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sl_end2end_videotest.py
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sl_end2end_videotest.py
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""" A class for testing a SegLink model on a video file or webcam """
import numpy as np
import cv2
import tensorflow as tf
from timeit import default_timer as timer
from sl_model import DSODSL512, SL512
from sl_utils import PriorUtil
from ssd_data import preprocess
from sl_utils import rbox_to_polygon
from crnn_model import CRNN
from crnn_utils import alphabet87 as alphabet
from crnn_data import crop_words
from crnn_utils import decode
if __name__ == '__main__':
Model = DSODSL512
input_shape = (512,512,3)
weights_path = './checkpoints/201711132011_dsodsl512_synthtext/weights.001.h5'
segment_threshold = 0.55
link_threshold = 0.40
det_model = Model(input_shape)
prior_util = PriorUtil(det_model)
det_model.load_weights(weights_path)
#input_width = 256
input_width = 384
input_height = 32
weights_path = './checkpoints/201806190711_crnn_gru_synthtext/weights.300000.h5'
rec_model = CRNN((input_width, input_height, 1), len(alphabet), prediction_only=True, gru=True)
rec_model.load_weights(weights_path, by_name=True)
# To test on webcam 0, /dev/video0
video_path = 0
start_frame = 0
record = True
record_file_name = 'sl_end2end_record.avi'
try:
vid = cv2.VideoCapture(video_path)
if not vid.isOpened():
raise IOError(("Couldn't open video file or webcam. If you're "
"trying to open a webcam, make sure you video_path is an integer!"))
vid_w = vid.get(cv2.CAP_PROP_FRAME_WIDTH)
vid_h = vid.get(cv2.CAP_PROP_FRAME_HEIGHT)
# skip frames until reaching start_frame
if start_frame > 0:
vid.set(cv2.CAP_PROP_POS_MSEC, start_frame)
accum_time = 0
curr_fps = 0
fps = "FPS: ??"
prev_time = timer()
input_size = input_shape[:2]
record_buffer = []
record_timestamps = []
init_time = timer()
while True:
retval, img = vid.read()
if not retval:
print("Done!")
break
# model to predict
x = np.array([preprocess(img, input_size)])
y = det_model.predict(x)
result = prior_util.decode(y[0], segment_threshold, link_threshold)
img1 = np.copy(img)
img2 = np.zeros_like(img)
# calculate fps
curr_time = timer()
exec_time = curr_time - prev_time
prev_time = curr_time
accum_time = accum_time + exec_time
curr_fps = curr_fps + 1
if accum_time > 1:
accum_time = accum_time - 1
fps = "FPS: " + str(curr_fps)
curr_fps = 0
rboxes = result[:,:5]
if len(rboxes) > 0:
bh = rboxes[:,3]
rboxes[:,2] += bh * 0.1
rboxes[:,3] += bh * 0.2
boxes = np.asarray([rbox_to_polygon(r) for r in rboxes])
boxes = np.flip(boxes, axis=1) # TODO: fix order of points, why?
boxes = np.reshape(boxes, (-1, 8))
boxes_mask = np.array([not (np.any(b < 0-10) or np.any(b > 512+10)) for b in boxes]) # box inside image
boxes = boxes[boxes_mask]
rboxes = rboxes[boxes_mask]
if len(boxes) == 0:
boxes = np.empty((0,8))
#for b in boxes:
# xy = b.reshape((-1,1,2)) / input_size * [vid_w, vid_h]
# xy = np.round(xy)
# xy = xy.astype(np.int32)
# cv2.polylines(img1, [xy], True, (0,0,255))
boxes = np.clip(boxes/512, 0, 1)
words = crop_words(img, boxes, input_height, width=input_width, grayscale=True)
words = np.asarray([w.transpose(1,0,2) for w in words])
if len(words) > 0:
res_crnn = rec_model.predict(words)
xy = rboxes[:,:2]
xy[:,0] = xy[:,0] - rboxes[:,2] / 2
xy = xy / input_size * [vid_w, vid_h]
for i in range(len(words)):
idxs = np.argmax(res_crnn[i], axis=1)
confs = res_crnn[i][range(len(idxs)),idxs]
non_blank_mask = idxs != len(alphabet)-1
if np.any(non_blank_mask):
mean_conf = np.mean(confs[non_blank_mask])
chars = [alphabet[c] for c in idxs]
res_str = decode(chars)
# filter based on recognition threshold
#if mean_conf > 0.7-0.4*np.exp(-0.1*np.sum(non_blank_mask)):
if mean_conf > 0.6:
b = boxes[i].reshape((-1,1,2)) * [vid_w, vid_h]
b = np.asarray(np.round(b), dtype=np.int32)
cv2.polylines(img1, [b], True, (0,0,255))
#cv2.imwrite('croped_word_%03i.png' % (i), words[i])
cv2.putText(img2, res_str, tuple(xy[i].astype(int)),
cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255,255,255), 1)
else:
#print('drop %5.3f %s' % (mean_conf, res_str))
pass
# draw fps
cv2.rectangle(img1, (0,0), (50, 17), (255,255,255), -1)
cv2.putText(img1, fps, (3,10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0,0,0), 1)
img = np.concatenate((img1, img2), axis=1)
cv2.imshow("SegLink detection", img)
if record:
record_buffer.append(img)
record_timestamps.append(timer()-init_time)
if cv2.waitKey(1) & 0xFF == ord('q'):
if record:
print('write viedo file: %s' % (record_file_name))
fourcc = cv2.VideoWriter_fourcc(*'HFYU') # losseless
output_size = (record_buffer[0].shape[1], record_buffer[0].shape[0])
output_framerate = 20.0
out = cv2.VideoWriter(record_file_name, fourcc, output_framerate, output_size)
#for i in range(len(record_buffer)):
# out.write(record_buffer[i])
i = -1
for t in np.arange(0, record_timestamps[-1], 1/output_framerate):
if i == -1 and t > record_timestamps[0]:
i += 1
elif t > record_timestamps[i]:
i += 1
if i == -1:
output_img = np.zeros_like(record_buffer[0])
else:
output_img = record_buffer[i]
out.write(output_img)
out.release()
break
except KeyboardInterrupt:
pass
# ffmpeg -y -i sl_end2end_record.avi -c:v libx264 -b:v 2400k -preset slow -movflags +faststart -pix_fmt yuv420p sl_end2end_record.mp4