def make_video_from_list(out_vid_path, frames_list):
if frames_list[0] is not None:
img = cv2.imread(frames_list[0], True)
print frames_list[0]
h, w = img.shape[:2]
fourcc = cv2.cv.CV_FOURCC('m', 'p', '4', 'v')
out = cv2.VideoWriter(out_vid_path,fourcc, 20.0, (w, h), True)
print("Start Making File Video:%s " % out_vid_path)
print("%d Frames to Compress"%len(frames_list))
progress = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ',progressbar.Percentage(), ' ',progressbar.ETA()])
for i in progress(range(0,len(frames_list))):
if utils_image.check_image_with_pil(frames_list[i]):
out.write(img)
img = cv2.imread(frames_list[i], True)
out.release()
print("Finished Making File Video:%s " % out_vid_path)
def make_video_from_list(out_vid_path, frames_list):
if frames_list[0] is not None:
img = cv2.imread(frames_list[0], True)
print(frames_list[0])
h, w = img.shape[:2]
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(out_vid_path,fourcc, 20.0, (w, h), True)
print("Start Making File Video:%s " % out_vid_path)
print("%d Frames to Compress"%len(frames_list))
progress = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ',progressbar.Percentage(), ' ',progressbar.ETA()])
for i in progress(range(0,len(frames_list))):
if utils_image.check_image_with_pil(frames_list[i]):
out.write(img)
img = cv2.imread(frames_list[i], True)
out.release()
print("Finished Making File Video:%s " % out_vid_path)
def make_tracked_video(out_vid_path, labeled_video_frames):
if labeled_video_frames[0] is not None:
img = cv2.imread(labeled_video_frames[0], True)
print "Reading Filename: %s"%labeled_video_frames[0]
h, w = img.shape[:2]
print "Video Size: width: %d height: %d"%(h, w)
fourcc = cv2.cv.CV_FOURCC('m', 'p', '4', 'v')
out = cv2.VideoWriter(out_vid_path,fourcc, 20.0, (w, h), True)
print("Start Making File Video:%s " % out_vid_path)
print("%d Frames to Compress"%len(labeled_video_frames))
progress = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ',progressbar.Percentage(), ' ',progressbar.ETA()])
for i in progress(range(0,len(labeled_video_frames))):
if utils_image.check_image_with_pil(labeled_video_frames[i]):
out.write(img)
img = cv2.imread(labeled_video_frames[i], True)
out.release()
print("Finished Making File Video:%s " % out_vid_path)
def make_tracked_video(out_vid_path, labeled_video_frames):
if labeled_video_frames[0] is not None:
img = cv2.imread(labeled_video_frames[0], True)
print("Reading Filename: %s"%labeled_video_frames[0])
h, w = img.shape[:2]
print("Video Size: width: %d height: %d"%(h, w))
fourcc = cv2.cv.CV_FOURCC('m', 'p', '4', 'v')
out = cv2.VideoWriter(out_vid_path,fourcc, 20.0, (w, h), True)
print("Start Making File Video:%s " % out_vid_path)
print("%d Frames to Compress"%len(labeled_video_frames))
progress = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ',progressbar.Percentage(), ' ',progressbar.ETA()])
for i in progress(range(0,len(labeled_video_frames))):
if utils_image.check_image_with_pil(labeled_video_frames[i]):
out.write(img)
img = cv2.imread(labeled_video_frames[i], True)
out.release()
print("Finished Making File Video:%s " % out_vid_path)
def bbox_det_TENSORBOX_multiclass(frames_list, path_video_folder, hypes_file,
weights_file, pred_idl):
from train import build_forward
print("Starting DET Phase")
#### START TENSORBOX CODE ###
lenght = int(len(frames_list))
video_info = []
### Opening Hypes file for parameters
with open(hypes_file, 'r') as f:
H = json.load(f)
### Building Network
tf.reset_default_graph()
googlenet = googlenet_load.init(H)
x_in = tf.placeholder(tf.float32,
name='x_in',
shape=[H['image_height'], H['image_width'], 3])
if H['use_rezoom']:
pred_boxes, pred_logits, pred_confidences, pred_confs_deltas, pred_boxes_deltas = build_forward(
H, tf.expand_dims(x_in, 0), googlenet, 'test', reuse=None)
grid_area = H['grid_height'] * H['grid_width']
pred_confidences = tf.reshape(
tf.nn.softmax(
tf.reshape(pred_confs_deltas,
[grid_area * H['rnn_len'], H['num_classes']])),
[grid_area, H['rnn_len'], H['num_classes']])
pred_logits = tf.reshape(
tf.nn.softmax(
tf.reshape(pred_logits,
