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lidar_dataset/calib.json

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+{"h": 480, "w": 640, "left_to_right_pose": [[0.99988914, 0.01471834, 0.00224488, -59.940565765], [-0.01477408, 0.99951947, 0.02724975, -16.198770165], [-0.00184273, -0.02727989, 0.99962616, -1.066967884], [0.0, 0.0, 0.0, 1.0]], "lidar_to_right_cam": [[0.9996945715038775, 0.006139268119090391, 0.023938945102920287, -0.37787606], [-0.015773836557348174, 0.9041927865274832, 0.4268332120066656, 1.125335], [-0.019024977948496474, -0.42708045398803607, 0.9040134600963817, 1.48952085], [0.0, 0.0, 0.0, 1.0]], "left_intrinsic": [[530.47003, 0.0, 320.0], [0.0, 530.47003, 240.0], [0.0, 0.0, 1.0]], "right_intrinsic": [[530.47003, 0.0, 320.0], [0.0, 530.47003, 240.0], [0.0, 0.0, 1.0]], "right_cam_to_world": [[0.9985752056108484, 0.048344787054235255, -0.022590712780536905, 0.0], [-0.053362521859935244, 0.9046781147189629, -0.4227409963669141, 29.3], [1.734723475976807e-18, 0.4233441747718091, 0.9059689341731185, 0.0], [0.0, 0.0, 0.0, 1.0]], "left_cam_to_world": [[0.9977918823961943, 0.06363519748513709, -0.0190232025998682, -60.61418531236938], [-0.0659433954156338, 0.9149903098838849, -0.3980504988743422, 18.294957963667375], [-0.007923976839681518, 0.3984260123278461, 0.9171662696732554, -7.824294744484554], [0.0, 0.0, 0.0, 1.0]]}

lidar_dataset/read_data.py

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+# This example loads a stereo pair together with LiDAR points associated with the stereo pair at that time instance
+# The LiDAR data is returned in the following form
+# 'lidar_points' is a list of 'lidar_points'
+# where 'lidar_point' is a dictionary containing:
+# 'backscatter': the backscatter profile scanned by the LiDAR
+# 'azi': the azimuthal angle of the scanned ray
+# 'elev': the elevation angle of the scanned ray
+# In addition, if a cloud is detected from the backscatter, then 'lidar_point' also contains:
+# 'lidar_depth': the depth from the LiDAR to the cloud
+# 'right_cam_xy': the xy coordinates of the cloud in the right image
+# 'right_cam_depth': the depth of the cloud to the right camera
+
+from PIL import Image
+import numpy as np
+import cv2
+import json
+import glob
+from collections import defaultdict
+
+def main():
+    # Choose input images to load
+    date = '2021-10-22'
+    hour = 12
+    frame_idx = 50
+    
+    # Load metadata
+    with open(f'calib.json', 'r') as fp:
+        meta = json.load(fp)
+
+    h, w = meta['h'], meta['w']
+    right_intrinsic = np.array(meta['right_intrinsic'])
+    lidar_to_right = np.array(meta['lidar_to_right_cam'])
+    
+    # Unused metadata in this example
+    # right_cam2world = np.array(meta['right_cam2world'])
+    # left_intrinsic = np.array(meta['left_intrinsic'])
+    # left_cam2world = np.array(meta['left_cam2world'])
+    # left_to_right = np.array(meta['left_to_right_pose'])
+
+
+    # Load LiDAR data associated with this time
+    lidar_files = glob.glob(f'lidar/{date}/{hour:0>2}*.hpl')
+    lidar_data = load_lidar_data(date, hour, right_intrinsic, lidar_to_right)
+
+    # ---------- Load Data --------- #
+    left_video = cv2.VideoCapture(f'left_images/{date}/{date}_{hour:0>2}.mp4')
+    right_video = cv2.VideoCapture(f'right_images/{date}/{date}_{hour:0>2}.mp4')
+    
+    for _ in range(frame_idx):
+        left_video.read()
+        right_video.read()
+    
+    _, left_image = left_video.read()
+    _, right_image = right_video.read()
+    
+    # BGR To RGB
+    left_image = cv2.cvtColor(left_image, cv2.COLOR_BGR2RGB).astype('uint8')
+    right_image = cv2.cvtColor(right_image, cv2.COLOR_BGR2RGB).astype('uint8')
+    
