Replace setup.py with pyproject.toml

wrappers/python/examples/align-depth2color.py

@@ -12,88 +12,92 @@
 # Import OpenCV for easy image rendering
 import cv2
 
-# Create a pipeline
-pipeline = rs.pipeline()
-
-# Create a config and configure the pipeline to stream
-#  different resolutions of color and depth streams
-config = rs.config()
-
-# Get device product line for setting a supporting resolution
-pipeline_wrapper = rs.pipeline_wrapper(pipeline)
-pipeline_profile = config.resolve(pipeline_wrapper)
-device = pipeline_profile.get_device()
-device_product_line = str(device.get_info(rs.camera_info.product_line))
-
-found_rgb = False
-for s in device.sensors:
-    if s.get_info(rs.camera_info.name) == 'RGB Camera':
-        found_rgb = True
-        break
-if not found_rgb:
-    print("The demo requires Depth camera with Color sensor")
-    exit(0)
-
-config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
-config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
-
-# Start streaming
-profile = pipeline.start(config)
-
-# Getting the depth sensor's depth scale (see rs-align example for explanation)
-depth_sensor = profile.get_device().first_depth_sensor()
-depth_scale = depth_sensor.get_depth_scale()
-print("Depth Scale is: " , depth_scale)
-
-# We will be removing the background of objects more than
-#  clipping_distance_in_meters meters away
-clipping_distance_in_meters = 1 #1 meter
-clipping_distance = clipping_distance_in_meters / depth_scale
-
-# Create an align object
-# rs.align allows us to perform alignment of depth frames to others frames
-# The "align_to" is the stream type to which we plan to align depth frames.
-align_to = rs.stream.color
-align = rs.align(align_to)
-
-# Streaming loop
-try:
-    while True:
-        # Get frameset of color and depth
-        frames = pipeline.wait_for_frames()
-        # frames.get_depth_frame() is a 640x360 depth image
-
-        # Align the depth frame to color frame
-        aligned_frames = align.process(frames)
-
-        # Get aligned frames
-        aligned_depth_frame = aligned_frames.get_depth_frame() # aligned_depth_frame is a 640x480 depth image
-        color_frame = aligned_frames.get_color_frame()
-
-        # Validate that both frames are valid
-        if not aligned_depth_frame or not color_frame:
-            continue
-
-        depth_image = np.asanyarray(aligned_depth_frame.get_data())
-        color_image = np.asanyarray(color_frame.get_data())
-
-        # Remove background - Set pixels further than clipping_distance to grey
-        grey_color = 153
-        depth_image_3d = np.dstack((depth_image,depth_image,depth_image)) #depth image is 1 channel, color is 3 channels
-        bg_removed = np.where((depth_image_3d > clipping_distance) | (depth_image_3d <= 0), grey_color, color_image)
-
-        # Render images:
-        #   depth align to color on left
-        #   depth on right
-        depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
-        images = np.hstack((bg_removed, depth_colormap))
-
-        cv2.namedWindow('Align Example', cv2.WINDOW_NORMAL)
-        cv2.imshow('Align Example', images)
-        key = cv2.waitKey(1)
-        # Press esc or 'q' to close the image window
-        if key & 0xFF == ord('q') or key == 27:
-            cv2.destroyAllWindows()
+def main():
+    # Create a pipeline
+    pipeline = rs.pipeline()
+
+    # Create a config and configure the pipeline to stream
+    #  different resolutions of color and depth streams
+    config = rs.config()
+
+    # Get device product line for setting a supporting resolution
+    pipeline_wrapper = rs.pipeline_wrapper(pipeline)
+    pipeline_profile = config.resolve(pipeline_wrapper)
+    device = pipeline_profile.get_device()
+    device_product_line = str(device.get_info(rs.camera_info.product_line))
+
+    found_rgb = False
+    for s in device.sensors:
+        if s.get_info(rs.camera_info.name) == 'RGB Camera':
+            found_rgb = True
             break
-finally:
-    pipeline.stop()
+    if not found_rgb:
+        print("The demo requires Depth camera with Color sensor")
+        exit(0)
+
+    config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
+    config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
+
+    # Start streaming
+    profile = pipeline.start(config)
+
+    # Getting the depth sensor's depth scale (see rs-align example for explanation)
+    depth_sensor = profile.get_device().first_depth_sensor()
+    depth_scale = depth_sensor.get_depth_scale()
+    print("Depth Scale is: " , depth_scale)
+
+    # We will be removing the background of objects more than
+    #  clipping_distance_in_meters meters away
+    clipping_distance_in_meters = 1 #1 meter
+    clipping_distance = clipping_distance_in_meters / depth_scale
+
+    # Create an align object
+    # rs.align allows us to perform alignment of depth frames to others frames
+    # The "align_to" is the stream type to which we plan to align depth frames.
+    align_to = rs.stream.color
+    align = rs.align(align_to)
+
+    # Streaming loop
+    try:
+        while True:
+            # Get frameset of color and depth
+            frames = pipeline.wait_for_frames()
+            # frames.get_depth_frame() is a 640x360 depth image
+
+            # Align the depth frame to color frame
+            aligned_frames = align.process(frames)
+
+            # Get aligned frames
+            aligned_depth_frame = aligned_frames.get_depth_frame() # aligned_depth_frame is a 640x480 depth image
+            color_frame = aligned_frames.get_color_frame()
+
+            # Validate that both frames are valid
+            if not aligned_depth_frame or not color_frame:
+                continue
+
+            depth_image = np.asanyarray(aligned_depth_frame.get_data())
+            color_image = np.asanyarray(color_frame.get_data())
+
+            # Remove background - Set pixels further than clipping_distance to grey
+            grey_color = 153
+            depth_image_3d = np.dstack((depth_image,depth_image,depth_image)) #depth image is 1 channel, color is 3 channels
+            bg_removed = np.where((depth_image_3d > clipping_distance) | (depth_image_3d <= 0), grey_color, color_image)
+
+            # Render images:
+            #   depth align to color on left
+            #   depth on right
+            depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
+            images = np.hstack((bg_removed, depth_colormap))
+
+            cv2.namedWindow('Align Example', cv2.WINDOW_NORMAL)
+            cv2.imshow('Align Example', images)
+            key = cv2.waitKey(1)
+            # Press esc or 'q' to close the image window
+            if key & 0xFF == ord('q') or key == 27:
+                cv2.destroyAllWindows()
+                break
+    finally:
+        pipeline.stop()
+
+if __name__ == "__main__":
+    main()

