1+ import numpy as np
2+ import scipy .ndimage
3+ from scipy .interpolate import griddata
4+
5+ # Optional imports for Deep Learning backends (PyTorch/TensorFlow)
6+ try :
7+ import torch
8+ import torch .nn .functional as F
9+ HAS_TORCH = True
10+ except ImportError :
11+ HAS_TORCH = False
12+
13+ def warp_image (image , u , v ):
14+ """
15+ Warps 'image' based on the velocity field (u, v).
16+ Used to warp Image 2 back towards Image 1 based on coarse estimation.
17+
18+ Args:
19+ image (np.ndarray): Image 2 (grayscale or RGB).
20+ u (np.ndarray): Dense displacement field in x (same shape as image).
21+ v (np.ndarray): Dense displacement field in y (same shape as image).
22+
23+ Returns:
24+ np.ndarray: The warped image.
25+ """
26+ # Create a grid of coordinates
27+ h , w = image .shape [:2 ]
28+ y_grid , x_grid = np .mgrid [0 :h , 0 :w ]
29+
30+ # Apply the reverse displacement (Warp Image 2 "back" to Image 1)
31+ # According to Choi et al: Warped(x,y) = Img2(x + u, y + v)
32+ map_x = x_grid + u
33+ map_y = y_grid + v
34+
35+ # Handle interpolation (Scipy is used to avoid adding OpenCV dependency,
36+ # though cv2.remap is faster)
37+ if image .ndim == 2 :
38+ warped = scipy .ndimage .map_coordinates (
39+ image , [map_y , map_x ], order = 1 , mode = 'nearest'
40+ )
41+ else :
42+ # Handle RGB if necessary
43+ warped = np .zeros_like (image )
44+ for i in range (image .shape [2 ]):
45+ warped [..., i ] = scipy .ndimage .map_coordinates (
46+ image [..., i ], [map_y , map_x ], order = 1 , mode = 'nearest'
47+ )
48+
49+ return warped
50+
51+ def upscale_flow (u_coarse , v_coarse , x_coarse , y_coarse , target_shape ):
52+ """
53+ Upscales the sparse PIV grid (from correlation) to dense pixel resolution.
54+
55+ Args:
56+ u_coarse, v_coarse: Velocity components from standard OpenPIV.
57+ x_coarse, y_coarse: Meshgrid coordinates of the coarse vectors.
58+ target_shape: (height, width) of the original image.
59+
60+ Returns:
61+ u_dense, v_dense: Fields matching target_shape.
62+ """
63+ h , w = target_shape
64+ grid_y , grid_x = np .mgrid [0 :h , 0 :w ]
65+
66+ # Flatten source points
67+ points = np .column_stack ((x_coarse .flatten (), y_coarse .flatten ()))
68+
69+ # Interpolate (Linear is usually sufficient for the "Coarse" step)
70+ u_dense = griddata (points , u_coarse .flatten (), (grid_x , grid_y ), method = 'linear' )
71+ v_dense = griddata (points , v_coarse .flatten (), (grid_x , grid_y ), method = 'linear' )
72+
73+ # Fill NaNs at edges (common in PIV) with nearest valid value or zero
74+ mask = np .isnan (u_dense )
75+ if np .any (mask ):
76+ u_dense [mask ] = 0 # Simplified; ideal is nearest neighbor inpaint
77+ v_dense [mask ] = 0
78+
79+ return u_dense , v_dense
80+
81+ class DeepRefiner :
82+ """
83+ Implements the Choi et al. refinement algorithm.
84+ Wrapper for an Optical Flow CNN (e.g., RAFT, FlowNet2, LiteFlowNet).
85+ """
86+ def __init__ (self , model_path = None , device = 'cpu' ):
87+ """
88+ Args:
89+ model_path (str): Path to trained weights (.pth, .onnx).
90+ device (str): 'cpu' or 'cuda'.
91+ """
92+ self .device = device
93+ self .model = self ._load_model (model_path )
94+
95+ def _load_model (self , path ):
96+ """
97+ Placeholder for model loading logic.
98+ In a real implementation, this would load RAFT or FlowNet2.
99+ """
100+ if not HAS_TORCH :
101+ print ("Warning: PyTorch not found. Using dummy identity model." )
102+ return None
103+
104+ # Boilerplate: Load your specific architecture here
105+ # model = RAFT(args)
106+ # model.load_state_dict(torch.load(path))
107+ # return model.eval().to(self.device)
108+ return "Loaded_Model_Placeholder"
109+
110+ def predict_residual (self , img1 , img2_warped ):
111+ """
112+ Uses the CNN to find the small 'residual' motion between Image 1
113+ and the Warped Image 2.
114+ """
115+ # 1. Preprocess images (Normalize to [0,1] or [-1,1], convert to Tensor)
116+ # 2. Feed to self.model
117+ # 3. Return residual flow numpy array
118+
119+ # --- DUMMY IMPLEMENTATION FOR BOILERPLATE ---
120+ # Returns zero residual (no refinement)
121+ h , w = img1 .shape
122+ return np .zeros ((h , w )), np .zeros ((h , w ))
123+
124+ def refine (self , image1 , image2 , u_piv , v_piv , x_piv , y_piv ):
125+ """
126+ Main execution method for the Hybrid PIV+CNN approach.
127+
128+ Args:
129+ image1, image2: Raw particle images.
130+ u_piv, v_piv: Result from openpiv.pyprocess.extended_search_area_piv
131+ x_piv, y_piv: Coordinates of the PIV grid.
132+
133+ Returns:
134+ u_final, v_final: Dense, high-resolution velocity fields.
135+ """
136+
137+ # Step 1: Upscale Coarse PIV to Pixel Resolution
138+ print ("Upscaling coarse PIV field..." )
139+ u_dense , v_dense = upscale_flow (
140+ u_piv , v_piv , x_piv , y_piv , image1 .shape
141+ )
142+
143+ # Step 2: Warp Image 2 using the Coarse Flow
144+ # The warped image should now align closely with Image 1
145+ print ("Warping Image 2..." )
146+ image2_warped = warp_image (image2 , u_dense , v_dense )
147+
148+ # Step 3: CNN Inference on (Image 1, Warped Image 2)
149+ # The CNN only needs to find the small differences (residuals)
150+ print ("Calculating residual flow with CNN..." )
151+ u_res , v_res = self .predict_residual (image1 , image2_warped )
152+
153+ # Step 4: Combine Results
154+ # Total Flow = Coarse Flow + Residual Flow
155+ u_final = u_dense + u_res
156+ v_final = v_dense + v_res
157+
158+ return u_final , v_final
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