+    # Ensure images match the expected size
+    left_image = cv2.resize(left_image, (w, h))
+    right_image = cv2.resize(left_image, (w, h))
+    
+    # See top of file for the format of 'lidar_points'
+    lidar_points = lidar_data[frame_idx]
+
+    return left_image, right_image, lidar_points
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+
+
+################ Define some helper functions ################
+def load_lidar_data(date, hour, right_intrinsic, lidar_to_right):
+    def flattenList(xss):
+        return [x for xs in xss for x in xs]
+    
+    decimal_time = []
+    azi = []
+    elev = []
+    distance = []
+    backscatter = []
+    
+    lidar_files = glob.glob(f'lidar/{date}/{date}_{hour:0>2}*.hpl')
+
+    for lidar_file in lidar_files:
+        ld = LidarData.fromfile(lidar_file)
+        
+        data_locs = ld.data_locs
+        decimal_time.append(list(data_locs[:,0]))
+        azi.append(list(data_locs[:,1]))
+        elev.append(list(data_locs[:,2]))
+        distance.append(list(ld.getDistance()))
+        backscatter.append(list(ld.getBackscatter()))
+    
+    decimal_time = np.array(flattenList(decimal_time), ndmin=1)
+    azi = np.array(flattenList(azi), ndmin=1)
+    elev = np.array(flattenList(elev), ndmin=1)
+    distance = np.array(flattenList(distance), ndmin=1)
+    backscatter = np.array(flattenList(backscatter), ndmin=1)
+    
+    # Go through all frames now
+    camera_time = hour + 10/3600  # First frame starts 10 seconds into the hour
+    
+    lidar_output = defaultdict(list)
+
+    # Simple unoptimised code for LiDAR loading
+    for frame_idx in range(717):  # 717 frames per video
+        
+        for time in decimal_time:
+            
+            if np.abs(time-camera_time) < 2.5/3600:  # Associate each LiDAR point with the closest frame (1 frame per 5 seconds)
+                i = list(decimal_time).index(time)
+
+                # Check if there is a cloud
+                _, cloud_depth = findCloud_in_backscatter(backscatter[i], int(500 / 3))  
+
+                if cloud_depth is not None:
+                    # Project lidar to right camera
+                    lidar_right_xy, lidar_right_depth = project_lidar_to_right(lidar_to_right, right_intrinsic, azi[i],
+                                                                            elev[i],
+                                                                            cloud_depth)
+
+                    
+                    lidar_output[frame_idx].append({'lidar_depth': cloud_depth, 'azi': azi[i], 'elev': elev[i],
+                                                    'right_cam_xy': lidar_right_xy, 'right_cam_depth': lidar_right_depth,
+                                                    'backscatter': backscatter[i]})
+                else:
+                    lidar_output[frame_idx].append({'azi': azi[i], 'elev': elev[i], 'backscatter': backscatter[i]})
+    
+        camera_time += 5/3600
+
+    return lidar_output
+    
+class LidarData():
+    def __init__(self,
+                 fname=None,
+                 system_id=None,
+                 num_gates=0,
+                 gate_length=0,
+                 gate_pulse_length=0,
+                 pulses_per_ray=0,
+                 start_time=None,
+                 data=None,
+                 data_locs=None):
+        self.fname = fname
+        self.system_id = system_id
+        self.num_gates = num_gates
+        self.gate_length = gate_length
+        self.gate_pulse_length = gate_pulse_length
+        self.pulses_per_ray = pulses_per_ray
+        self.start_time = start_time
+        self.data = data
+        self.data_locs = data_locs
+
+    @classmethod
+    def fromfile(cls, filename):