wrappers/python/examples/export_ply_example.py

@@ -8,41 +8,44 @@
 # First import the library
 import pyrealsense2 as rs
 
-
-# Declare pointcloud object, for calculating pointclouds and texture mappings
-pc = rs.pointcloud()
-# We want the points object to be persistent so we can display the last cloud when a frame drops
-points = rs.points()
-
-# Declare RealSense pipeline, encapsulating the actual device and sensors
-pipe = rs.pipeline()
-config = rs.config()
-# Enable depth stream
-config.enable_stream(rs.stream.depth)
-
-# Start streaming with chosen configuration
-pipe.start(config)
-
-# We'll use the colorizer to generate texture for our PLY
-# (alternatively, texture can be obtained from color or infrared stream)
-colorizer = rs.colorizer()
-
-try:
-    # Wait for the next set of frames from the camera
-    frames = pipe.wait_for_frames()
-    colorized = colorizer.process(frames)
-
-    # Create save_to_ply object
-    ply = rs.save_to_ply("1.ply")
-
-    # Set options to the desired values
-    # In this example we'll generate a textual PLY with normals (mesh is already created by default)
-    ply.set_option(rs.save_to_ply.option_ply_binary, False)
-    ply.set_option(rs.save_to_ply.option_ply_normals, True)
-
-    print("Saving to 1.ply...")
-    # Apply the processing block to the frameset which contains the depth frame and the texture
-    ply.process(colorized)
-    print("Done")
-finally:
-    pipe.stop()
+def main():
+    # Declare pointcloud object, for calculating pointclouds and texture mappings
+    pc = rs.pointcloud()
+    # We want the points object to be persistent so we can display the last cloud when a frame drops
+    points = rs.points()
+
+    # Declare RealSense pipeline, encapsulating the actual device and sensors
+    pipe = rs.pipeline()
+    config = rs.config()
+    # Enable depth stream
+    config.enable_stream(rs.stream.depth)
+
+    # Start streaming with chosen configuration
+    pipe.start(config)
+
+    # We'll use the colorizer to generate texture for our PLY
+    # (alternatively, texture can be obtained from color or infrared stream)
+    colorizer = rs.colorizer()
+
+    try:
+        # Wait for the next set of frames from the camera
+        frames = pipe.wait_for_frames()
+        colorized = colorizer.process(frames)
+
+        # Create save_to_ply object
+        ply = rs.save_to_ply("1.ply")
+
+        # Set options to the desired values
+        # In this example we'll generate a textual PLY with normals (mesh is already created by default)
+        ply.set_option(rs.save_to_ply.option_ply_binary, False)
+        ply.set_option(rs.save_to_ply.option_ply_normals, True)
+
+        print("Saving to 1.ply...")
+        # Apply the processing block to the frameset which contains the depth frame and the texture
+        ply.process(colorized)
+        print("Done")
+    finally:
+        pipe.stop()
+
+if __name__ == "__main__":
+    main()

