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tracker.py
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tracker.py
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# -*- coding: utf-8 -*-
import cv2
import numpy as np
import torch
from mobile_sam import SamPredictor, sam_model_registry
from skimage import measure
from inference.inference_core import InferenceCore
from inference.interact.interactive_utils import (image_to_torch,
index_numpy_to_one_hot_torch)
from model.network import XMem
class Tracker:
def __init__(self, xmem_config, max_obj_cnt, device):
self.device = device
self.xmem_config = xmem_config
self.max_obj_cnt = max_obj_cnt
if self.device.lower() != 'cpu':
self.network = XMem(
self.xmem_config, './saves/XMem.pth').eval().to('cuda')
else:
self.network = XMem(
self.xmem_config, './saves/XMem.pth', map_location='cpu').eval()
self.processor = InferenceCore(self.network, config=self.xmem_config)
self.processor.set_all_labels(range(1, self.max_obj_cnt+1))
def masks_on_im(self, masks, image):
result = np.zeros_like(image, dtype=np.uint8)
for mask in masks:
color = np.random.randint(0, 256, size=3)
colored_mask = np.repeat(mask[:, :, np.newaxis], 3, axis=2)
masked_region = colored_mask * color
result += masked_region.astype(np.uint8)
return result
def create_mask_from_img(self, image, yolov7_bboxes, sam_checkpoint='./saves/mobile_sam.pt', model_type='vit_t', device='0'):
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
if self.device.lower() != 'cpu':
sam.to(device=f'cuda:{device}')
else:
sam.to(device='cpu')
predictor = SamPredictor(sam)
predictor.set_image(image)
input_boxes = torch.tensor(yolov7_bboxes, device=predictor.device)
transformed_boxes = predictor.transform.apply_boxes_torch(
input_boxes, image.shape[:2])
masks = []
for box in transformed_boxes:
mask, _, _ = predictor.predict_torch(
point_coords=None,
point_labels=None,
boxes=box.unsqueeze(0),
multimask_output=False,
)
values, counts = torch.unique(mask, return_counts=True)
value_count = [(v.item(), c.item())
for v, c in zip(values, counts)]
value_count = sorted(value_count, key=lambda x: x[1], reverse=True)
mask[mask != 0] = value_count[0][0] if value_count[0][0] != 0 else value_count[1][0]
masks.append(mask)
for i, mask in enumerate(masks):
binary_mask = masks[i].cpu().squeeze().numpy().astype(np.uint8)
contours, hierarchy = cv2.findContours(
binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
largest_contour = max(contours, key=cv2.contourArea)
bbox = [int(x) for x in cv2.boundingRect(largest_contour)]
segmentation = largest_contour.flatten().tolist()
mask = segmentation
height, width, _ = image.shape
mask = np.array(mask).reshape(-1, 2)
mask_norm = mask / np.array([width, height])
xmin, ymin = mask_norm.min(axis=0)
xmax, ymax = mask_norm.max(axis=0)
bbox_norm = np.array([xmin, ymin, xmax, ymax])
yolo = np.concatenate([bbox_norm, mask_norm.reshape(-1)])
result = self.masks_on_im(
[mask.cpu().squeeze().numpy().astype(np.uint8) for mask in masks], image)
result = result[:, :, 0]
# Filter result from small segmented areas, if np.uniq(result) > len(yolov7bboxes)
if len(np.unique(result)) > len(yolov7_bboxes) + 1:
filtered_result_values = []
mask_uniq_values = torch.unique(torch.tensor(
result), return_counts=True)[0].tolist()
class_pixel_cnts = torch.unique(torch.tensor(
result), return_counts=True)[1].tolist()
sorted_indices = np.argsort(class_pixel_cnts)[::-1].tolist()
for index in sorted_indices:
filtered_result_values.append(mask_uniq_values[index])
if len(filtered_result_values) == len(yolov7_bboxes) + 1:
break
for pixel_val in mask_uniq_values:
if pixel_val not in filtered_result_values:
result[result == pixel_val] = 0
return result
def masks_to_boxes_with_ids(self, mask_tensor: torch.Tensor) -> torch.Tensor:
unique_values = torch.unique(mask_tensor[mask_tensor != 0])
bbox_list = []
for unique_value in unique_values:
binary_mask = (mask_tensor == unique_value).byte()
nonzero_coords = torch.nonzero(binary_mask, as_tuple=False)
if nonzero_coords.numel() > 0:
min_x = torch.min(nonzero_coords[:, 2])
min_y = torch.min(nonzero_coords[:, 1])
max_x = torch.max(nonzero_coords[:, 2])
max_y = torch.max(nonzero_coords[:, 1])
bbox = [unique_value.item(), min_x.item(), min_y.item(),
max_x.item(), max_y.item()]
bbox_list.append(bbox)
return bbox_list
def predict(self, image):
if self.device.lower() != 'cpu':
frame_torch, _ = image_to_torch(image, device='cuda')
else:
frame_torch, _ = image_to_torch(image, device='cpu')
return self.processor.step(frame_torch)
def add_mask(self, image, mask):
if self.device.lower() != 'cpu':
frame_torch, _ = image_to_torch(image, device='cuda')
mask_torch = index_numpy_to_one_hot_torch(
mask, self.max_obj_cnt + 1).to('cuda')
else:
frame_torch, _ = image_to_torch(image, device='cpu')
mask_torch = index_numpy_to_one_hot_torch(
mask, self.max_obj_cnt + 1).to('cpu')
print('Added new mask')
return self.processor.step(frame_torch, mask_torch[1:])
def keep_largest_connected_components(self, mask):
mask_np = mask.squeeze().cpu().numpy()
unique_values = np.unique(mask_np)
unique_values = unique_values[unique_values != 0]
new_mask = np.zeros_like(mask_np)
for class_value in unique_values:
binary_mask = (mask_np == class_value).astype(np.uint8)
# Dynamic kernel size = (25% of object width, 25% of object height)
_, _, w, h = cv2.boundingRect(binary_mask)
kernel = (max(1, int(w // 4)), max(1, int(h // 4)))
binary_mask = cv2.morphologyEx(binary_mask, cv2.MORPH_CLOSE, kernel)
labeled_mask, num_components = measure.label(binary_mask, background=0, return_num=True)
component_sizes = [np.sum(labeled_mask == i) for i in range(1, num_components + 1)]
largest_component = np.argmax(component_sizes) + 1 # +1 because labels start from 1
new_mask[labeled_mask == largest_component] = class_value
new_mask = torch.from_numpy(new_mask).unsqueeze(0)
return new_mask