def demo_MBNet(self, opt, val_data, weight_path, out_path):
self.model_all.load_weights(weight_path, by_name=True)
print('load weights from {}'.format(weight_path))
for f in range(len(val_data)):
img_name = os.path.join(out_path, val_data[f] + '_visible.png')
if not img_name.lower().endswith(('.jpg', '.png')):
continue
print(img_name)
img = cv2.imread(img_name)
img_name_lwir = os.path.join(out_path, val_data[f] + '_lwir.png')
print(img_name_lwir)
img_lwir = cv2.imread(img_name_lwir)
x_in = bbox_process.format_img(img, opt)
x_in_lwir = bbox_process.format_img(img_lwir, opt)
Y = self.model_all.predict([x_in, x_in_lwir])
proposals = bbox_process.pred_pp_1st(self.anchors, Y[0], Y[1], opt)
bbx, scores = bbox_process.pred_det(proposals,
Y[2],
Y[3],
opt,
step=2)
for ind in range(len(bbx)):
if scores[ind][0] < 0.5:
continue
(x1, y1, x2, y2) = bbx[ind, :]
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
#cv2.putText(img, str(scores[ind][0]), (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 0.3, (0, 0, 255), 1)
cv2.rectangle(img_lwir, (x1, y1), (x2, y2), (0, 255, 0), 2)
#cv2.putText(img_lwir, str(scores[ind][0]), (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 0.3, (0, 0, 255), 1)
img_concat = np.concatenate([img, img_lwir], axis=1)
cv2.imwrite(os.path.join(out_path, val_data[f] + '.png'),
img_concat)
def demo_video_MBNet(self, opt, test_file, lwir_test_file, weight_path):
self.model_all.load_weights(weight_path, by_name=True)
print('loaded weights from {}'.format(weight_path))
print('loaded visible video from :' + test_file)
print('loaded lwir video from :' + lwir_test_file)
vid = cv2.VideoCapture(test_file)
lwir_vid = cv2.VideoCapture(lwir_test_file)
fps = vid.get(cv2.CAP_PROP_FPS)
frame_all = vid.get(cv2.CAP_PROP_FRAME_COUNT)
print("[INFO] 视频FPS: {}".format(fps))
print("[INFO] 视频总帧数: {}".format(frame_all))
print("[INFO] 视频时长: {}s".format(frame_all / fps))
frame_width = int(vid.get(3))
frame_height = int(vid.get(4))
out_vid = cv2.VideoWriter('./output_videos/output_visible.avi',
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),
fps, (frame_width, frame_height))
out_lwir_vid = cv2.VideoWriter(
'./output_videos/output_lwir.avi',
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps,
(frame_width, frame_height))
idx = 0
while idx < frame_all: #防止越界
ret, frame = vid.read()
lwir_ret, lwir_frame = lwir_vid.read()
x_in = bbox_process.format_img(frame, opt)
x_in_lwir = bbox_process.format_img(lwir_frame, opt)
Y = self.model_all.predict([x_in, x_in_lwir])
proposals = bbox_process.pred_pp_1st(self.anchors, Y[0], Y[1], opt)
bbx, scores = bbox_process.pred_det(proposals,
Y[2],
Y[3],
opt,
step=2)
for ind in range(len(bbx)):
if scores[ind][0] < 0.5:
continue
(x1, y1, x2, y2) = bbx[ind, :]
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
# cv2.putText(img, str(scores[ind][0]), (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 0.3, (0, 0, 255), 1)
cv2.rectangle(lwir_frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
out_vid.write(frame)
out_lwir_vid.write(lwir_frame)
idx += 1
vid.release()
lwir_vid.release()
out_vid.release()
out_lwir_vid.release()
cv2.destroyAllWindows()
def test_MBNet(self, opt, data_path, val_data, weight_path):
self.model_all.load_weights(weight_path, by_name=True)
print('load weights from {}'.format(weight_path))
for f in range(len(val_data)):
img_name = os.path.join(data_path, val_data[f])
if not img_name.lower().endswith(('.jpg', '.png')):
continue
print(img_name)
img = cv2.imread(img_name)
img_name_lwir = os.path.join(data_path[:-7] + 'lwir',
val_data[f][:-11] + 'lwir.png')
print(img_name_lwir)
img_lwir = cv2.imread(img_name_lwir)
start_time = time.time()
x_in = bbox_process.format_img(img, opt)
x_in_lwir = bbox_process.format_img(img_lwir, opt)
Y = self.model_all.predict([x_in, x_in_lwir])
proposals = bbox_process.pred_pp_1st(self.anchors, Y[0], Y[1], opt)
bbx, scores = bbox_process.pred_det(proposals,
Y[2],
Y[3],
opt,
step=2)
print('Test time: %.4f s' % (time.time() - start_time))
image_name_save, png = val_data[f].split('.')
image_name_save = image_name_save[:-8] #lwir
result_path = './data/result'
if not os.path.exists(result_path):
os.makedirs(result_path)
image_set_file = os.path.join(result_path,
image_name_save + '.txt')
list_file = open(image_set_file, 'w')
for i in range(len(bbx)):
image_write_txt = 'person' + ' ' + str(np.round(bbx[i][0], 4)) + ' ' + str(np.round(bbx[i][1], 4)) + ' ' \
+ str(np.round(bbx[i][2], 4)) + ' ' + str(np.round(bbx[i][3], 4)) + ' ' + str(round(float(scores[i]), 8))
list_file.write(image_write_txt)
list_file.write('\n')
list_file.close()