update after ojzm

calibration_script.py

@@ -0,0 +1,192 @@
+import os
+os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
+import cv2
+import orisys
+import numpy as np
+import time
+
+import pyqtgraph as pg
+
+def create_plot():
+    p1 = win.addPlot(title="法相力")
+    p1.setLabel('bottom', '时间', units='s')
+    win.nextRow()
+    p2 = win.addPlot(title="切向力 X")
+    p2.setLabel('bottom', '时间', units='s')
+    win.nextRow()
+    p3 = win.addPlot(title="切向力 Y")
+    p3.setLabel('bottom', '时间', units='s')
+    plot_af = p1.plot(pen='k')
+    plot_tfx = p2.plot(pen='k')
+    plot_tfy = p3.plot(pen='k')
+    return p1, p2, p3, plot_af, plot_tfx, plot_tfy
+
+def update(plot_af, plot_tfx, plot_tfy, data, p1, p2, p3):
+    # global plot_af, plot_tfx, plot_tfy, data, p1, p2, p3
+    plot_af.setData(data[:,:,0])
+    p1.setYRange(min(data[:,1,0]),max(data[:,1,0]))
+    plot_tfx.setData(data[:,:,1])
+    p2.setYRange(min(data[:,1,1]),max(data[:,1,1]))
+    plot_tfy.setData(data[:,:,2])
+    p3.setYRange(min(data[:,1,2]),max(data[:,1,2]))
+
+# 深度图
+def draw_magnitude_map(vfield, threshold=3.0, colormap=cv2.COLORMAP_JET) -> np.ndarray:
+        """
+        Draw color map of pointwise magnitude of velocity field.
+        
+        Args:
+            vfield: Velocity field to visualize (2D vector field)
+            threshold: Minimum magnitude to display (below threshold shows as zero/blue)
+            colormap: OpenCV colormap to use (default: COLORMAP_JET)
+        
+        Returns:
+            np.ndarray: 400x400 BGR image with colored magnitude map
+        """
+        flow_field = vfield
+            
+        # Compute magnitude at each point
+        magnitude = np.sqrt(flow_field[:, :, 0]**2 + flow_field[:, :, 1]**2)
+        
+        # Smooth by downsampling and interpolating back
+        downsample_factor = 10
+        h, w = magnitude.shape
+        magnitude_downsampled = magnitude[::downsample_factor, ::downsample_factor]
+        magnitude_smoothed = cv2.resize(magnitude_downsampled, (w, h), interpolation=cv2.INTER_CUBIC)
+        
+        # Apply threshold - set below-threshold values to zero
+        magnitude_thresholded = magnitude_smoothed.copy()
+        magnitude_thresholded[magnitude_smoothed < threshold] = 0
+        
+        # Normalize to 0-255 range
+        mag_min, mag_max = magnitude_thresholded.min(), magnitude_thresholded.max()
+        if mag_max > mag_min:
+            magnitude_norm = ((magnitude_thresholded - mag_min) / (mag_max - mag_min) * 255).astype(np.uint8)
+        else:
+            magnitude_norm = np.zeros_like(magnitude_thresholded, dtype=np.uint8)
+        
+        # Apply colormap
+        magnitude_colored = cv2.applyColorMap(magnitude_norm, colormap)
+        
+        # Resize to output size
+        out_img = cv2.resize(magnitude_colored, (400, 400), interpolation=cv2.INTER_CUBIC)
+        
+        return out_img
+
+# 实时图表
+npoints = 100 # 图表数据长度
+pg.setConfigOption('background', 'w')
+pg.setConfigOption('foreground', 'k')
+app = pg.mkQApp("Plotting Example")
+win = pg.GraphicsLayoutWidget(show=True, title="Basic plotting examples")
+win.setWindowTitle('pyqtgraph example: Plotting')
+p1, p2, p3, plot_af, plot_tfx, plot_tfy = create_plot()
+data = np.zeros([npoints,2,3])
+start_time = None  # Will be set when sensor starts
+for i in range(3):
+    data[:,0,i] = np.linspace(-npoints,0,npoints)
+
+
+
+def main():
+    global start_time
+    
+    # 打开视频
+    # sensor1 = Sensor("./s1.mp4",isstitch=True,cuda = True,verbose = True)
+   # 方式1: 从视频文件读取
+    sensor1 = orisys.Sensor(8, config_name="./config/ddjx01.json", cal_path= "./config/ddjx01.npy", cuda=False, verbose=True)
+
+    start_time = time.time()
