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import cv2
import concern.webcv2 as webcv2
import numpy as np
import torch
from concern.config import Configurable, State
from data.processes.make_icdar_data import MakeICDARData
class SegDetectorVisualizer(Configurable):
vis_num = State(default=4)
eager_show = State(default=False)
def __init__(self, **kwargs):
cmd = kwargs['cmd']
if 'eager_show' in cmd:
self.eager_show = cmd['eager_show']
def visualize(self, batch, output_pair, pred):
boxes, _ = output_pair
result_dict = {}
for i in range(batch['image'].size(0)):
result_dict.update(
self.single_visualize(batch, i, boxes[i], pred))
if self.eager_show:
webcv2.waitKey()
return {}
return result_dict
def _visualize_heatmap(self, heatmap, canvas=None):
if isinstance(heatmap, torch.Tensor):
heatmap = heatmap.cpu().numpy()
heatmap = (heatmap[0] * 255).astype(np.uint8)
if canvas is None:
pred_image = heatmap
else:
pred_image = (heatmap.reshape(
*heatmap.shape[:2], 1).astype(np.float32) / 255 + 1) / 2 * canvas
pred_image = pred_image.astype(np.uint8)
return pred_image
def single_visualize(self, batch, index, boxes, pred):
image = batch['image'][index]
polygons = batch['polygons'][index]
if isinstance(polygons, torch.Tensor):
polygons = polygons.cpu().data.numpy()
ignore_tags = batch['ignore_tags'][index]
original_shape = batch['shape'][index]
filename = batch['filename'][index]
std = np.array([0.229, 0.224, 0.225]).reshape(3, 1, 1)
mean = np.array([0.485, 0.456, 0.406]).reshape(3, 1, 1)
image = (image.cpu().numpy() * std + mean).transpose(1, 2, 0) * 255
pred_canvas = image.copy().astype(np.uint8)
pred_canvas = cv2.resize(pred_canvas, (original_shape[1], original_shape[0]))
if isinstance(pred, dict) and 'thresh' in pred:
thresh = self._visualize_heatmap(pred['thresh'][index])
if isinstance(pred, dict) and 'thresh_binary' in pred:
thresh_binary = self._visualize_heatmap(pred['thresh_binary'][index])
MakeICDARData.polylines(self, thresh_binary, polygons, ignore_tags)
for box in boxes:
box = np.array(box).astype(np.int32).reshape(-1, 2)
cv2.polylines(pred_canvas, [box], True, (0, 255, 0), 2)
if isinstance(pred, dict) and 'thresh_binary' in pred:
cv2.polylines(thresh_binary, [box], True, (0, 255, 0), 1)
if self.eager_show:
webcv2.imshow(filename + ' output', cv2.resize(pred_canvas, (1024, 1024)))
if isinstance(pred, dict) and 'thresh' in pred:
webcv2.imshow(filename + ' thresh', cv2.resize(thresh, (1024, 1024)))
webcv2.imshow(filename + ' pred', cv2.resize(pred_canvas, (1024, 1024)))
if isinstance(pred, dict) and 'thresh_binary' in pred:
webcv2.imshow(filename + ' thresh_binary', cv2.resize(thresh_binary, (1024, 1024)))
return {}
else:
if isinstance(pred, dict) and 'thresh' in pred:
return {
filename + '_output': pred_canvas,
filename + '_thresh': thresh,
# filename + '_pred': thresh_binary
}
else:
return {
filename + '_output': pred_canvas,
# filename + '_pred': thresh_binary
}
def demo_visualize(self, image_path, output):
boxes, _ = output
boxes = boxes[0]
original_image = cv2.imread(image_path, cv2.IMREAD_COLOR)
original_shape = original_image.shape
pred_canvas = original_image.copy().astype(np.uint8)
pred_canvas = cv2.resize(pred_canvas, (original_shape[1], original_shape[0]))
for box in boxes:
box = np.array(box).astype(np.int32).reshape(-1, 2)
cv2.polylines(pred_canvas, [box], True, (0, 255, 0), 2)
return pred_canvas
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