102 lines
4.0 KiB
Python
102 lines
4.0 KiB
Python
|
# Copyright (C) 2020-2022 Intel Corporation
|
||
|
|
# Copyright (C) CVAT.ai Corporation
|
||
|
|
#
|
||
|
|
# SPDX-License-Identifier: MIT
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import os
|
||
|
|
import cv2
|
||
|
|
import torch
|
||
|
|
from networks.mainnetwork import Network
|
||
|
|
from dataloaders import helpers
|
||
|
|
|
||
|
|
class ModelHandler:
|
||
|
|
def __init__(self):
|
||
|
|
base_dir = os.environ.get("MODEL_PATH", "/opt/nuclio/iog")
|
||
|
|
model_path = os.path.join(base_dir, "IOG_PASCAL_SBD.pth")
|
||
|
|
self.device = torch.device("cpu")
|
||
|
|
|
||
|
|
# Number of input channels (RGB + heatmap of IOG points)
|
||
|
|
self.net = Network(nInputChannels=5, num_classes=1, backbone='resnet101',
|
||
|
|
output_stride=16, sync_bn=None, freeze_bn=False)
|
||
|
|
|
||
|
|
pretrain_dict = torch.load(model_path, weights_only=True)
|
||
|
|
self.net.load_state_dict(pretrain_dict)
|
||
|
|
self.net.to(self.device)
|
||
|
|
self.net.eval()
|
||
|
|
|
||
|
|
def handle(self, image, bbox, pos_points, neg_points, threshold):
|
||
|
|
with torch.no_grad():
|
||
|
|
# extract a crop with padding from the image
|
||
|
|
crop_padding = 30
|
||
|
|
crop_bbox = [
|
||
|
|
max(bbox[0][0] - crop_padding, 0),
|
||
|
|
max(bbox[0][1] - crop_padding, 0),
|
||
|
|
min(bbox[1][0] + crop_padding, image.width - 1),
|
||
|
|
min(bbox[1][1] + crop_padding, image.height - 1)
|
||
|
|
]
|
||
|
|
crop_shape = (
|
||
|
|
int(crop_bbox[2] - crop_bbox[0] + 1), # width
|
||
|
|
int(crop_bbox[3] - crop_bbox[1] + 1), # height
|
||
|
|
)
|
||
|
|
|
||
|
|
# try to use crop_from_bbox(img, bbox, zero_pad) here
|
||
|
|
input_crop = np.array(image.crop(crop_bbox)).astype(np.float32)
|
||
|
|
|
||
|
|
# resize the crop
|
||
|
|
input_crop = cv2.resize(input_crop, (512, 512), interpolation=cv2.INTER_NEAREST)
|
||
|
|
crop_scale = (512 / crop_shape[0], 512 / crop_shape[1])
|
||
|
|
|
||
|
|
def translate_points_to_crop(points):
|
||
|
|
points = [
|
||
|
|
((p[0] - crop_bbox[0]) * crop_scale[0], # x
|
||
|
|
(p[1] - crop_bbox[1]) * crop_scale[1]) # y
|
||
|
|
for p in points]
|
||
|
|
|
||
|
|
return points
|
||
|
|
|
||
|
|
pos_points = translate_points_to_crop(pos_points)
|
||
|
|
neg_points = translate_points_to_crop(neg_points)
|
||
|
|
|
||
|
|
# Create IOG image
|
||
|
|
pos_gt = np.zeros(shape=input_crop.shape[:2], dtype=np.float64)
|
||
|
|
neg_gt = np.zeros(shape=input_crop.shape[:2], dtype=np.float64)
|
||
|
|
for p in pos_points:
|
||
|
|
pos_gt = np.maximum(pos_gt, helpers.make_gaussian(pos_gt.shape, center=p))
|
||
|
|
for p in neg_points:
|
||
|
|
neg_gt = np.maximum(neg_gt, helpers.make_gaussian(neg_gt.shape, center=p))
|
||
|
|
iog_image = np.stack((pos_gt, neg_gt), axis=2).astype(dtype=input_crop.dtype)
|
||
|
|
|
||
|
|
# Convert iog_image to an image (0-255 values)
|
||
|
|
cv2.normalize(iog_image, iog_image, 0, 255, cv2.NORM_MINMAX)
|
||
|
|
|
||
|
|
# Concatenate input crop and IOG image
|
||
|
|
input_blob = np.concatenate((input_crop, iog_image), axis=2)
|
||
|
|
|
||
|
|
# numpy image: H x W x C
|
||
|
|
# torch image: C X H X W
|
||
|
|
input_blob = input_blob.transpose((2, 0, 1))
|
||
|
|
# batch size is 1
|
||
|
|
input_blob = np.array([input_blob])
|
||
|
|
input_tensor = torch.from_numpy(input_blob)
|
||
|
|
|
||
|
|
input_tensor = input_tensor.to(self.device)
|
||
|
|
output_mask = self.net.forward(input_tensor)[4]
|
||
|
|
output_mask = output_mask.to(self.device)
|
||
|
|
pred = np.transpose(output_mask.data.numpy()[0, :, :, :], (1, 2, 0))
|
||
|
|
pred = pred > threshold
|
||
|
|
pred = np.squeeze(pred)
|
||
|
|
|
||
|
|
# Convert a mask to a polygon
|
||
|
|
pred = np.array(pred, dtype=np.uint8)
|
||
|
|
pred = cv2.resize(pred, dsize=(crop_shape[0], crop_shape[1]),
|
||
|
|
interpolation=cv2.INTER_CUBIC)
|
||
|
|
cv2.normalize(pred, pred, 0, 255, cv2.NORM_MINMAX)
|
||
|
|
|
||
|
|
mask = np.zeros((image.height, image.width), dtype=np.uint8)
|
||
|
|
x = int(crop_bbox[0])
|
||
|
|
y = int(crop_bbox[1])
|
||
|
|
mask[y : y + crop_shape[1], x : x + crop_shape[0]] = pred
|
||
|
|
|
||
|
|
return mask
|