+        with open(filename) as f:
+            header = [f.readline().split(':', maxsplit=1) for i in range(17)]
+            
+            fname = header[0][1].strip()
+            system_id = header[1][1].strip()
+            num_gates = int(header[2][1].strip())
+            gate_length = header[3][1].strip()
+            gate_pulse_length = header[4][1].strip()
+            pulses_per_ray = header[5][1].strip()
+            start_time = header[9][1].strip()
+            data_locs_format = header[13][0].split(' ')[0].strip()
+            data_format = header[15][0].split(' ')[0].strip()
+
+            data = []
+            data_locs = []
+            while True:
+                try:
+                    data_locs_in = np.array(f.readline().split()).astype('float')
+                    if len(data_locs_in) == 0:
+                        break
+                    data_locs.append(data_locs_in)
+                    data.append(np.array(
+                        [f.readline().split() for i in range(num_gates)]).astype('float'))
+                except:
+                    break
+            data = np.array(data)
+            data_locs = np.array(data_locs)
+
+            return cls(
+                 fname=fname,
+                 system_id=system_id,
+                 num_gates=num_gates,
+                 gate_length=gate_length,
+                 gate_pulse_length=gate_pulse_length,
+                 pulses_per_ray=pulses_per_ray,
+                 start_time=start_time,
+                 data=data,
+                 data_locs=data_locs)
+        
+    # starting all these at 20 means we avoid the peak at zero distance    
+    def getDistance(self):
+        return self.data[:,20:,0]*3 #multiply by 3 to get distance in m
+    
+    def getDoppler(self):
+         return self.data[:,20:,1]
+    
+    def getBackscatter(self):
+         return self.data[:,20:,2]
+     
+    def getBeta(self):
+         return self.data[:,20:,3]
+
+
+def project_lidar_to_right(lidar_to_right_cam, right_intrinsic, azi, elev, depth):
+    # Convert from azi, elev, depth to lidar xyz
+    azi = -azi + 270
+    x = depth * np.cos(elev * np.pi / 180) * np.cos(azi * np.pi / 180)
+    z = depth * np.cos(elev * np.pi / 180) * np.sin(azi * np.pi / 180)
+    y = -depth * np.sin(elev * np.pi / 180) 
+    
+    lidar_xyz = np.stack((x, y, z), axis=0)
+
+    # Go from lidar coordinate system to right camera coordinate system
+    desired_shape = list(lidar_xyz.shape)
+    desired_shape[0] = 1
+    cam_xyz = lidar_to_right_cam @ np.concatenate((lidar_xyz, np.ones(desired_shape)), axis=0)
+
+    # Note: here you can also use the left_to_right_pose to project onto the left camera
+
+    # Project onto right camera
+    projected_lidar = cam_xyz[:3] / cam_xyz[2]
+    projected_lidar = right_intrinsic @ projected_lidar
+    projected_lidar = projected_lidar[:2]
+
+    # Get depth
+    right_cam_depth = np.sqrt(np.sum(cam_xyz ** 2, axis=0))
+    
+    return projected_lidar, right_cam_depth
+
+def findCloud_in_backscatter(backscatter, dist_thresh=300):
+    if np.max(backscatter) > 10:
+        # Building reflection
+        return (None, None)
+    
+    cloud = np.argmax(backscatter[dist_thresh:])
+    if backscatter[cloud+dist_thresh] - np.median(backscatter[dist_thresh:]) > 0.03: 
+        # Found cloud
+        return cloud+dist_thresh, (cloud+dist_thresh+20)*3 #cloud index, cloud distance
+    else:
+        # No cloud found
+        return (None, None)
+    
+##############################################################
+
+
+
+if __name__ == "__main__":
+    left_image, right_image, lidar_points = main()

synthetic_dataset/read_data.py

