y)
fps, fn, fx, fy, flow = sensor1.read_info(sensor1.info.FPS, sensor1.info.FNORMAL, sensor1.info.FSHEARX, sensor1.info.FSHEARY, sensor1.info.VRAW)
# 显示向量场
arrows = orisys.util.draw_arrows(sensor1.img, flow, threshold=5, grid_spacing=40, arrow_scale=1.0)
cv2.imshow("Deformation", arrows)
key = cv2.waitKey(1)
if key & 0xFF == ord("q"):
print("\n 停止程序")
break
if key & 0xFF == ord("r"):
sensor1.reset()
sensor1.disconnect() # 断开传感器连接
if __name__ == '__main__':
main()
src/orisys/sensor.py
+118 -77
View File
@@ -202,7 +202,7 @@ class Sensor:
return True
def compute_deformation(self, log_timing=False):
def compute_deformation(self):
"""
Compute optical flow and decompose into force components.
@@ -211,66 +211,76 @@ class Sensor:
"""
if self.__img is None:
raise Exception("Warning: Get image first")
if log_timing:
timings = {}
t_start = time.perf_counter()
# Optical flow
if log_timing: t0 = time.perf_counter()
self.__compute_flow()
self.__compute_hhd()
# FPS calculation
self._fps = 1.0 / (time.perf_counter() - self.last_log_time)
self.last_log_time = time.perf_counter()
return True
def __compute_flow(self):
if self.__img is None:
raise Exception("Warning: Get image first")
flow = self.__tracker.track(self.__img)
if hasattr(flow, 'download'):
flow = flow.download()
if log_timing: timings['optical_flow'] = (time.perf_counter() - t0) * 1000
self.__flow = flow
return True
def __compute_hhd(self):
flow = self.__flow
if flow is None:
raise Exception("Warning: Compute flow first")
# Initialize HHD solver
if self.__hhd is None:
self.__hhd = nHHD(grid=flow.shape[:2], spacings=(18, 18))
# Force computation
if log_timing: t0 = time.perf_counter()
if self.cuda and self.CUPY_AVAILABLE:
_, forces = self.__hhd.get_force_gpu_v2(flow)
else:
_, forces = self.__hhd.get_force(flow)
self.__fnormal, self.__fshearx, self.__fsheary, self.__ftor = forces
if log_timing: timings['hhd_decompose'] = (time.perf_counter() - t0) * 1000
# Get components
self.__vnormal = self.__hhd.get_normal()
self.__vshear = self.__hhd.get_shear()
# # Depth computation
# if log_timing: t0 = time.perf_counter()
# self.depth = getFilteredDensity(self.vnormal, use_cuda=self.cuda)
# if log_timing: timings['get_density'] = (time.perf_counter() - t0) * 1000
# Update state
self.__flow = flow
self.compute_deformation_valid = True
# FPS calculation
self._fps = 1.0 / (time.perf_counter() - self.last_log_time)
self.last_log_time = time.perf_counter()
if log_timing:
timings['total'] = (time.perf_counter() - t_start) * 1000
self._print_timings("compute_deformation", timings)
return True
def compute_contact(self):
def compute_contact(self, vnormal=None):
"""
Compute contact information from vnormal field.
multiple_foe=False
if not self.compute_deformation_valid:
raise Exception("Warning: Compute deformation first")
Args:
vnormal: Optional vnormal field. If None, uses self.__vnormal.
