unknown
218 lines
7.9 KiB
Python
218 lines
7.9 KiB
Python
import os
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os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
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import cv2
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import torch
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import torchvision.transforms.functional as F
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import torchvision.transforms as T
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from torchvision.models.optical_flow import raft_large, raft_small
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import numpy as np
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import time
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import orisys
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from torchvision.utils import flow_to_image
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import matplotlib.pyplot as plt
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def plot(imgs, **imshow_kwargs):
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if not isinstance(imgs[0], list):
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# Make a 2d grid even if there's just 1 row
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imgs = [imgs]
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num_rows = len(imgs)
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num_cols = len(imgs[0])
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_, axs = plt.subplots(nrows=num_rows, ncols=num_cols, squeeze=False)
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for row_idx, row in enumerate(imgs):
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for col_idx, img in enumerate(row):
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ax = axs[row_idx, col_idx]
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img = F.to_pil_image(img.to("cpu"))
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ax.imshow(np.asarray(img), **imshow_kwargs)
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ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
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plt.tight_layout()
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def preprocess(batch, resize=None):
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"""
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Preprocess image batch for RAFT model.
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Args:
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batch: Tensor of shape (N, C, H, W)
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resize: Optional tuple (height, width) to resize. If None, keeps original size.
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Returns:
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Preprocessed tensor
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"""
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transform_list = [
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T.ConvertImageDtype(torch.float32),
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T.Normalize(mean=0.5, std=0.5), # map [0, 1] into [-1, 1]
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]
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if resize is not None:
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transform_list.append(T.Resize(size=resize))
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transforms = T.Compose(transform_list)
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batch = transforms(batch)
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return batch
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def setup_simple_raft():
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"""
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Setup a simpler version using available models
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"""
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# Check for torchvision RAFT (available in torchvision 0.13+)
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# try:
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# import torchvision
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# if hasattr(torchvision.models, 'optical_flow'):
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# raft = torchvision.models.optical_flow.raft_small(pretrained=True)
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# raft = raft.to('cuda')
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# raft.eval()
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# print("Loaded RAFT small from torchvision")
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# return raft
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# except:
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# pass
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model = raft_small(pretrained=True, progress=False).to('cuda')
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model = model.eval()
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# Alternative: Use LiteFlowNet (lighter alternative)
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print("RAFT loaded successfully")
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return model
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def compute_dense_optical_flow_simple(prev_gray, curr_gray):
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"""
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Fallback to OpenCV optical flow if RAFT is not available
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"""
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# Use OpenCV's Farneback optical flow
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flow = cv2.calcOpticalFlowFarneback(
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prev_gray, curr_gray, None,
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pyr_scale=0.5, levels=3, winsize=15,
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iterations=3, poly_n=5, poly_sigma=1.2, flags=0
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)
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# Convert flow to visualization
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hsv = np.zeros((curr_gray.shape[0], curr_gray.shape[1], 3), dtype=np.uint8)
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hsv[..., 1] = 255
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mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
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hsv[..., 0] = ang * 180 / np.pi / 2
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hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
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flow_rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
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return flow_rgb
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def main_simple():
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# Initialize camera
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sensor = orisys.Sensor('s1.mp4', config_name="prototype", cal_path= "./config/cal.npy", isstitch=True, cuda=True, verbose=True)
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model = setup_simple_raft()
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"Using device: {device}")
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# cap = cv2.VideoCapture(0)
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# cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
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# cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
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prev_gray = None
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fps_buffer = []
