120 lines
3.7 KiB
Python
120 lines
3.7 KiB
Python
import numpy as np
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import cv2
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# RAFT dependencies
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import sys
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sys.path.append('RAFT/core')
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from collections import namedtuple
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import torch
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import argparse
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from raft import RAFT
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from utils.utils import InputPadder
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RAFT_model = None
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fgbg = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=16, detectShadows=True)
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def background_subtractor(frame, fgbg):
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fgmask = fgbg.apply(frame)
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return cv2.bitwise_and(frame, frame, mask=fgmask)
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def RAFT_estimate_flow(frame1, frame2, device='cuda', subtract_background=True):
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global RAFT_model
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if RAFT_model is None:
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args = argparse.Namespace(**{
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'model': 'RAFT/models/raft-things.pth',
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'mixed_precision': True,
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'small': False,
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'alternate_corr': False,
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'path': ""
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})
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RAFT_model = torch.nn.DataParallel(RAFT(args))
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RAFT_model.load_state_dict(torch.load(args.model))
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RAFT_model = RAFT_model.module
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RAFT_model.to(device)
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RAFT_model.eval()
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if subtract_background:
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frame1 = background_subtractor(frame1, fgbg)
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frame2 = background_subtractor(frame2, fgbg)
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with torch.no_grad():
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frame1_torch = torch.from_numpy(frame1).permute(2, 0, 1).float()[None].to(device)
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frame2_torch = torch.from_numpy(frame2).permute(2, 0, 1).float()[None].to(device)
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padder = InputPadder(frame1_torch.shape)
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image1, image2 = padder.pad(frame1_torch, frame2_torch)
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# estimate optical flow
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_, next_flow = RAFT_model(image1, image2, iters=20, test_mode=True)
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_, prev_flow = RAFT_model(image2, image1, iters=20, test_mode=True)
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next_flow = next_flow[0].permute(1, 2, 0).cpu().numpy()
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prev_flow = prev_flow[0].permute(1, 2, 0).cpu().numpy()
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fb_flow = next_flow + prev_flow
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fb_norm = np.linalg.norm(fb_flow, axis=2)
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occlusion_mask = fb_norm[..., None].repeat(3, axis=-1)
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return next_flow, prev_flow, occlusion_mask, frame1, frame2
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# ... rest of the file ...
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def compute_diff_map(next_flow, prev_flow, prev_frame, cur_frame, prev_frame_styled):
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h, w = cur_frame.shape[:2]
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next_flow = cv2.resize(next_flow, (w, h))
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prev_flow = cv2.resize(prev_flow, (w, h))
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# This is not correct. The flow map should be applied to the next frame to get previous frame
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# flow_map = -next_flow.copy()
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# remove white noise (@alexfredo suggestion)
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next_flow[np.abs(next_flow) < 3] = 0
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prev_flow[np.abs(prev_flow) < 3] = 0
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# Here is the correct version
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flow_map = prev_flow.copy()
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flow_map[:,:,0] += np.arange(w)
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flow_map[:,:,1] += np.arange(h)[:,np.newaxis]
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warped_frame = cv2.remap(prev_frame, flow_map, None, cv2.INTER_NEAREST, borderMode = cv2.BORDER_REFLECT)
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warped_frame_styled = cv2.remap(prev_frame_styled, flow_map, None, cv2.INTER_NEAREST, borderMode = cv2.BORDER_REFLECT)
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# compute occlusion mask
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fb_flow = next_flow + prev_flow
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fb_norm = np.linalg.norm(fb_flow, axis=2)
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occlusion_mask = fb_norm[..., None]
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diff_mask_org = np.abs(warped_frame.astype(np.float32) - cur_frame.astype(np.float32)) / 255
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diff_mask_org = diff_mask_org.max(axis = -1, keepdims=True)
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diff_mask_stl = np.abs(warped_frame_styled.astype(np.float32) - cur_frame.astype(np.float32)) / 255
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diff_mask_stl = diff_mask_stl.max(axis = -1, keepdims=True)
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alpha_mask = np.maximum(occlusion_mask * 0.3, diff_mask_org * 4, diff_mask_stl * 2)
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alpha_mask = alpha_mask.repeat(3, axis = -1)
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#alpha_mask_blured = cv2.dilate(alpha_mask, np.ones((5, 5), np.float32))
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alpha_mask = cv2.GaussianBlur(alpha_mask, (51,51), 5, cv2.BORDER_REFLECT)
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alpha_mask = np.clip(alpha_mask, 0, 1)
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return alpha_mask, warped_frame_styled
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def frames_norm(occl): return occl / 127.5 - 1
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def flow_norm(flow): return flow / 255
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def occl_norm(occl): return occl / 127.5 - 1
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def flow_renorm(flow): return flow * 255
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def occl_renorm(occl): return (occl + 1) * 127.5
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