+    is_save = False
+    frame_count = 0
+    writer = None
+    while True:
+        sensor1.get_img() # 提取并拼接复眼图像
+        
+        if is_save and writer is not None and sensor1.frame is not None:
+            frame_count += 1
+            save_img = sensor1.frame
+            
+            # Convert grayscale (1 channel) to BGR (3 channels) if needed
+            if len(save_img.shape) == 2 or (len(save_img.shape) == 3 and save_img.shape[2] == 1):
+                if len(save_img.shape) == 3:
+                    save_img = save_img.squeeze(2)
+                save_img = cv2.cvtColor(save_img, cv2.COLOR_GRAY2BGR)
+            
+            writer.write(save_img)
+
+        sensor1.compute_deformation(check_motion=False) # 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)
+        # print(fps,fn,fx,fy)
+        # 显示向量场
+        arrows  = orisys.util.draw_arrows(sensor1.img, flow, threshold=5, grid_spacing=10, arrow_scale=1.0) 
+        cv2.imshow("Deformation", arrows)
+        # 显示深度图
+        mag = draw_magnitude_map(flow)
+        cv2.imshow("Magnitude", mag)
+
+        # 显示图表 - 更新时间轴
+        current_time = time.time() - start_time
+        for i in range(3):
+            data[:-1, 0, i] = data[1:, 0, i]  # shift time data left
+            data[:-1, 1, i] = data[1:, 1, i]  # shift force data left
+        data[-1, 0, :] = current_time  # set current time for all plots
+        data[-1, 1, 0] = fn
+        data[-1, 1, 1] = fx
+        data[-1, 1, 2] = fy
+        fn_mean = np.mean(data[:,1,0])
+        fx_mean = np.mean(data[:,1,1])
+        fy_mean = np.mean(data[:,1,2])
+        print(f"fps: {fps}, fn_mean: {fn_mean:.2f}, fx_mean: {fx_mean:.2f}, fy_mean: {fy_mean:.2f}")
+        update(plot_af, plot_tfx, plot_tfy, data, p1, p2, p3) # Update plot
+
+        key = cv2.waitKey(1)
+        if key & 0xFF == ord("q"):
+            print("\n 停止程序")
+            break
+        if key == ord("s"):
+            save_path = f"./data/pulse_test.mp4"
+            # Ensure directory exists
+            os.makedirs(os.path.dirname(save_path), exist_ok=True)
+            # Get frame dimensions from the actual frame
+            save_img = sensor1.frame
+
+            if save_img is not None:
+                # Convert grayscale (1 channel) to BGR (3 channels) if needed for dimension check
+                temp_img = save_img.copy()
+                if len(temp_img.shape) == 2 or (len(temp_img.shape) == 3 and temp_img.shape[2] == 1):
+                    if len(temp_img.shape) == 3:
+                        temp_img = temp_img.squeeze(2)
+                    temp_img = cv2.cvtColor(temp_img, cv2.COLOR_GRAY2BGR)
+                
+                frame_height, frame_width = temp_img.shape[:2]
+                writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), 30, (frame_width, frame_height))
+                if writer.isOpened():
+                    is_save = True
+                    print(f"Start writing to {save_path}")
+                else:
+                    print(f"Failed to open video writer for {save_path}")
+                    writer = None
+            else:
+                print("No frame available to determine video dimensions")
+        if key == ord("e"):
+            is_save = False
+            if writer is not None:
+                writer.release()
+                writer = None
+            if save_path is not None:
+                print(f"Stop writing to {save_path}, total frames: {frame_count}")
+            else:
+                print(f"Stop writing, total frames: {frame_count}")
+            frame_count = 0
+    
+    sensor1.disconnect() # 断开传感器连接
+    
+
+
+if __name__ == '__main__':
+    main()

