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import torch
import sys
import torch.nn.functional as F
import numpy as np
import utils.loss
import utils.samp
import utils.data
import utils.improc
import utils.misc
import utils.saveload
import cv2
from nets.blocks import InputPadder
import torch
from PIL import Image, ImageDraw
import numpy as np
torch.set_float32_matmul_precision('medium')
def run_example(processor, model, task_prompt, image, text_input=None):
if text_input is None:
prompt = task_prompt
else:
prompt = task_prompt + text_input
inputs = processor(text=prompt, images=image, return_tensors="pt").to('cuda', torch.float32)
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
num_beams=3
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))
return parsed_answer
def polygons_to_mask(image, prediction, fill_value=255):
"""
Converts polygons into a mask.
Parameters:
- image: A PIL Image instance whose size will be used for the mask.
- prediction: Dictionary containing 'polygons' and 'labels'.
'polygons' is a list where each element is a list of sub-polygons.
- fill_value: The pixel value used to fill the polygon areas (default 255 for a binary mask).
Returns:
- A NumPy array representing the mask (same width and height as the input image).
"""
# Create a blank grayscale mask image with the same size as the original image.
mask = Image.new('L', image.size, 0)
draw = ImageDraw.Draw(mask)
# Iterate over each set of polygons
for polygons in prediction['polygons']:
# Each element in "polygons" can be a sub-polygon
for poly in polygons:
# Ensure the polygon is in the right shape and has at least 3 points.
poly_arr = np.array(poly).reshape(-1, 2)
if poly_arr.shape[0] < 3:
print('Skipping invalid polygon:', poly_arr)
continue
# Convert the polygon vertices into a list for drawing.
poly_list = poly_arr.reshape(-1).tolist()
# Draw the polygon on the mask with the fill_value.
draw.polygon(poly_list, fill=fill_value)
# Convert the PIL mask image to a NumPy array and return it.
return np.array(mask)
class Tracker:
def __init__(self, model, mean, std, S, stride, inference_iters, target_res, device='cuda'):
"""
Initializes the Tracker.
Args:
model: The model used to compute feature maps and forward window flow.
mean: Tensor or value used for normalizing the input.
std: Tensor or value used for normalizing the input.
S: Window size for the tracker.
stride: The stride used when updating the window.
inference_iters: Number of inference iterations.
device: Torch device, defaults to 'cuda'.
"""
self.model = model
self.mean = mean
self.std = std
self.S = S
self.stride = stride
self.inference_iters = inference_iters
self.device = device
self.target_res = target_res
self.padder = None
self.cnt = 0
self.fmap_anchor = None
self.fmaps2 = None
self.flows8 = None
self.visconfs8 = None
self.flows = [] # List to store computed flows
self.visibs = [] # List to store visibility confidences
self.rgbs = [] # List to store RGB frames
def reset(self):
"""Reset the tracker state."""
self.padder = None
self.cnt = 0
self.fmap_anchor = None
self.fmaps2 = None
self.flows8 = None
self.visconfs8 = None
self.flows = []
self.visibs = []
self.rgbs = []
def preprocess(self, rgb_frame):
# Resize frame (scale to keep maximum dimension ~1024)
scale = min(self.target_res / rgb_frame.shape[0], self.target_res / rgb_frame.shape[1])
rgb_resized = cv2.resize(rgb_frame, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)
# Convert to tensor, normalize and move to device.
rgb_tensor = torch.from_numpy(rgb_resized).permute(2, 0, 1).float().unsqueeze(0).to(self.device)
rgb_tensor = rgb_tensor / 255.0
self.rgbs.append(rgb_tensor.cpu())
# import pdb; pdb.set_trace()
rgb_tensor = (rgb_tensor - self.mean) / self.std
return rgb_tensor
@torch.no_grad()
def track(self, rgb_frame):
"""
Process a single RGB frame and return the computed flow when available.
Args:
rgb_frame: A NumPy array containing the RGB frame.
(Assumed to be in RGB; if coming from OpenCV, convert it before passing.)
Returns:
flow_predictions: The predicted flow for the current frame (or None if not enough frames have been processed).
"""
torch.cuda.empty_cache()
rgb_tensor = self.preprocess(rgb_frame)
# Initialize padder on the first frame.
if self.cnt == 0:
self.padder = InputPadder(rgb_tensor.shape)
rgb_padded = self.padder.pad(rgb_tensor)[0]
_, _, H_pad, W_pad = rgb_padded.shape
C = 256 # Feature map channel dimension (could be parameterized if needed)
H8, W8 = H_pad // 8, W_pad // 8
# Accumulate feature maps until the window is full.
if self.cnt == 0:
self.fmap_anchor = self.model.get_fmaps(rgb_padded, 1, 1, None, False, False).reshape(1, C, H8, W8)
self.fmaps2 = self.fmap_anchor[:, None]
self.cnt += 1
return None
new_fmap = self.model.get_fmaps(rgb_padded, 1, 1, None, False, False).reshape(1, 1, C, H8, W8)
self.fmaps2 = torch.cat([self.fmaps2[:, (1 if self.fmaps2.shape[1] >= self.S else 0):].detach().clone(), new_fmap], dim=1)
# need to track
if self.cnt - self.S + 1 >= 0 and (self.cnt - self.S + 1) % self.stride == 0:
# Initialize or update temporary flow buffers.
iter_num = self.inference_iters
if self.flows8 is None:
self.flows8 = torch.zeros((self.S, 2, H_pad // 8, W_pad // 8), device=self.device)
self.visconfs8 = torch.zeros((self.S, 2, H_pad // 8, W_pad // 8), device=self.device)
# iter_num = self.inference_iters
else:
self.flows8 = torch.cat([
self.flows8[self.stride:self.stride + self.S // 2].detach().clone(),
self.flows8[self.stride + self.S // 2 - 1:self.stride + self.S // 2].detach().clone().repeat(self.S // 2, 1, 1, 1)
])
self.visconfs8 = torch.cat([
self.visconfs8[self.stride:self.stride + self.S // 2].detach().clone(),
self.visconfs8[self.stride + self.S // 2 - 1:self.stride + self.S // 2].detach().clone().repeat(self.S // 2, 1, 1, 1)
])
# import pdb; pdb.set_trace()
# Compute flow predictions using the model's forward window.
flow_predictions, visconf_predictions, self.flows8, self.visconfs8, _ = self.model.forward_window(
self.fmap_anchor,
self.fmaps2,
self.visconfs8,
iters=iter_num,
flowfeat=None,
flows8=self.flows8,
is_training=False
)
flow_predictions = self.padder.unpad(flow_predictions[-1][0 if self.cnt == self.S - 1 else -self.stride:])
visconf_predictions = self.padder.unpad(torch.sigmoid(visconf_predictions[-1][0 if self.cnt == self.S - 1 else -self.stride:]))
self.cnt += 1
self.flows.append(flow_predictions.cpu())
self.visibs.append(visconf_predictions.cpu())
return flow_predictions, visconf_predictions
self.cnt += 1
return None
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