Real depth working, disparity not currently feasible

scripts/Depth2Disparity/depth_to_disparity.py

@@ -109,73 +109,113 @@ class Depth2Disparity:
         abs_depth_map = a * rel_depth_map + b
         print("Applied transformation to entire depth map")
         
-        abs_depth_map = np.maximum(abs_depth_map, 0.01)
+        #abs_depth_map = np.maximum(abs_depth_map, 0.01)
         
-        #plt.imshow(abs_depth_map, cmap='gray')
-        #plt.colorbar()
-        #plt.show()
+        plt.imshow(abs_depth_map, cmap='gray')
+        plt.colorbar()
+        plt.show()
 
         # this should not be normalised, the values in the array should equate to literal distances
         return abs_depth_map
 
     def _depth2disparity(self, abs_depth_map):
-           
-                    # Get image dimensions
-             # Get image dimensions
-            height, width = abs_depth_map.shape
-
-            # Calculate step sizes
-            unit_w = 2.0 / width  # Longitude step size
-            unit_h = 1.0 / height  # Latitude step size
-
-            # Initialize disparity map
-            disparity_map = np.zeros_like(abs_depth_map, dtype=np.float32)
-
-            # Iterate over each pixel
-            for i in range(height):
-                # Calculate latitude angle for the row
-                theta_t = i * unit_h * np.pi
-
-                for j in range(width):
-                    # Retrieve the absolute depth (r_t) for this pixel
-                    r_t = abs_depth_map[i, j]
-
-                    # Avoid invalid or infinite depth values
-                    if r_t <= 0:
-                        disparity_map[i, j] = 0
-                        continue
-
-                    try:
-                        # Compute denominator for the formula
-                        epsilon = 1e-8  # Small value to prevent instability
-                        tan_theta_t = np.tan(theta_t) if np.abs(np.cos(theta_t)) > epsilon else np.sign(np.sin(theta_t)) * np.inf
-                        denominator = r_t * np.sin(theta_t) + r_t * np.cos(theta_t) * tan_theta_t
-
-                        # Avoid invalid denominator values
-                        if denominator <= 0:
-                            disparity_map[i, j] = 0
-                            continue
-
-                        # Calculate angular disparity
-                        angle_disp = np.arctan(self.baseline / denominator) - theta_t
-
-                        # Convert angular disparity to pixel disparity
-                        pixel_disp = (angle_disp / (unit_h * np.pi) - self.disp_offset) * self.disp_scale
-                        disparity_map[i, j] = pixel_disp
-
-                    except ZeroDivisionError:
-                        disparity_map[i, j] = 0
-
-            plt.imshow(disparity_map, cmap='gray')
-            plt.colorbar()
-            plt.show()
-
-            # Normalize disparity map for visualization
-            disparity_map_normalized = (disparity_map - disparity_map.min()) / (disparity_map.max() - disparity_map.min())
-            disparity_map_normalized *= 255
-            disparity_map_normalized = disparity_map_normalized.astype(np.uint8)
-            
-            return disparity_map, disparity_map_normalized
+        """
+        Convert absolute depth map to disparity map for 360-degree images.
+
+        Parameters:
+            abs_depth_map (numpy.ndarray): Absolute depth map with pixel values as distances in meters.
+
+        Returns:
+            disparity_map (numpy.ndarray): Disparity map with values representing disparity.
+            disparity_map_normalized (numpy.ndarray): Normalized disparity map for visualization.
+        """
+        # Define parameters for the conversion
+        height, width = abs_depth_map.shape
+        
+        # Latitude grid (θ_l)
+        epsilon = 1e-6
+        latitudes = np.linspace(-np.pi / 2, np.pi / 2, height)
+        latitudes_grid = np.tile(latitudes[:, np.newaxis], (1, width))
+
+        # Clamp extreme latitudes to avoid instability
+        latitudes_grid = np.clip(latitudes_grid, -np.pi/2 + epsilon, np.pi/2 - epsilon)
+
+        plt.imshow(latitudes_grid, cmap="jet")
+        plt.colorbar()
+        plt.title("Latitude Grid")
+        plt.show()
+
+        self.baseline = 0.176
+        
+        
+        # Calculate angular disparity Δθ(x, y)
+        # Δθ(x, y) = arctan(B / (r_t * sin(θ_l) + r_t * cos(θ_l) * tan(θ_l))) - θ_l        
+        epsilon = 1e-6  # Small value to prevent instability
+        denominator = (
+            abs_depth_map * np.sin(latitudes_grid) +
+            abs_depth_map * np.cos(latitudes_grid) * np.tan(latitudes_grid) +
+            epsilon
+        )
+        angular_disparity = np.arctan(self.baseline / denominator) - latitudes_grid
+        
+        self.disp_scale = 10
+        self.disp_offset = np.median(angular_disparity)
+        
+        # Normalize the disparity map for saving
+        pixel_angle_scale = 1  # Assume scale factor is 1 if not provided (can be adjusted if needed)
+        disparity_map = (angular_disparity / pixel_angle_scale - self.disp_offset) * self.disp_scale
+
+        # Normalize for visualization
+        disparity_map_normalized = (disparity_map - disparity_map.min()) / (disparity_map.max() - disparity_map.min())
+        disparity_map_normalized *= 255
+        disparity_map_normalized = disparity_map_normalized.astype(np.uint8)
+
+        # Debugging visualization
+        plt.imshow(disparity_map, cmap="jet")
+        plt.colorbar()
+        plt.title("Disparity Map (Debugging)")
+        plt.show()
+
+        return disparity_map, disparity_map_normalized
+
+    def __depth2disparity(self, abs_depth_map):
+        height, width = abs_depth_map.shape
+        unit_w = 2.0 / width  # Horizontal unit angle
+        unit_h = 1.0 / height  # Vertical unit angle
+        epsilon = 1e-8  # Small value to prevent instability
+
+        # Latitude angles (theta_t) for each row
+        theta_t_grid = np.linspace(0, np.pi, height).reshape(height, 1)
+
+        # Ensure no instability in tangent calculation
+        tan_theta_t = np.tan(np.clip(theta_t_grid, -np.pi/2 + epsilon, np.pi/2 - epsilon))
 
+        # Broadcast depth map and latitude grid
+        r_t = abs_depth_map  # Absolute depth values (meters)
+        sin_theta_t = np.sin(theta_t_grid)
+        cos_theta_t = np.cos(theta_t_grid)
+
+        # Compute denominator
+        denominator = r_t * sin_theta_t + r_t * cos_theta_t * tan_theta_t + epsilon
+
+        # Avoid invalid denominator values
+        denominator[denominator <= 0] = epsilon
+
+        # Calculate angular disparity
+        angle_disp = np.arctan(self.baseline / denominator) - theta_t_grid
+
+        # Convert angular disparity to pixel disparity
+        disparity_map = (angle_disp / (unit_h * np.pi) - self.disp_offset) * self.disp_scale
+
+        plt.imshow(disparity_map, cmap='gray')
+        plt.colorbar()
+        plt.show()
+
+        # Normalize disparity map for visualization
+        disparity_map_normalized = (disparity_map - disparity_map.min()) / (disparity_map.max() - disparity_map.min())
+        disparity_map_normalized *= 255
+        disparity_map_normalized = disparity_map_normalized.astype(np.uint8)
+        
+        return disparity_map, disparity_map_normalized
 
     
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