[grid_area * H['rnn_len'], H['num_classes']])),
[grid_area, H['rnn_len'], H['num_classes']])
if H['reregress']:
pred_boxes = pred_boxes + pred_boxes_deltas
else:
pred_boxes, pred_logits, pred_confidences = build_forward(
H, tf.expand_dims(x_in, 0), googlenet, 'test', reuse=None)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
saver.restore(
sess, weights_file
) ##### Restore a Session of the Model to get weights and everything working
#### Starting Evaluating the images
print(("%d Frames to DET" % len(frames_list)))
progress = progressbar.ProgressBar(widgets=[
progressbar.Bar('=', '[', ']'), ' ',
progressbar.Percentage(), ' ',
progressbar.ETA()
])
frameNr = 0
skipped = 0
for i in progress(list(range(0, len(frames_list)))):
current_frame = frame.Frame_Info()
current_frame.frame = frameNr
current_frame.filename = frames_list[i]
if utils_image.isnotBlack(
frames_list[i]) & utils_image.check_image_with_pil(
frames_list[i]):
img = imread(frames_list[i])
# test(frames_list[i])
feed = {x_in: img}
(np_pred_boxes, np_pred_logits,
np_pred_confidences) = sess.run(
[pred_boxes, pred_logits, pred_confidences],
feed_dict=feed)
_, rects = get_multiclass_rectangles(H,
np_pred_confidences,
np_pred_boxes,
rnn_len=H['rnn_len'])
if len(rects) > 0:
# pick = NMS(rects)
pick = rects
print((len(rects), len(pick)))
current_frame.rects = pick
frameNr = frameNr + 1
video_info.insert(len(video_info), current_frame)
print((len(current_frame.rects)))
else:
skipped = skipped + 1
else:
skipped = skipped + 1
print(("Skipped %d Black Frames" % skipped))
#### END TENSORBOX CODE ###
return video_info
def bbox_det_TENSORBOX_multiclass(frames_list,path_video_folder,hypes_file,weights_file,pred_idl):
from train import build_forward
print("Starting DET Phase")
#### START TENSORBOX CODE ###
lenght=int(len(frames_list))
video_info = []
### Opening Hypes file for parameters
with open(hypes_file, 'r') as f:
H = json.load(f)
### Building Network
tf.reset_default_graph()
googlenet = googlenet_load.init(H)
x_in = tf.placeholder(tf.float32, name='x_in', shape=[H['image_height'], H['image_width'], 3])
if H['use_rezoom']:
pred_boxes, pred_logits, pred_confidences, pred_confs_deltas, pred_boxes_deltas = build_forward(H, tf.expand_dims(x_in, 0), googlenet, 'test', reuse=None)
grid_area = H['grid_height'] * H['grid_width']
pred_confidences = tf.reshape(tf.nn.softmax(tf.reshape(pred_confs_deltas, [grid_area * H['rnn_len'], H['num_classes']])), [grid_area, H['rnn_len'], H['num_classes']])
pred_logits = tf.reshape(tf.nn.softmax(tf.reshape(pred_logits, [grid_area * H['rnn_len'], H['num_classes']])), [grid_area, H['rnn_len'], H['num_classes']])
if H['reregress']:
pred_boxes = pred_boxes + pred_boxes_deltas
else:
pred_boxes, pred_logits, pred_confidences = build_forward(H, tf.expand_dims(x_in, 0), googlenet, 'test', reuse=None)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
saver.restore(sess, weights_file )##### Restore a Session of the Model to get weights and everything working
#### Starting Evaluating the images
print("%d Frames to DET"%len(frames_list))
progress = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ',progressbar.Percentage(), ' ',progressbar.ETA()])
frameNr=0
skipped=0
for i in progress(range(0, len(frames_list))):
current_frame = frame.Frame_Info()
current_frame.frame=frameNr
current_frame.filename=frames_list[i]
if utils_image.isnotBlack(frames_list[i]) & utils_image.check_image_with_pil(frames_list[i]):
img = imread(frames_list[i])
# test(frames_list[i])
feed = {x_in: img}
(np_pred_boxes,np_pred_logits, np_pred_confidences) = sess.run([pred_boxes,pred_logits, pred_confidences], feed_dict=feed)
_,rects = get_multiclass_rectangles(H, np_pred_confidences, np_pred_boxes, rnn_len=H['rnn_len'])
if len(rects)>0:
# pick = NMS(rects)
pick = rects
print len(rects),len(pick)
current_frame.rects=pick
frameNr=frameNr+1
video_info.insert(len(video_info), current_frame)
print len(current_frame.rects)
else: skipped=skipped+1
else: skipped=skipped+1
print("Skipped %d Black Frames"%skipped)
#### END TENSORBOX CODE ###
return video_info