wrappers/python/examples/opencv_viewer_example.py

@@ -9,64 +9,68 @@
 import numpy as np
 import cv2
 
-# Configure depth and color streams
-pipeline = rs.pipeline()
-config = rs.config()
-
-# Get device product line for setting a supporting resolution
-pipeline_wrapper = rs.pipeline_wrapper(pipeline)
-pipeline_profile = config.resolve(pipeline_wrapper)
-device = pipeline_profile.get_device()
-device_product_line = str(device.get_info(rs.camera_info.product_line))
-
-found_rgb = False
-for s in device.sensors:
-    if s.get_info(rs.camera_info.name) == 'RGB Camera':
-        found_rgb = True
-        break
-if not found_rgb:
-    print("The demo requires Depth camera with Color sensor")
-    exit(0)
-
-config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
-config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
-
-# Start streaming
-pipeline.start(config)
-
-try:
-    while True:
-
-        # Wait for a coherent pair of frames: depth and color
-        frames = pipeline.wait_for_frames()
-        depth_frame = frames.get_depth_frame()
-        color_frame = frames.get_color_frame()
-        if not depth_frame or not color_frame:
-            continue
-
-        # Convert images to numpy arrays
-        depth_image = np.asanyarray(depth_frame.get_data())
-        color_image = np.asanyarray(color_frame.get_data())
-
-        # Apply colormap on depth image (image must be converted to 8-bit per pixel first)
-        depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
-
-        depth_colormap_dim = depth_colormap.shape
-        color_colormap_dim = color_image.shape
-
-        # If depth and color resolutions are different, resize color image to match depth image for display
-        if depth_colormap_dim != color_colormap_dim:
-            resized_color_image = cv2.resize(color_image, dsize=(depth_colormap_dim[1], depth_colormap_dim[0]), interpolation=cv2.INTER_AREA)
-            images = np.hstack((resized_color_image, depth_colormap))
-        else:
-            images = np.hstack((color_image, depth_colormap))
-
-        # Show images
-        cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)
-        cv2.imshow('RealSense', images)
-        cv2.waitKey(1)
-
-finally:
-
-    # Stop streaming
-    pipeline.stop()
+def main():
+    # Configure depth and color streams
+    pipeline = rs.pipeline()
+    config = rs.config()
+
+    # Get device product line for setting a supporting resolution
+    pipeline_wrapper = rs.pipeline_wrapper(pipeline)
+    pipeline_profile = config.resolve(pipeline_wrapper)
+    device = pipeline_profile.get_device()
+    device_product_line = str(device.get_info(rs.camera_info.product_line))
+
+    found_rgb = False
+    for s in device.sensors:
+        if s.get_info(rs.camera_info.name) == 'RGB Camera':
+            found_rgb = True
+            break
+    if not found_rgb:
+        print("The demo requires Depth camera with Color sensor")
+        exit(0)
+
+    config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
+    config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
+
+    # Start streaming
+    pipeline.start(config)
+
+    try:
+        while True:
+
+            # Wait for a coherent pair of frames: depth and color
+            frames = pipeline.wait_for_frames()
+            depth_frame = frames.get_depth_frame()
+            color_frame = frames.get_color_frame()
+            if not depth_frame or not color_frame:
+                continue
+
+            # Convert images to numpy arrays
+            depth_image = np.asanyarray(depth_frame.get_data())
+            color_image = np.asanyarray(color_frame.get_data())
+
+            # Apply colormap on depth image (image must be converted to 8-bit per pixel first)
+            depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
+
+            depth_colormap_dim = depth_colormap.shape
+            color_colormap_dim = color_image.shape
+
+            # If depth and color resolutions are different, resize color image to match depth image for display
+            if depth_colormap_dim != color_colormap_dim:
+                resized_color_image = cv2.resize(color_image, dsize=(depth_colormap_dim[1], depth_colormap_dim[0]), interpolation=cv2.INTER_AREA)
+                images = np.hstack((resized_color_image, depth_colormap))
+            else:
+                images = np.hstack((color_image, depth_colormap))
+
+            # Show images
+            cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)
+            cv2.imshow('RealSense', images)
+            cv2.waitKey(1)
+
+    finally:
+
+        # Stop streaming
+        pipeline.stop()
+
+if __name__ == "__main__":
+    main()