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+from PIL import Image
+import numpy as np
+import cv2
+import json
+
+# Choose split (train/test)
+split = "train" 
+
+# Choose sample number
+sample = 123
+
+# ------ Camera Parameters ------ #
+def get_camera_intrinsics(fov_degrees, resolution):
+    fov_rads = fov_degrees * np.pi / 180
+    left_focal_length = resolution / (np.tan(fov_rads / 2) * 2)
+    right_focal_length = resolution / (np.tan(fov_rads / 2) * 2)
+
+    left_intrinsic = np.array([[left_focal_length, 0.000000000000000000e+00, 256],
+                                [0.000000000000000000e+00,left_focal_length, 256],
+                                [0.000000000000000000e+00,0.000000000000000000e+00,1.000000000000000000e+00]])
+
+    right_intrinsic = np.array([[right_focal_length, 0.000000000000000000e+00, 256],
+                                [0.000000000000000000e+00,right_focal_length, 256],
+                                [0.000000000000000000e+00,0.000000000000000000e+00,1.000000000000000000e+00]])
+    
+    return left_intrinsic, right_intrinsic
+
+with open(f'{split}.json', 'r') as fp:
+    meta = json.load(fp)
+    
+h, w = meta['h'], meta['w']
+curr_frame = meta['frames'][sample]
+fov_degrees = curr_frame['fov_x_y']
+
+left_intrinsic, right_intrinsic = get_camera_intrinsics(fov_degrees, h)
+
+# ---------- Load Data --------- #
+left_img = np.array(Image.open(curr_frame['left_image_path']))
+right_img = np.array(Image.open(curr_frame['right_image_path']))
+disparity = cv2.imread(curr_frame['disparity_path'], cv2.IMREAD_UNCHANGED | cv2.IMREAD_ANYDEPTH)
+opacity = cv2.imread(curr_frame['opacity_path'], cv2.IMREAD_UNCHANGED | cv2.IMREAD_ANYDEPTH)

synthetic_dataset/test.json

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+{"h": 512, "w": 512, "frames": [{"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/side_0010.tif", "right_image_path": "./right_images/side_0010.tif", "disparity_path": "./disparities/side_0010.pfm", "opacity_path": "./opacities/side_0010.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/top_0010.tif", "right_image_path": "./right_images/top_0010.tif", "disparity_path": "./disparities/top_0010.pfm", "opacity_path": "./opacities/top_0010.pfm"}, {"baseline": 80, "fov_x_y": 62.2, "left_image_path": "./left_images/diag_0010.tif", "right_image_path": "./right_images/diag_0010.tif", "disparity_path": "./disparities/diag_0010.pfm", "opacity_path": "./opacities/diag_0010.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/side_0020.tif", "right_image_path": "./right_images/side_0020.tif", "disparity_path": "./disparities/side_0020.pfm", "opacity_path": "./opacities/side_0020.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/top_0020.tif", "right_image_path": "./right_images/top_0020.tif", "disparity_path": "./disparities/top_0020.pfm", "opacity_path": "./opacities/top_0020.pfm"}, {"baseline": 80, "fov_x_y": 62.2, "left_image_path": "./left_images/diag_0020.tif", "right_image_path": "./right_images/diag_0020.tif", "disparity_path": "./disparities/diag_0020.pfm", "opacity_path": "./opacities/diag_0020.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/side_0030.tif", "right_image_path": "./right_images/side_0030.tif", "disparity_path": "./disparities/side_0030.pfm", "opacity_path": "./opacities/side_0030.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/top_0030.tif", "right_image_path": "./right_images/top_0030.tif", "disparity_path": "./disparities/top_0030.pfm", "opacity_path": "./opacities/top_0030.pfm"}, {"baseline": 80, "fov_x_y": 62.2, "left_image_path": "./left_images/diag_0030.tif", "right_image_path": "./right_images/diag_0030.tif", "disparity_path": "./disparities/diag_0030.pfm", "opacity_path": "./opacities/diag_0030.