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visual.py
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visual.py
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# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
import os
import numpy as np
import random
import argparse
import torch
import torchvision.transforms.functional as F
import cv2
from tqdm import tqdm
from pathlib import Path
from PIL import Image, ImageDraw
from models import build_model
from util.tool import load_model
from util.plot_utils import COCO_CATEGORIES
from main import get_args_parser
from torch.nn.functional import interpolate
from typing import List
import motmetrics as mm
import shutil
import json
import pycocotools.mask as mask_util
from detectron2.structures import Instances
from detectron2.utils.visualizer import ColorMode, Visualizer
from detectron2.layers import paste_masks_in_image
from detectron2.utils.memory import retry_if_cuda_oom
np.random.seed(2020)
COLORS_10 = [(144, 238, 144), (178, 34, 34), (221, 160, 221), (0, 255, 0), (0, 128, 0), (210, 105, 30), (220, 20, 60),
(192, 192, 192), (255, 228, 196), (50, 205, 50), (139, 0, 139), (100, 149, 237), (138, 43, 226),
(238, 130, 238),
(255, 0, 255), (0, 100, 0), (127, 255, 0), (255, 0, 255), (0, 0, 205), (255, 140, 0), (255, 239, 213),
(199, 21, 133), (124, 252, 0), (147, 112, 219), (106, 90, 205), (176, 196, 222), (65, 105, 225),
(173, 255, 47),
(255, 20, 147), (219, 112, 147), (186, 85, 211), (199, 21, 133), (148, 0, 211), (255, 99, 71),
(144, 238, 144),
(255, 255, 0), (230, 230, 250), (0, 0, 255), (128, 128, 0), (189, 183, 107), (255, 255, 224),
(128, 128, 128),
(105, 105, 105), (64, 224, 208), (205, 133, 63), (0, 128, 128), (72, 209, 204), (139, 69, 19),
(255, 245, 238),
(250, 240, 230), (152, 251, 152), (0, 255, 255), (135, 206, 235), (0, 191, 255), (176, 224, 230),
(0, 250, 154),
(245, 255, 250), (240, 230, 140), (245, 222, 179), (0, 139, 139), (143, 188, 143), (255, 0, 0),
(240, 128, 128),
(102, 205, 170), (60, 179, 113), (46, 139, 87), (165, 42, 42), (178, 34, 34), (175, 238, 238),
(255, 248, 220),
(218, 165, 32), (255, 250, 240), (253, 245, 230), (244, 164, 96), (210, 105, 30)]
def plot_one_box(x, img, color=None, label=None, score=None, line_thickness=None, mask=None):
tl = 1
color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
# cv2.rectangle(img, c1, c2, color, thickness=tl)
if label:
tf = max(tl - 1, 1) # font thickness
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(img, c1, c2, color, -1) # filled
cv2.putText(img,
label, (c1[0], c1[1] - 2),
0,
tl / 3, [225, 255, 255],
thickness=tf,
lineType=cv2.LINE_AA)
if score is not None:
cv2.putText(img, score, (c1[0], c1[1] + 30), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
# print("c1c2 = {} {}".format(c1, c2))
if mask is not None:
v = Visualizer(img, scale=1)
vis_mask = v.draw_binary_mask(mask[0].cpu().numpy(), color=None, edge_color=None, text=None)
img = vis_mask.get_image()
return img
def draw_bboxes(ori_img, bbox, mask=None, offset=(0, 0), cvt_color=False):
img = ori_img
for i, box in enumerate(bbox):
if mask is not None and mask.shape[0] > 0:
m = mask[i]
else:
m = None
x1, y1, x2, y2 = [int(i) for i in box[:4]]
x1 += offset[0]
x2 += offset[0]
y1 += offset[1]
y2 += offset[1]
if len(box) > 4:
score = '{:.2f}'.format(box[4])
label = int(box[5])
else:
score = None
label = None
# box text and bar
color = COCO_CATEGORIES[label-1]['color']
class_name = COCO_CATEGORIES[label-1]['name']
label_str = '{}@{}'.format(class_name, score)
# t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]
# img = plot_one_box([x1, y1, x2, y2], img, color, label, score=score, mask=m)
# img = plot_one_box([x1, y1, x2, y2], img, color, label_str, score=None, mask=m)
img = plot_one_box([x1, y1, x2, y2], img, color, None, score=None, mask=m)
return img
def draw_points(img: np.ndarray, points: np.ndarray, color=(255, 255, 255)) -> np.ndarray:
assert len(points.shape) == 2 and points.shape[1] == 2, 'invalid points shape: {}'.format(points.shape)
for i, (x, y) in enumerate(points):
if i >= 300:
color = (0, 255, 0)
cv2.circle(img, (int(x), int(y)), 2, color=color, thickness=2)
return img
def tensor_to_numpy(tensor: torch.Tensor) -> np.ndarray:
return tensor.detach().cpu().numpy()
class Detector(object):
def __init__(self, args, model=None, postprocessors=None, seq_num=2, img_dir=None):
self.args = args
self.detr = model
self.postprocessors = postprocessors
self.img_dir = img_dir
self.file_name = seq_num['file_name']
self.id = seq_num['id']
self.img_height = 800
self.img_width = 1333
self.mean = [0.485, 0.456, 0.406]
self.std = [0.229, 0.224, 0.225]
self.save_path = os.path.join(self.args.output_dir, 'results')
os.makedirs(self.save_path, exist_ok=True)
def init_img(self, img):
ori_img = img.copy()
self.seq_h, self.seq_w = img.shape[:2]
scale = self.img_height / min(self.seq_h, self.seq_w)
if max(self.seq_h, self.seq_w) * scale > self.img_width:
scale = self.img_width / max(self.seq_h, self.seq_w)
target_h = int(self.seq_h * scale)
target_w = int(self.seq_w * scale)
img = cv2.resize(img, (target_w, target_h))
img = F.normalize(F.to_tensor(img), self.mean, self.std)
img = img.unsqueeze(0)
return img, ori_img
@staticmethod
def filter_dt_by_score(dt_instances: Instances, prob_threshold: float) -> Instances:
keep = dt_instances.scores > prob_threshold
return dt_instances[keep]
@staticmethod
def visualize_img_with_bbox(img_path, img, dt_instances: Instances, ref_pts=None, gt_boxes=None):
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
if dt_instances.has('scores'):
if dt_instances.has('masks'):
img_show = draw_bboxes(img, np.concatenate(
[dt_instances.boxes, dt_instances.scores.reshape(-1, 1), dt_instances.labels.reshape(-1, 1)],
axis=-1), dt_instances.masks)
else:
img_show = draw_bboxes(img, np.concatenate(
[dt_instances.boxes, dt_instances.scores.reshape(-1, 1), dt_instances.labels.reshape(-1, 1)],
axis=-1))
else:
img_show = draw_bboxes(img, dt_instances.boxes)
if ref_pts is not None:
img_show = draw_points(img_show, ref_pts)
cv2.imwrite(img_path, img_show)
def detect(self, prob_threshold=0.4, vis=True):
img = cv2.imread(os.path.join(self.img_dir, self.file_name))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
cur_img, ori_img = self.init_img(img)
outputs = self.detr(cur_img.cuda().float())
orig_target_sizes = torch.stack([torch.tensor([self.seq_h, self.seq_w]).cuda()])
results = self.postprocessors['bbox'](outputs, target_sizes=orig_target_sizes)
if 'segm' in postprocessors.keys():
target_sizes = torch.stack([torch.tensor([self.img_height, self.img_width]).cuda()])
results = postprocessors['segm'](results, outputs, orig_target_sizes, target_sizes)
dt_instances = Instances((1, 1))
dt_instances.boxes = results[0]['boxes']
dt_instances.scores = results[0]['scores']
dt_instances.labels = results[0]['labels']
dt_instances.masks = results[0]['masks']
dt_instances = dt_instances.to(torch.device('cpu'))
dt_instances = self.filter_dt_by_score(dt_instances, prob_threshold)
if vis:
cur_vis_img_path = os.path.join(self.save_path, self.file_name)
self.visualize_img_with_bbox(cur_vis_img_path, ori_img, dt_instances, ref_pts=None)
return dt_instances
if __name__ == '__main__':
parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
args = parser.parse_args()
if args.output_dir:
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
# load model and weights
detr, _, postprocessors = build_model(args)
checkpoint = torch.load(args.resume, map_location='cpu')
detr = load_model(detr, args.resume)
detr.eval()
detr = detr.cuda()
ann_path = './data/coco/annotations/instances_val2017.json'
img_dir = './data/coco/val2017'
annos = json.load(open(ann_path, 'r'))
images = annos['images'][1000:2000]
for seq_num in tqdm(images):
det = Detector(args, model=detr, postprocessors=postprocessors, seq_num=seq_num, img_dir=img_dir)
det.detect(vis=True)