Update app.py

app.py

@@ -12,7 +12,7 @@ NUM_WORKERS = min(cpu_count(), 4)
 SHARED_MEMORY_NAME = 'shared_canvas'
 
 # === Parameters ===
-IMAGE_HEIGHT = 4096 * 2  # Default image height
+IMAGE_HEIGHT = 4096  # Default image height
 POINT_SIZE = 3
 SCALE_COLORS = False
 FULL_RES = False
@@ -20,16 +20,20 @@ MAX_RADIUS = 30
 
 
 def create_shared_memory_array(data, name, dtype):
+    print("[STEP 7] Creating shared memory...")
     shm = shared_memory.SharedMemory(create=True, size=data.nbytes, name=name)
     shared_array = np.ndarray(data.shape, dtype=dtype, buffer=shm.buf)
     shared_array[:] = data
+    print("[STEP 7] Shared memory initialized.")
     return shm
 
 
 def release_shared_memory(name):
+    print("[STEP 12] Releasing shared memory...")
     shm = shared_memory.SharedMemory(name=name)
     shm.close()
     shm.unlink()
+    print("[STEP 12] Shared memory released.")
 
 
 def draw_points(start, length, canvas_shape, theta, phi, radius, color):
@@ -49,48 +53,58 @@ def draw_points(start, length, canvas_shape, theta, phi, radius, color):
 
 
 def project_point_cloud(input_file):
+    print("[STEP 1] Reading LAS file...")
     try:
         point_cloud = laspy.read(input_file.name)
     except Exception as e:
         return f"Failed to read LAS file: {e}"
 
+    print("[STEP 1] LAS file loaded successfully.")
+
     height, width = IMAGE_HEIGHT, IMAGE_HEIGHT * 2
     output_shape = (height, width, 3)
-
     total_points = point_cloud.header.point_count
-    print(f"[INFO] Loaded {total_points} points.")
+    print(f"[STEP 2] Point cloud shape: {total_points} points")
 
+    # === Step 3: Create blank canvas ===
+    print("[STEP 3] Initializing blank canvas...")
     canvas = np.zeros(output_shape, dtype=np.uint16)
 
-    # === Coordinate setup ===
+    # === Step 4: Compute distances ===
+    print("[STEP 4] Computing 3D distances...")
     xyz = np.vstack((point_cloud.x, point_cloud.y, point_cloud.z)).T
     distances = np.linalg.norm(xyz, axis=1)
     distances[distances == 0] = 1e-6  # Avoid divide by zero
 
-    # === Normalize distances for radius scaling ===
+    # === Step 5: Normalize distances for depth-aware radius ===
+    print("[STEP 5] Normalizing distances...")
     dist_norm = (distances - distances.min()) / (distances.max() - distances.min())
 
-    # === Compute angles ===
+    # === Step 6: Compute angles ===
+    print("[STEP 6] Calculating spherical projection angles...")
     theta = ((np.arctan2(point_cloud.y, point_cloud.x) + pi) / (2 * pi)) * width * FACTOR
     theta = np.mod(theta, width * FACTOR).astype(np.uint64)
 
     z_norm = np.clip(point_cloud.z / distances, -1.0, 1.0)
     phi = ((np.arccos(z_norm) / pi) * height * FACTOR).astype(np.uint64)
 
-    # === Radius based on depth (closer → larger radius) ===
+    # === Step 6.5: Compute radius based on distance ===
+    print("[STEP 6.5] Calculating radii for depth effect...")
     radii = (FACTOR * POINT_SIZE * (1 - dist_norm) + 1).astype(np.uint64)
     radii = np.clip(radii, 1, MAX_RADIUS)
 
-    # === Colors ===
+    # === Step 6.6: Compute colors ===
+    print("[STEP 6.6] Extracting and adjusting point colors...")
     colors = np.stack([point_cloud.blue, point_cloud.green, point_cloud.red], axis=-1).astype(np.uint16)
     if SCALE_COLORS:
         colors *= 256
     colors = np.clip(colors, 0, 65535)
 
-    # === Shared Memory Canvas ===
+    # === Step 7: Create shared memory canvas ===
     shm = create_shared_memory_array(canvas, SHARED_MEMORY_NAME, np.uint16)
 
-    # === Start workers ===
+    # === Step 8: Start multiprocessing drawing ===
+    print("[STEP 8] Launching drawing processes...")
     processes = []
     for i in range(NUM_WORKERS):
         start = int(i * total_points / NUM_WORKERS)
@@ -101,19 +115,27 @@ def project_point_cloud(input_file):
         p.start()
         processes.append(p)
 
+    print("[STEP 9] Waiting for drawing to complete...")
     for p in processes:
         p.join()
+    print("[STEP 9] All drawing processes finished.")
 
-    # === Finalize output ===
+    # === Step 10: Convert canvas back ===
+    print("[STEP 10] Retrieving final image from shared memory...")
     final_image = np.ndarray(output_shape, dtype=np.uint16, buffer=shm.buf)
     if not FULL_RES:
+        print("[STEP 10.1] Downsampling image to 8-bit for display...")
         final_image = (final_image / 256).astype(np.uint8)
 
+    # === Step 11: Save image ===
     output_file = "output_image.jpg"
+    print(f"[STEP 11] Saving image to: {output_file}")
     cv2.imwrite(output_file, final_image)
+
+    # === Step 12: Cleanup shared memory ===
     release_shared_memory(SHARED_MEMORY_NAME)
-    print(f"[INFO] Saved output to {output_file}")
 
+    print("[STEP 13] Projection pipeline complete.")
     return output_file