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/side_0040.tif", "right_image_path": "./right_images/side_0040.tif", "disparity_path": "./disparities/side_0040.pfm", "opacity_path": "./opacities/side_0040.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/top_0040.tif", "right_image_path": "./right_images/top_0040.tif", "disparity_path": "./disparities/top_0040.pfm", "opacity_path": "./opacities/top_0040.pfm"}, {"baseline": 80, "fov_x_y": 62.2, "left_image_path": "./left_images/diag_0040.tif", "right_image_path": "./right_images/diag_0040.tif", "disparity_path": "./disparities/diag_0040.pfm", "opacity_path": "./opacities/diag_0040.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/side_0050.tif", "right_image_path": "./right_images/side_0050.tif", "disparity_path": "./disparities/side_0050.pfm", "opacity_path": "./opacities/side_0050.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/top_0050.tif", "right_image_path": "./right_images/top_0050.tif", "disparity_path": "./disparities/top_0050.pfm", "opacity_path": "./opacities/top_0050.pfm"}, {"baseline": 80, "fov_x_y": 62.2, "left_image_path": "./left_images/diag_0050.tif", "right_image_path": "./right_images/diag_0050.tif", "disparity_path": "./disparities/diag_0050.pfm", "opacity_path": "./opacities/diag_0050.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/side_0060.tif", "right_image_path": "./right_images/side_0060.tif", "disparity_path": "./disparities/side_0060.pfm", "opacity_path": "./opacities/side_0060.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/top_0060.tif", "right_image_path": "./right_images/top_0060.tif", "disparity_path": "./disparities/top_0060.pfm", "opacity_path": "./opacities/top_0060.pfm"}, {"baseline": 80, "fov_x_y": 62.2, "left_image_path": "./left_images/diag_0060.tif", "right_image_path": "./right_images/diag_0060.tif", "disparity_path": "./disparities/diag_0060.pfm", "opacity_path": "./opacities/diag_0060.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/side_0070.tif", "right_image_path": "./right_images/side_0070.tif", "disparity_path": "./disparities/side_0070.pfm", "opacity_path": "./opacities/side_0070.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/top_0070.tif", "right_image_path": "./right_images/top_0070.tif", "disparity_path": "./disparities/top_0070.pfm", "opacity_path": "./opacities/top_0070.pfm"}, {"baseline": 80, "fov_x_y": 62.2, "left_image_path": "./left_images/diag_0070.tif", "right_image_path": "./right_images/diag_0070.tif", "disparity_path": "./disparities/diag_0070.pfm", "opacity_path": "./opacities/diag_0070.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/side_0080.tif", "right_image_path": "./right_images/side_0080.tif", "disparity_path": "./disparities/side_0080.pfm", "opacity_path": "./opacities/side_0080.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/top_0080.tif", "right_image_path": "./right_images/top_0080.tif", "disparity_path": "./disparities/top_0080.pfm", "opacity_path": "./opacities/top_0080.pfm"}, {"baseline": 80, "fov_x_y": 62.2, "left_image_path": "./left_images/diag_0080.tif", "right_image_path": "./right_images/diag_0080.tif", "disparity_path": "./disparities/diag_0080.pfm", "opacity_path": "./opacities/diag_0080.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/side_0090.tif", "right_image_path": "./right_images/side_0090.tif", "disparity_path": "./disparities/side_0090.pfm", "opacity_path": "./opacities/side_0090.pfm"}, {"baseline": 152, "fov_x_y": 90, "left_image_path": "./left_images/top_0090.tif", "right_image_path": "./right_images/top_0090.tif", "disparity_path": "./disparities/top_0090.pfm", 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