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flow_track = None
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while True:
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# ret, frame = cap.read()
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# if not ret:
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# break
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# # Convert to grayscale
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# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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sensor.get_img()
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frame = sensor.img.copy()
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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gray = frame
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# Compute optical flow
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if prev_gray is not None:
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start_time = time.time()
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# Use simple optical flow
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# flow_viz = compute_dense_optical_flow_simple(prev_gray, gray)
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# Convert numpy arrays from (H, W, C) to (C, H, W) format
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# Normalize to [0, 1] range
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prev_tensor = torch.from_numpy(prev_gray.transpose(2, 0, 1).astype(np.float32) / 255.0)
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curr_tensor = torch.from_numpy(gray.transpose(2, 0, 1).astype(np.float32) / 255.0)
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# Preprocess: add batch dimension, preprocess (keeps batch dimension)
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# preprocess expects (N, C, H, W), returns (N, C, H, W)
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# Pass resize=None to keep original size (400x400)
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prev_tensor = preprocess(prev_tensor.unsqueeze(0), resize=None).to(device) # (1, C, H, W)
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curr_tensor = preprocess(curr_tensor.unsqueeze(0), resize=None).to(device) # (1, C, H, W)
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print(f"prev_tensor shape: {prev_tensor.shape}")
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# RAFT expects tensors with batch dimension: (batch_size, C, H, W)
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with torch.no_grad():
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flow = model(prev_tensor, curr_tensor)
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predicted_flows = flow[-1]
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print(f"Flow shape: {predicted_flows.shape}")
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print(f"dtype = {predicted_flows.dtype}")
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print(f"shape = {predicted_flows.shape} = (N, 2, H, W)")
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print(f"min = {predicted_flows.min()}, max = {predicted_flows.max()}")
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# Calculate FPS
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fps = 1.0 / (time.time() - start_time)
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fps_buffer.append(fps)
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if len(fps_buffer) > 30:
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fps_buffer.pop(0)
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avg_fps = np.mean(fps_buffer)
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print(f"avg_fps: {avg_fps}")
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# grid = [[img1, flow_img] for (img1, flow_img) in zip(img1_batch, flow_imgs)]
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# plot(grid)
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# plt.show()
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# Convert RAFT flow tensor to numpy array for draw_arrows
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# RAFT flow shape: (1, 2, H, W) where:
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# - Channel 0: x-component (horizontal flow)
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# - Channel 1: y-component (vertical flow)
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# Flow values are already in pixel coordinates (not normalized)
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# Remove batch dimension: (1, 2, H, W) -> (2, H, W)
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flow_np = predicted_flows[0].cpu().numpy() # or .squeeze(0)
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# Transpose to (H, W, 2) format expected by draw_arrows
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flow_np = flow_np.transpose(1, 2, 0) # (2, H, W) -> (H, W, 2)
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if flow_track is None:
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flow_track = np.zeros_like(flow_np)
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else:
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if abs(np.mean(flow_np)) >0.1:
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flow_track = flow_track + flow_np
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flow_np = flow_track
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# Flow is already in pixel coordinates, no denormalization needed
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# Keep as float32 for draw_arrows (it can handle float values)
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print(f"Flow numpy shape: {flow_np.shape}, dtype: {flow_np.dtype}")
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print(f"Flow range: x=[{flow_np[:,:,0].min():.2f}, {flow_np[:,:,0].max():.2f}], "
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f"y=[{flow_np[:,:,1].min():.2f}, {flow_np[:,:,1].max():.2f}]")
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arrows = orisys.util.draw_arrows(
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gray,
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flow_np,
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threshold=1,
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grid_spacing=10,
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arrow_scale=1.0
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)
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# Display results
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# cv2.putText(frame, f"FPS: {avg_fps:.1f}", (10, 30),
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# cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
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# cv2.imshow('Original', frame)
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# cv2.imshow('Grayscale', gray)
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cv2.imshow('Optical Flow', arrows)
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prev_gray = gray.copy()
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#步骤 3.5: 键盘控制
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key = cv2.waitKey(1) & 0xFF
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if key == ord("q"):
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print("\n退出程序...")
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break
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if key == ord("r"):
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# 重置光流追踪器（当追踪漂移时使用）
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flow_track = None
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print("传感器已重置")
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if __name__ == "__main__":
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main_simple() |