config/ddjx01.json

@@ -12,7 +12,7 @@
   "stitcher": {
     "min_area_frac": 0.01,
     "max_area_frac": 0.04,
-    "max_aspect_ratio": 100,
+    "max_aspect_ratio": 50,
     "index": [1,2,3,6,7,8,11,12,13],
     "margin": 10,
     "nominal_ov_pos": 100,

example2_two_sensor_pipeline.py

@@ -124,21 +124,21 @@ def main():
     # 1. 配置多个传感器
     # =========================================================================
     # 目标帧率：设为正数时，两路传感器会按此帧率限速，使 FPS 接近一致；设为 None 则不限速
-    target_fps = 15
+    target_fps = 30
 
     num_sensors = 2
     sensor_configs = [
         {
             'sensor_id': 0,
-            'vid_src': './s1_720p.mp4',
+            'vid_src': 4,
-            'config_name': './config/prototype_of3_720p.json',
+            'config_name': './config/prototype_of3_480p.json',
-            'cal_path': './config/dd01a_0_720p.npy'
+            'cal_path': './config/dd01a_0_480p.npy'
-        },
+        },  
         {
             'sensor_id': 1,
-            'vid_src': './s1_720p.mp4',  # 可以改为不同的视频源或摄像头索引
+            'vid_src': 6,  # 可以改为不同的视频源或摄像头索引
-            'config_name': './config/prototype_of3_720p.json',
+            'config_name': './config/prototype_of3_480p.json',
-            'cal_path': './config/dd01a_0_720p.npy'
+            'cal_path': './config/dd01a_0_480p.npy'
         },
     ]
 

example3_plot_data.py

@@ -94,7 +94,7 @@ def main():
     # 打开视频
     # sensor1 = Sensor("./s1.mp4",isstitch=True,cuda = True,verbose = True)
    # 方式1: 从视频文件读取
-    sensor1 = orisys.Sensor(0, config_name="./config/prototype_of3.json", cal_path= "./config/dd01a_0.npy", isstitch=True, cuda=False, verbose=True)
+    sensor1 = orisys.Sensor(8, config_name="./config/ddjx01.json", cal_path= "./config/ddjx01.npy", cuda=False, verbose=True)
 
     start_time = time.time()
     is_save = False
@@ -115,7 +115,7 @@ def main():
             
             writer.write(save_img)
 
-        sensor1.compute_deformation() # 计算形变和分解
+        sensor1.compute_deformation(check_motion=False) # 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

patch_generation/patch_generation_3_org.py

@@ -115,8 +115,10 @@ def generate_color_pattern(h, w, d, r):
 def main():
     # Parameters for the pattern.
     # For example, using 100x100 pixels image with patches of 3x3 pixels.
-    height = 2750
+    # height = 2750
-    width = 1950
+    # width = 1950
+    height = 1200
+    width = 1000
     patch_size = 12
     # Set r to a value that gives sufficient difference.
     # Note: r is the threshold for squared difference per channel, and the condition is

src/orisys/Stitcher.py

@@ -449,7 +449,7 @@ class Stitcher:
         n_boxes = 0
         bk_thresh = 0  # initial black threshold
         bk_thresh_step = 5
-        bk_thresh_max = 100
+        bk_thresh_max = 150
 
         # Also include a low global V threshold to capture colored but dark tiles
         hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
@@ -458,7 +458,12 @@ class Stitcher:
         while n_boxes != n_boxes_target and bk_thresh <= bk_thresh_max:
             _, v_mask = cv2.threshold(v, bk_thresh, 255, cv2.THRESH_BINARY)  # tune 50 if needed
 
-            mask = v_mask
+            mask = v_mask.copy()
+            # Exclude left and right boundary (20 pixels each) from detection
+            edge = 20
+            if w > edge * 2:
+                mask[:, :edge] = 0
+                mask[:, -edge:] = 0
 
             # Morphology: remove small specks, fill small holes
             k = max(1, morph_k)

test_opencl.py

@@ -0,0 +1,33 @@
+import pyopencl as cl
+
+# Get all platforms
+platforms = cl.get_platforms()
+if not platforms:
+    raise RuntimeError("No OpenCL platforms found")
+
+# Use the first platform (rusticl in your case)
+platform = platforms[0]
+print("Using platform:", platform.name)
+
+# Get devices for that platform
+devices = platform.get_devices()
+if not devices:
+    raise RuntimeError("No devices found on platform")
+
+for platform in cl.get_platforms():
+    for device in platform.get_devices():
+        print("Device:", device.name)
+        # if device.type & cl.device_type.GPU == 0:
+        #     print("Skipping non-GPU device:", device.name)
+
+# Use the first device (your Adreno GPU)
+device = devices[0]
+print("Using device:", device.name)
+
+# Create a context with that device
+ctx = cl.Context([device])
+
+# Create a command queue
+queue = cl.CommandQueue(ctx)
+
+print("Context and command queue created successfully!")