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test.py
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from models.CornerNet import CornerNet
import numpy as np
import cv2
import torch
import os
from inference import kp_detection
scales = [1]
# img_mean = np.array([0.40789654, 0.44719302, 0.47026115], dtype=np.float32)
# img_std = np.array([0.28863828, 0.27408164, 0.27809835], dtype=np.float32)
categories = 1
nms_threshold = 0.5
nms_algorithm = {
"nms": 0,
"linear_soft_nms": 1,
"exp_soft_nms": 2
}["exp_soft_nms"]
input_size = (511, 511)
def draw_detection(net, img_file, result_path=None):
img = cv2.imread(img_file)
detections = kp_detection(net, img)
image = cv2.imread(img_file)
for j in range(1, categories + 1):
cat_size = cv2.getTextSize(str(j), cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)[0]
color = np.random.random((3,)) * 0.6 + 0.4
color = color * 255
color = color.astype(np.int32).tolist()
if categories == 1:
color = (0, 0, 255)
for bbox in detections[j]:
bbox = bbox[0:4].astype(np.int32)
if bbox[1] - cat_size[1] - 2 < 0:
cv2.rectangle(image,
(bbox[0], bbox[1] + 2),
(bbox[0] + cat_size[0], bbox[1] + cat_size[1] + 2),
color, -1
)
cv2.putText(image, str(j),
(bbox[0], bbox[1] + cat_size[1] + 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), thickness=1
)
else:
cv2.rectangle(image,
(bbox[0], bbox[1] - cat_size[1] - 2),
(bbox[0] + cat_size[0], bbox[1] - 2),
color, -1
)
cv2.putText(image, str(j),
(bbox[0], bbox[1] - 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), thickness=1
)
cv2.rectangle(image,
(bbox[0], bbox[1]),
(bbox[2], bbox[3]),
color, 2
)
if result_path is not None:
cv2.imwrite(os.path.join(result_path, os.path.split(img_file)[1]), image)
print(os.path.join(result_path, os.path.split(img_file)[1]))
print("process {} over".format(img_file))
def main():
model_path = "result/checkpoint/0419/epoch_0_3.157.cpkt"
result_path = "result_img/"
img_path = "img/"
img_files = os.listdir(img_path)
net = CornerNet()
net.load_state_dict(torch.load(model_path))
net.cuda()
net.eval()
for f in img_files:
img_file = os.path.join(img_path, f)
draw_detection(net, img_file, result_path)
def save_heat():
model_file_path = "result/checkpoint/0411/epoch_18_0.710.cpkt"
result_path = "result_img/"
img_dir = "img/"
img_files = os.listdir(img_dir)
net = CornerNet()
print("loading model state_dict")
net.load_state_dict(torch.load(model_file_path))
print("loading over")
net.cuda()
net.eval()
with torch.no_grad():
for img_name in img_files:
img_path = os.path.join(img_dir, img_name)
print("process img: ", img_path)
img = cv2.imread(img_path)
img = cv2.resize(img, (511, 511))
img = img.transpose((2, 0, 1))
img = img / 255
img = torch.from_numpy(img)
img = img.float().cuda()
img = img.unsqueeze(0)
print("img: ", img.shape)
out = net(img)
out = [a.cpu().numpy() for a in out]
np.savez(os.path.join(result_path, img_name),
tl_heat=out[0],
br_heat=out[1],
tl_tag=out[2],
br_tag=out[3]
)
if __name__ == "__main__":
main()
# save_heat()