def predict(image_path):
original_image = cv2.imread(image_path)
original_image_size = original_image.shape[:2]
image_data = utils.image_preporcess(np.copy(original_image),
[input_size, input_size])
image_data = image_data[np.newaxis, ...]
return_tensors = utils.read_pb_return_tensors(graph, pb_file,
return_elements)
with tf.Session(graph=graph) as sess:
pred_sbbox, pred_mbbox, pred_lbbox = sess.run(
[return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={return_tensors[0]: image_data})
pred_bbox = np.concatenate([
np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))
],
axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size,
input_size, 0.4)
bboxes = utils.nms(bboxes, 0.45, method='nms')
image = utils.draw_bbox(original_image, bboxes)
image = Image.fromarray(image)
image.show()
image.save(output_path)
original_image_size = original_image.shape[:2]
image_data = utils.image_preporcess(np.copy(original_image),
[input_size, input_size])
image_data = image_data[np.newaxis, ...]
return_tensors = utils.read_pb_return_tensors(graph, pb_file, return_elements)
with tf.Session(graph=graph) as sess:
pred_sbbox, pred_mbbox, pred_lbbox = sess.run(
[return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={return_tensors[0]: image_data})
# print(pred_sbbox.shape)
pred_bbox = np.concatenate([
np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))
],
axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, original_image_size, input_size,
0.4)
bboxes = utils.nms(bboxes, 0.45, method='nms')
image = utils.draw_bbox(original_image, bboxes)
# cv2.imshow("print chinese to image", image)
# cv2.waitKey()
image = Image.fromarray(image)
image.show()
image.save(output_path)
prev_time = time.time()
pred_sbbox, pred_mbbox, pred_lbbox = sess.run(
[return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={ return_tensors[0]: image_data})
pred_time = time.time()
# print('time:',pred_time-prev_time)
pred_bbox = np.concatenate([np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))], axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size, 0.45)
bboxes = utils.nms(bboxes, 0.45, method='nms')
image = utils.draw_bbox(frame, bboxes)
curr_time = time.time()
exec_time = curr_time - prev_time
result = np.asarray(image)
info = "time: %.2f ms" %(1000*exec_time)
cv2.putText(result, text=info, org=(50, 70), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1, color=(255, 0, 0), thickness=2)
cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if writeVideo_flag:
# save a frame
out.write(result)
cv2.imshow("result", result)
if cv2.waitKey(1) & 0xFF == ord('q'): break
def infer_video(video_path, output_path):
with tf.Session(graph=graph) as sess:
writeVideo_flag = True
if writeVideo_flag:
vid = cv2.VideoCapture(video_path)
if not vid.isOpened():
raise IOError("Couldn't open webcam or video")
video_FourCC = cv2.VideoWriter_fourcc(*'MP4V')
video_fps = vid.get(cv2.CAP_PROP_FPS)
video_size = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
isOutput = True if output_path != "" else False
if isOutput:
#print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size))
out = cv2.VideoWriter(output_path, video_FourCC, video_fps,
video_size)
list_file = open('detection.txt', 'w')
frame_index = -1
while True:
return_value, frame = vid.read()
if return_value != True:
break
if return_value:
# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(frame)
# print('image:',image)
else:
raise ValueError("No image!")
frame_size = frame.shape[:2]
image_data = utils.image_preporcess(np.copy(frame),
[input_size, input_size])
image_data = image_data[np.newaxis, ...]
prev_time = time.time()
pred_sbbox, pred_mbbox, pred_lbbox = sess.run(
[return_tensors[1], return_tensors[2], return_tensors[3]],
feed_dict={return_tensors[0]: image_data})
pred_time = time.time()
# print('time:',pred_time-prev_time)
pred_bbox = np.concatenate([
np.reshape(pred_sbbox, (-1, 5 + num_classes)),
np.reshape(pred_mbbox, (-1, 5 + num_classes)),
np.reshape(pred_lbbox, (-1, 5 + num_classes))
],
axis=0)
bboxes = utils.postprocess_boxes(pred_bbox, frame_size, input_size,
0.45)
bboxes = utils.nms(bboxes, 0.45, method='nms')
image = utils.draw_bbox(frame, bboxes)
curr_time = time.time()
exec_time = curr_time - prev_time
result = np.asarray(image)
info = "time:" + str(round(
1000 * exec_time, 2)) + " ms, FPS: " + str(
round((1000 / (1000 * exec_time)), 1))
cv2.putText(result,
text=info,
org=(50, 70),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1,
color=(255, 0, 0),
thickness=2) # 把字加上去
cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if writeVideo_flag:
# save a frame
out.write(result)
cv2.imshow("result", result)
if cv2.waitKey(1) & 0xFF == ord('q'): break