Returns:
dict: Contact result with centroid, contour, and depth_map
"""
multiple_foe = False
# Use provided vnormal or fall back to internal state
vnormal_to_use = vnormal if vnormal is not None else self.__vnormal
if vnormal_to_use is None:
raise Exception("Warning: vnormal is required for contact computation")
if multiple_foe:
result = self.__contact3d.compute_multiple_foe(self.__vnormal)
result = self.__contact3d.compute_multiple_foe(vnormal_to_use)
else:
if self.cuda:
result = self.__contact3d.compute_foe_gpu(self.__vnormal)
result = self.__contact3d.compute_foe_gpu(vnormal_to_use)
else:
result = self.__contact3d.compute_foe(self.__vnormal)
result = self.__contact3d.compute_foe(vnormal_to_use)
self.__centroid = result['centroid']
self.__centroid_found = result['centroid_found']
@@ -279,7 +289,7 @@ class Sensor:
self.__depth_map = result['divergence_map']
self.compute_contact_valid = True
return True
return result
# def get_depth(self, denoise_sigma=15):
@@ -368,7 +378,7 @@ class Sensor:
# Public API - Async Pipeline
# =========================================================================
def enable_async(self, queue_size=2):
def enable_async(self, queue_size=2, compute_contact=False):
"""
Enable 3-stage async pipeline: Capture → Flow → HHD.
@@ -376,25 +386,33 @@ class Sensor:
"""
if self._async_enabled:
return
self._compute_contact_enabled = compute_contact
self._capture_queue = Queue(maxsize=queue_size)
self._flow_queue = Queue(maxsize=queue_size)
self._result_queue = Queue(maxsize=queue_size)
self._contact_queue = Queue(maxsize=queue_size)
if compute_contact:
self._contact_queue = Queue(maxsize=queue_size)
self._stop_event = Event()
self._async_enabled = True
self._capture_thread = Thread(target=self._capture_worker, daemon=True)
self._flow_thread = Thread(target=self.__flow_worker, daemon=True)
self._compute_thread = Thread(target=self.__hhd_worker, daemon=True)
self._contact_thread = Thread(target=self._contact_worker, daemon=True)
self._flow_thread = Thread(target=self._flow_worker, daemon=True)
self._compute_thread = Thread(target=self._hhd_worker, daemon=True)
if compute_contact:
self._contact_thread = Thread(target=self._contact_worker, daemon=True)
self._capture_thread.start()
self._flow_thread.start()
self._compute_thread.start()
if compute_contact:
self._contact_thread.start()
if self.verbose:
print("Async pipeline enabled: Capture → Flow → HHD")
if compute_contact:
print("Async pipeline enabled: Capture → Flow → HHD → Contact")
else:
print("Async pipeline enabled: Capture → Flow → HHD")
def get_async(self, timeout=0.05):
"""
@@ -403,6 +421,7 @@ class Sensor:
Returns:
dict or None: Sensor data if available
"""
compute_contact = self._compute_contact_enabled
if not self._async_enabled:
return None
try:
@@ -413,8 +432,12 @@ class Sensor:
# Sync instance variables
# for k in ['img', 'flow', 'normal', 'shear', 'depth', 'fnormal', 'fshearx', 'fsheary', 'ftor', 'fps']:
for k in ['img', 'flow', 'normal', 'shear', 'fnormal', 'fshearx', 'fsheary', 'ftor', 'fps']:
setattr(self, k, result.get(k))
if compute_contact:
for k in ['img', 'flow', 'normal', 'shear', 'depth', 'centroid', 'centroid_found', 'contour', 'fps', 'fnormal', 'fshearx', 'fsheary', 'ftor']:
setattr(self, k, result.get(k))
else:
for k in ['img', 'flow', 'normal', 'shear', 'fnormal', 'fshearx', 'fsheary', 'ftor', 'fps']:
setattr(self, k, result.get(k))
self.vupdate = True
return result
except Empty:
@@ -564,37 +587,29 @@ class Sensor:
q.put(item)
def _capture_worker(self):
"""Stage 1: Capture and preprocess frames."""
alpha = 0.7
"""Stage 1: Capture and preprocess frames by reusing get_img()."""
while not self._stop_event.is_set():
try:
ret, frame = self.cap.read()
if not ret:
# Reuse get_img() to avoid code duplication
if self.get_img() is None:
continue
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
self.__stitcher.get_stitch_unit(gray)
self._extract_tiles(gray)
img = np.ascontiguousarray(self.__stitcher.stitch_rt_optimized())
img = cv2.resize(img, tuple(self._config_resolution_stitch), interpolation=cv2.INTER_CUBIC)
if self.__img_filtered is None:
self.__img_filtered = img.astype(np.float32)
else:
self.__img_filtered = alpha * img + (1 - alpha) * self.__img_filtered
self._queue_put(self._capture_queue, self.img_filtered.astype(np.uint8))
# Put the processed image in the queue
# get_img() updates self.__img, so we use that
if self.__img is not None:
self._queue_put(self._capture_queue, self.__img.copy())
except Exception as e:
if self.verbose: print(f"Capture error: {e}")
def _flow_worker(self):
"""Stage 2: Compute optical flow."""
