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124 lines
4.6 KiB
Python
124 lines
4.6 KiB
Python
# Copyright (C) 2020-2022 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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import numpy as np
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import os
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import cv2
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import torch
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from networks.mainnetwork import Network
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from dataloaders import helpers
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def convert_mask_to_polygon(mask):
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mask = np.array(mask, dtype=np.uint8)
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cv2.normalize(mask, mask, 0, 255, cv2.NORM_MINMAX)
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contours = None
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if int(cv2.__version__.split('.')[0]) > 3:
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contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)[0]
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else:
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contours = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS)[1]
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contours = max(contours, key=lambda arr: arr.size)
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if contours.shape.count(1):
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contours = np.squeeze(contours)
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if contours.size < 3 * 2:
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raise Exception('Less then three point have been detected. Can not build a polygon.')
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polygon = []
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for point in contours:
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polygon.append([int(point[0]), int(point[1])])
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return polygon
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class ModelHandler:
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def __init__(self):
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base_dir = os.environ.get("MODEL_PATH", "/opt/nuclio/iog")
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model_path = os.path.join(base_dir, "IOG_PASCAL_SBD.pth")
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self.device = torch.device("cpu")
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# Number of input channels (RGB + heatmap of IOG points)
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self.net = Network(nInputChannels=5, num_classes=1, backbone='resnet101',
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output_stride=16, sync_bn=None, freeze_bn=False)
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pretrain_dict = torch.load(model_path)
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self.net.load_state_dict(pretrain_dict)
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self.net.to(self.device)
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self.net.eval()
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def handle(self, image, bbox, pos_points, neg_points, threshold):
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with torch.no_grad():
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# extract a crop with padding from the image
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crop_padding = 30
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crop_bbox = [
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max(bbox[0][0] - crop_padding, 0),
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max(bbox[0][1] - crop_padding, 0),
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min(bbox[1][0] + crop_padding, image.width - 1),
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min(bbox[1][1] + crop_padding, image.height - 1)
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]
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crop_shape = (
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int(crop_bbox[2] - crop_bbox[0] + 1), # width
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int(crop_bbox[3] - crop_bbox[1] + 1), # height
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)
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# try to use crop_from_bbox(img, bbox, zero_pad) here
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input_crop = np.array(image.crop(crop_bbox)).astype(np.float32)
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# resize the crop
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input_crop = cv2.resize(input_crop, (512, 512), interpolation=cv2.INTER_NEAREST)
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crop_scale = (512 / crop_shape[0], 512 / crop_shape[1])
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def translate_points_to_crop(points):
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points = [
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((p[0] - crop_bbox[0]) * crop_scale[0], # x
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(p[1] - crop_bbox[1]) * crop_scale[1]) # y
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for p in points]
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return points
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pos_points = translate_points_to_crop(pos_points)
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neg_points = translate_points_to_crop(neg_points)
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# Create IOG image
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pos_gt = np.zeros(shape=input_crop.shape[:2], dtype=np.float64)
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neg_gt = np.zeros(shape=input_crop.shape[:2], dtype=np.float64)
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for p in pos_points:
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pos_gt = np.maximum(pos_gt, helpers.make_gaussian(pos_gt.shape, center=p))
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for p in neg_points:
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neg_gt = np.maximum(neg_gt, helpers.make_gaussian(neg_gt.shape, center=p))
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iog_image = np.stack((pos_gt, neg_gt), axis=2).astype(dtype=input_crop.dtype)
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# Convert iog_image to an image (0-255 values)
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cv2.normalize(iog_image, iog_image, 0, 255, cv2.NORM_MINMAX)
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# Concatenate input crop and IOG image
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input_blob = np.concatenate((input_crop, iog_image), axis=2)
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# numpy image: H x W x C
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# torch image: C X H X W
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input_blob = input_blob.transpose((2, 0, 1))
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# batch size is 1
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input_blob = np.array([input_blob])
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input_tensor = torch.from_numpy(input_blob)
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input_tensor = input_tensor.to(self.device)
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output_mask = self.net.forward(input_tensor)[4]
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output_mask = output_mask.to(self.device)
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pred = np.transpose(output_mask.data.numpy()[0, :, :, :], (1, 2, 0))
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pred = pred > threshold
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pred = np.squeeze(pred)
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# Convert a mask to a polygon
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polygon = convert_mask_to_polygon(pred)
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def translate_points_to_image(points):
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points = [
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(p[0] / crop_scale[0] + crop_bbox[0], # x
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p[1] / crop_scale[1] + crop_bbox[1]) # y
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for p in points]
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return points
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polygon = translate_points_to_image(polygon)
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return polygon
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