added codes

codes/.gitignore

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+analysis/
+data/
+color_palatte.png
+azure kinect color.png
+data.zip
+**/__pycache__/
+zi0vrcRW
+ziVuEiKI
+zinxpZy7
+analysis2/
+main*
+_minted-main/
+images/
+../360-alignment/
+../dummy.png
+../experiments/test.py
+../test.png
+../test_realsense.png
+../k4a-hdr-imaging/
+stat/__pycache__/

codes/config/azurekinect.json

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+{
+	"camera_fps" : "K4A_FRAMES_PER_SECOND_30",
+	"color_format" : "K4A_IMAGE_FORMAT_COLOR_MJPG",
+	"color_resolution" : "K4A_COLOR_RESOLUTION_720P",
+	"depth_delay_off_color_usec" : "0",
+	"depth_mode" : "K4A_DEPTH_MODE_NFOV_UNBINNED",
+	"disable_streaming_indicator" : "false",
+	"subordinate_delay_off_master_usec" : "0",
+	"synchronized_images_only" : "false",
+	"wired_sync_mode" : "K4A_WIRED_SYNC_MODE_STANDALONE"
+}

codes/config/default_config.json

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+{
+	"camera_fps" : "K4A_FRAMES_PER_SECOND_30",
+	"color_format" : "K4A_IMAGE_FORMAT_COLOR_MJPG",
+	"color_resolution" : "K4A_COLOR_RESOLUTION_720P",
+	"depth_delay_off_color_usec" : "0",
+	"depth_mode" : "K4A_DEPTH_MODE_NFOV_2X2BINNED",
+	"disable_streaming_indicator" : "false",
+	"subordinate_delay_off_master_usec" : "0",
+	"synchronized_images_only" : "false",
+	"wired_sync_mode" : "K4A_WIRED_SYNC_MODE_STANDALONE",
+	"enable_auto_exposure": "false",
+	"exposure_time_us": 5
+}

codes/config/realsense.json

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+{
+    "name": "Realsense bag file",
+    "path_dataset": "dataset/realsense.bag",
+    "path_intrinsic": "optional/read_from_bag_file.json",
+    "depth_max": 3.0,
+    "voxel_size": 0.05,
+    "depth_diff_max": 0.07,
+    "preference_loop_closure_odometry": 0.1,
+    "preference_loop_closure_registration": 5.0,
+    "tsdf_cubic_size": 3.0,
+    "icp_method": "color",
+    "global_registration": "ransac",
+    "python_multi_threading": true,
+    "resolution": [1280, 720],
+    "camera_fps": 30
+}

codes/experiments/ambiance.py

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+import numpy as np 
+import argparse
+# import matplotlib
+# matplotlib.use('TkAgg')
+import matplotlib.pyplot as plt
+from libs.experiments import Experiments
+from libs.visualization import Visualization
+
+def subplots(data_list, title, camera, label_list, path, heatmap=True):
+    num_rows, num_cols = Visualization.get_grid(len(data_list))
+    print(f"Rows: {num_rows}, Columns: {num_cols}")
+    fig = plt.figure(constrained_layout=True)
+    fig.suptitle('Per-Pixel Mean Distance Distribution')
+    subfigs = fig.subfigures(nrows=num_rows, ncols=1)
+
+    for row, subfig in enumerate(subfigs):
+        subfig.suptitle(f"({camera[row]})", fontsize=12)
+        axs = subfig.subplots(nrows=1, ncols=num_cols)
+        for col, ax in enumerate(axs):
+            if heatmap:
+                im = data_list[col+len(axs)*row].noise_visualization(ax=ax)
+                cbar = fig.colorbar(im, ax=ax,label=f"distance ({label_list[row]})")
+                cbar.ax.tick_params(labelsize=8)
+            else:
+                Visualization.temporal_depth_variation(data_list[i])
+            if row==0:
+                ax.set_title(title[col])      
+    if path is not None:
+        plt.savefig(path)
+    plt.show()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+            description=
+            "Realsense Recorder. Please select one of the optional arguments")
+    parser.add_argument("--input",
+                        required=False,
+                        nargs = '+',
+                        help="set path to input color and depth directory. Give color path as 1st argument")
+    parser.add_argument("--npy",
+                        required=False,
+                        nargs = '*',
+                        help="npy filename to export data or npy path to load data")
+    parser.add_argument("--export_depth",
+                        action='store_true',
+                        help="option to export depth data after selection ROIs")
+    parser.add_argument("--fig_path",
+                        required=False,
+                        help="path to save plots")
+
+    args = parser.parse_args()
+
+    obj_list = []
+    # title_list = ["Indoor Ambient", "Indoor Controlled", "Outdoor Shadow", "Outdoor Sunlight"]
+    title_list = ["Controlled Environment", "Uncontrolled Environment"]
+    if len(args.npy)>1:
+        for i,file_name in enumerate(args.npy):
+            data=np.load(file_name)
+            if i<len(title_list):   #pass RealSense .npy files first then AzureKinect
+                data=(data+0.0082)*1000
+            obj_list.append(Visualization(data,data.shape[1],data.shape[2]))
+        # print(len(obj_list))
+        subplots(obj_list, title_list, camera=["RealSense","AzureKinect"],label_list=['mm','mm'], path=args.fig_path)
+
+    else:
+        ROI_coordinates = None
+        experiment5 = Experiments(args.input, args.export_depth, args.npy)
+        if len(args.input)>1:
+            ROI_coordinates, depth_data = experiment5.get_depth_data()
+            print(f'depth_data.shape = {depth_data.shape}, ROI: {ROI_coordinates}')
+        else:
+            ROI_coordinates, _ = experiment5.get_depth_data()
+            print(f'Starting and ending coordinates of ROI: {ROI_coordinates}')

codes/experiments/camera_distance.py

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+import numpy as np 
+import argparse
+import matplotlib
+matplotlib.use('TkAgg')
+import matplotlib.pyplot as plt
+import sys
+sys.path.append(".")
+import os
+from libs.experiments import Experiments
+from libs.visualization import Visualization
+from libs.type_convert import convert_float64_to_32
+
+def plots(data_list, title_list, suptitle, camera, path, label_list, heatmap, warmup, offset, check_distribution):
+    if offset:
+        Visualization.subplots(data_list, title_list[1], suptitle[1], camera, path, label_list, heatmap, offset)
+        return
+    if warmup:
+        Visualization.subplots(data_list, title_list[0], suptitle[0], camera, path, label_list, heatmap)
+    else:
+        Visualization.subplots(data_list, title_list[1], suptitle[1], camera, path, label_list, heatmap, offset, check_distribution)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+            description=
+            "Realsense Recorder. Please select one of the optional arguments")
+    parser.add_argument("--input",
+                        required=False,
+                        nargs = '+',
+                        help="set path to input color and depth directory. Give color path as 1st argument")
+    parser.add_argument("--npy",
+                        required=False,
+                        nargs = '*',
+                        help="npy filename to export data or npy path to load data")
+    parser.add_argument("--export_depth",
+                        action='store_true',
+                        help="option to export depth data after selection ROIs")
+    parser.add_argument("--camera_id",
+                        type=int,
+                        help="RealSense = 0 or AzureKinect = 1")
+    parser.add_argument("--fig_path",
+                        required=False,
+                        help="path to save plots")
+
+    args = parser.parse_args()
+
+    object_list = []
+    data_list = []
+    if len(args.npy)>1:
+        for i, file_name in enumerate(args.npy):
+            print(f'Loading {file_name}')
+            data=np.load(file_name)
+            convert_float64_to_32(data, out_dir=os.path.dirname(file_name), idx=[args.camera_id, i])
+            if args.camera_id==0:
+                data = (data+0.0082)*1000 #for RealSense
+
+            object_list.append(Visualization(data,[50,50],[100,100]))
+            data_list.append(data)
+        obj_list = [object_list, data_list]
+        title_list = [["Before Warmup", "After Warmup"], ["0.5m", "1m", "1.5m", "2m", "2.5m"]]
+        suptitles = ["Effect of Warmup Time", "Effect of Camera Distance"]
+        camera=["RealSense","AzureKinect"]
+        label_list=['mm','mm']  #Replace mm by m in case metric required as meters.
+
+        plots(obj_list, title_list, suptitles, camera=camera[args.camera_id], path=args.fig_path, label_list=label_list[args.camera_id], heatmap=False, warmup=False, offset=False, check_distribution=False)
+
+    else:
+        ROI_coordinates = None
+        experiment1 = Experiments(args.input, args.export_depth, args.npy)
+        if len(args.input)>1:
+            ROI_coordinates, depth_data = experiment1.get_depth_data()
+            print(f'depth_data.shape = {depth_data.shape}, ROI: {ROI_coordinates}')
+        else:
+            ROI_coordinates, _ = experiment1.get_depth_data()
+            print(f'Starting and ending coordinates of ROI: {ROI_coordinates}')