"""Stage 2: Compute optical flow by reusing __compute_flow()."""
while not self._stop_event.is_set():
try:
img = self._capture_queue.get(timeout=0.1)
flow = self.tracker.track(img)
if hasattr(flow, 'download'):
flow = flow.download()
# Set __img to reuse __compute_flow()
self.__img = img
self.__compute_flow()
flow = self.__flow.copy() if self.__flow is not None else None
self._queue_put(self._flow_queue, (img, flow))
except Empty:
continue
@@ -602,38 +617,64 @@ class Sensor:
if self.verbose: print(f"Flow error: {e}")
def _hhd_worker(self):
"""Stage 3: HHD decomposition."""
"""Stage 3: HHD decomposition by reusing __compute_hhd()."""
while not self._stop_event.is_set():
try:
img, flow = self._flow_queue.get(timeout=0.1)
if self.hhd is None:
self.hhd = nHHD(grid=flow.shape[:2], spacings=(18, 18))
if self.cuda and self.CUPY_AVAILABLE:
_, forces = self.hhd.get_force_gpu_v2(flow)
else:
_, forces = self.hhd.get_force(flow)
self._queue_put(self._result_queue, {
# Set __flow to reuse __compute_hhd()
self.__flow = flow
self.__compute_hhd()
# Get results from internal variables set by __compute_hhd()
result = {
'img': img, 'flow': flow,
'normal': self.hhd.get_normal(), 'shear': self.hhd.get_shear(),
'normal': self.__vnormal.copy() if self.__vnormal is not None else None,
'shear': self.__vshear.copy() if self.__vshear is not None else None,
# 'depth': getFilteredDensity(flow, use_cuda=self.cuda),
'fnormal': forces[0], 'fshearx': forces[1],
'fsheary': forces[2], 'ftor': forces[3],
'fnormal': self.__fnormal, 'fshearx': self.__fshearx,
'fsheary': self.__fsheary, 'ftor': self.__ftor,
}
# Always include contact fields to ensure consistent result structure
# When compute_contact is True, contact worker will update these values
# When compute_contact is False, they remain None
result.update({
'centroid': None,
'centroid_found': False,
'contour': None,
'depth_map': None,
'depth': None, # Alias for depth_map for compatibility
})
self._queue_put(self._result_queue, result)
except Empty:
continue
except Exception as e:
if self.verbose: print(f"HHD error: {e}")
def _contact_worker(self):
"""Stage 4: Contact computation."""
"""Stage 4: Contact computation using vnormal from HHD results."""
while not self._stop_event.is_set():
try:
img, flow = self._flow_queue.get(timeout=0.1)
contact_result = self.compute_contact(flow)
self._queue_put(self._result_queue, contact_result)
# Get results from HHD worker which includes vnormal
hhd_result = self._result_queue.get(timeout=0.1)
vnormal = hhd_result.get('normal') # This is vnormal from HHD
if vnormal is not None:
# Compute contact using vnormal directly
contact_result = self.compute_contact(vnormal=vnormal)
# Add contact results to the existing HHD result
depth_map = contact_result.get('divergence_map')
hhd_result.update({
'centroid': contact_result.get('centroid'),
'centroid_found': contact_result.get('centroid_found'),
'contour': contact_result.get('contour'),
'depth_map': depth_map,
'depth': depth_map, # Alias for depth_map for compatibility
})
# Put updated result back to queue
self._queue_put(self._result_queue, hhd_result)
except Empty:
continue
except Exception as e: