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#
# Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# Unless required by applicable law or agreed to in writing, software distributed
# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
# CONDITIONS OF ANY KIND, either express or implied. See the License for the
# specific language governing permissions and limitations under the License.
import sys
import cv2
import numpy as np
from ncnn.model_zoo import get_model
def draw_detection_objects_seg(image, class_names, objects, mat_map):
color = [128, 255, 128, 244, 35, 232]
color_count = len(color)
for obj in objects:
print(
"%d = %.5f at %.2f %.2f %.2f x %.2f\n"
% (obj.label, obj.prob, obj.rect.x, obj.rect.y, obj.rect.w, obj.rect.h)
)
cv2.rectangle(
image,
(int(obj.rect.x), int(obj.rect.y)),
(int(obj.rect.x + obj.rect.w), int(obj.rect.y + obj.rect.h)),
(255, 0, 0),
)
text = "%s %.1f%%" % (class_names[int(obj.label)], obj.prob * 100)
label_size, baseLine = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
x = obj.rect.x
y = obj.rect.y - label_size[1] - baseLine
if y < 0:
y = 0
if x + label_size[0] > image.shape[1]:
x = image.shape[1] - label_size[0]
cv2.rectangle(
image,
(int(x), int(y)),
(int(x + label_size[0]), int(y + label_size[1] + baseLine)),
(255, 255, 255),
-1,
)
cv2.putText(
image,
text,
(int(x), int(y + label_size[1])),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
(0, 0, 0),
)
width = mat_map.w
height = mat_map.h
size = mat_map.c
img_index2 = 0
threshold = 0.45
ptr2 = np.array(mat_map)
for i in range(height):
ptr1 = image[i].flatten()
img_index1 = 0
for j in range(width):
maxima = threshold
index = -1
for c in range(size):
# const float* ptr3 = ptr2 + c*width*height
ptr3 = ptr2[c].flatten()
if ptr3[img_index2] > maxima:
maxima = ptr3[img_index2]
index = c
if index > -1:
color_index = (index) * 3
if color_index < color_count:
b = color[color_index]
g = color[color_index + 1]
r = color[color_index + 2]
ptr1[img_index1] = b / 2 + ptr1[img_index1] / 2
ptr1[img_index1 + 1] = g / 2 + ptr1[img_index1 + 1] / 2
ptr1[img_index1 + 2] = r / 2 + ptr1[img_index1 + 2] / 2
img_index1 += 3
img_index2 += 1
image[i] = ptr1.reshape(image[i].shape)
cv2.imshow("image", image)
cv2.waitKey(0)
if __name__ == "__main__":
if len(sys.argv) != 2:
print("Usage: %s [imagepath]\n" % (sys.argv[0]))
sys.exit(0)
imagepath = sys.argv[1]
m = cv2.imread(imagepath)
if m is None:
print("cv2.imread %s failed\n" % (imagepath))
sys.exit(0)
net = get_model("peleenet_ssd", num_threads=4, use_gpu=True)
objects, seg_out = net(m)
draw_detection_objects_seg(m, net.class_names, objects, seg_out)
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