codes/experiments/object_color.py

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+import numpy as np
+import argparse 
+import os
+import matplotlib.pyplot as plt
+from libs.experiments import Experiments
+from libs.visualization import Visualization
+
+def plots(obj_list, title_list, suptitle, camera, path, label_list, heatmap=True):
+    if camera=="AzureKinect":
+        Visualization.subplots(obj_list, title_list[::-1], suptitle, camera, path, label_list, heatmap)
+    else:
+        Visualization.subplots(obj_list, title_list, suptitle, camera, path, label_list, heatmap)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+            description=
+            "Realsense Recorder. Please select one of the optional arguments")
+    parser.add_argument("--input",
+                        required=False,
+                        nargs = '+',
+                        help="set path to input color and depth directory. Give color path as 1st argument")
+    parser.add_argument("--npy",
+                        required=False,
+                        help="npy filename to export data or npy path to read data")
+    parser.add_argument("--export_depth",
+                        action='store_true',
+                        help="option to view bounding box")
+    parser.add_argument("--camera_id",
+                        type=int,
+                        required=False,
+                        help="RealSense = 0 or AzureKinect = 1")
+    parser.add_argument("--fig_path",
+                        required=False,
+                        help="path to save plots")
+
+    args = parser.parse_args()
+
+    ROI_coordinates = []
+    data = None
+    experiment3 = Experiments(args.input, args.export_depth, args.npy)
+    if len(args.input)>1:        
+        ROI_coordinates, depth_data = experiment3.get_depth_data()  # Depth values can be obtained from depth images only for Azure Kinect
+        print(f'depth_data.shape = {depth_data.shape}, ROI: {ROI_coordinates}')
+        path_splitted = os.path.split(args.input[1])
+        kinect_data_path = path_splitted[0]
+        data = np.load(os.path.join(kinect_data_path, args.npy))
+        if args.camera_id==0:
+            data = (data + (0.0084))*1000 #For RealSense
+
+    else:                       
+        ROI_coordinates, _ = experiment3.get_depth_data()   # For RealSense Camera, only ROI coordinates can be obtained from color images
+        print(f'Starting and ending coordinates of ROI: {ROI_coordinates}')
+        data = np.load(args.npy)
+        data = (data + (0.0084))*1000
+        # data = data+0.0084
+
+    scaled_roi = Experiments.crop_pallette(ROI_coordinates)
+    print(f"scaled ROI: {scaled_roi}")
+    object_list = []
+    data_list = []
+    for i, roi in enumerate(scaled_roi):
+        object_list.append(Visualization(data, roi[0], roi[1]))
+        data_list.append(data)
+
+    obj_list = [object_list, data_list]
+    textures_list = ["Blue", "Green", "Red", "Black", "Yellow", "White"]
+    camera=["RealSense","AzureKinect"]
+    label_list=['mm','mm']
+
+    plots(obj_list, title_list=textures_list, suptitle="Effect of Color (Absorptivity)", camera=camera[args.camera_id], path=args.fig_path, label_list=label_list[args.camera_id])

codes/experiments/object_motion.py

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+import numpy as np
+import argparse 
+import matplotlib
+matplotlib.use('TkAgg')
+import matplotlib.pyplot as plt
+import sys
+sys.path.append(".")
+from libs.experiments import Experiments
+from libs.visualization import Visualization
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+            description=
+            "Realsense Recorder. Please select one of the optional arguments")
+    parser.add_argument("--input",
+                        required=False,
+                        nargs = '+',
+                        help="set path to input color and depth directory. Give color path as 1st argument")
+    parser.add_argument("--npy",
+                        required=False,
+                        nargs="*",
+                        help="npy filename to export data or npy path to read data")
+    parser.add_argument("--export_depth",
+                        action='store_true',
+                        help="option to view bounding box")
+    parser.add_argument("--camera_id",
+                        type=int,
+                        help="RealSense = 0 or AzureKinect = 1")
+    parser.add_argument("--fig_path",
+                        required=False,
+                        help="path to save plots")
+
+    args = parser.parse_args()
+    object_list = [] 
+    data_list = []
+    if len(args.npy)>1:
+        for file_name in args.npy:
+            data=np.load(file_name)
+            if args.camera_id==0: #for RealSense
+                data = (data + (0.0084))*1000   #in case not added in realsense_recorder.py
+            object_list.append(Visualization(data,data.shape[1],data.shape[2]))
+            data_list.append(data)
+
+        obj_list = [object_list, data_list]
+        title_list = ["Static", "Motion"]
+        camera=["RealSense","AzureKinect"]
+        label_list='mm'
+        suptitle = "Effect of Motion"
+
+        Visualization.subplots(obj_list, title_list, suptitle, camera=camera[args.camera_id], path=None, label_list=label_list, heatmap=False)    
+    else:
+        ROI_coordinates = None
+        experiment2 = Experiments(args.input, args.export_depth, args.npy)
+        if len(args.input)>1:
+            ROI_coordinates, depth_data = experiment2.get_depth_data()
+            print(f'depth_data.shape = {depth_data.shape}, ROI: {ROI_coordinates}')
+        else:
+            ROI_coordinates, _ = experiment2.get_depth_data()
+            print(f'Starting and ending coordinates of ROI: {ROI_coordinates}')