def updateGUI(self):
"""
update the GUI based on img_index
:return:
"""
row = self.df.iloc[self.img_index]
# update path
new_path = "{0}/{1}.jpg".format(row.trial, row.img_id)
# update path_lbl
new_lbl = "Path: {0}/{1}.jpg".format(row.trial, row.img_id)
self.path_lbl.configure(text=new_lbl)
# update pager
new_text = "{0}/{1}".format(self.img_index + 1, self.n_img)
self.pager_lbl.configure(text=new_text)
# update status
self.status_lbl.configure(text=str(row.status))
# update truth and predicted labels
new_x = "x:\t{0:5.1f} {1:5.1f}".format(row.xt, row.xp)
self.x_lbl.configure(text=new_x)
new_y = "y:\t{0:5.1f} {1:5.1f}".format(row.yt, row.yp)
self.y_lbl.configure(text=new_y)
new_w = "w:\t{0:5.1f} {1:5.1f}".format(row.wt, row.wp)
self.w_lbl.configure(text=new_w)
new_h = "h:\t{0:5.1f} {1:5.1f}".format(row.ht, row.hp)
self.h_lbl.configure(text=new_h)
new_ang = "a:\t{0:5.1f} {1:5.1f}".format(row.angt, row.angp)
self.a_lbl.configure(text=new_ang)
# update image holder
# load image
img = cv2.imread(new_path, cv2.IMREAD_GRAYSCALE)
truth = [row.xt, row.yt, row.wt, row.ht, row.angt]
pred = [row.xp, row.yp, row.wp, row.hp, row.angp]
# update thumbnails before manipulation
s_img = np.asarray(img, dtype=np.uint8)
s_img = Image.fromarray(s_img, 'L')
self.photo_s = ImageTk.PhotoImage(image=s_img)
self.canvas_s.itemconfig(self.image_refs, image=self.photo_s)
# Update the labeled image
img = annotator((120, 120, 120), img, *pred) # gray
img = annotator((0, 250, 0), img, *truth) # Green
img = numpy2pil(img)
img = img.resize((576, 576), )
self.photo = ImageTk.PhotoImage(image=img)
self.canvas.itemconfig(self.image_ref, image=self.photo)
def capture(self):
row = self.df.iloc[self.img_index]
new_path = "{0}/{1}.jpg".format(row.trial, row.img_id)
img = cv2.imread(new_path, cv2.IMREAD_GRAYSCALE)
truth = [row.xt, row.yt, row.wt, row.ht, row.angt]
pred = [row.xp, row.yp, row.wp, row.hp, row.angp]
# Update the labeled image
img = annotator((120, 120, 120), img, *pred) # gray
img = annotator((0, 250, 0), img, *truth) # Green
save_path = new_path.replace("/", "-")
cv2.imwrite("purifier/" + save_path, img)
def main(m_type, m_name, logger, video_path=None, write_output=True):
with tf.Session() as sess:
# load best model
model = load_model(sess, m_type, m_name, logger)
# check input source is a file or camera
if video_path == None:
video_path = 0
# load the video or camera
cap = cv2.VideoCapture(video_path)
ret = True
counter = 0
tic = time.time()
frames = []
preds = []
while ret:
ret, frame = cap.read()
if ret:
# Our operations on the frame come here
frames.append(frame)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
f_shape = frame.shape
if frame.shape[0] != 192:
frame = rescale(frame)
image = gray_normalizer(frame)
image = change_channel(image, config["input_channel"])
[p] = model.predict(sess, [image])
x, y, w = upscale_preds(p, f_shape)
preds.append([x, y, w])
# frames.append(gray)
counter += 1
toc = time.time()
print("{0:0.2f} FPS".format(counter / (toc - tic)))
# get the video size
video_size = frames[0].shape[0:2]
if write_output:
# prepare a video write to show the result
video = cv2.VideoWriter("predicted_video.avi",
cv2.VideoWriter_fourcc(*"XVID"), 15,
(video_size[1], video_size[0]))
for i, img in enumerate(frames):
labeled_img = annotator((0, 250, 0), img, *preds[i])
video.write(labeled_img)
# close the video
cv2.destroyAllWindows()
video.release()
print("Done...")
def capture(self):
row = self.current_df.iloc[self.img_index]
img = cv2.imread(row.path, cv2.IMREAD_GRAYSCALE)
truth = [row.xt, row.yt, row.wt, row.ht, row.angt]
# Update the labeled image
img = annotator((0, 250, 0), img, *truth) # Green
save_path = row.path.replace("/", "-")
cv2.imwrite("purifier/" + save_path, img)
def video_creator(video_name, images, labels, fps=15):
"""
get a list of images and their corresponidng labels and create a labeled video
:param video_name: the output video name
:param images: test images with shape (192,192,1) which should squeezed before writing the video
:param labels: predicted labels with a value between 0-1
:return: None
"""
f_size = images[0].shape
video = cv2.VideoWriter(video_name + " pred.mp4",
cv2.VideoWriter_fourcc(*'MP4V'), fps,
(f_size[1], f_size[0]))
for img, lbl in zip(images, labels):
img = np.squeeze(img)
# img = gray_denormalizer(img)
annotated_img = annotator((0, 250, 0), img, *lbl)
video.write(annotated_img)
cv2.destroyAllWindows()
video.release()
print("{} video has been created successfully".format(video_name))
def updateGUI(self):
"""
update the GUI based on img_index
:return:
"""
row = self.current_df.iloc[self.img_index]
# update pager
new_text = "{0}/{1}".format(self.img_index + 1, self.n_img)
self.pager_lbl.configure(text=new_text)
# update status
self.status_lbl.configure(text=str(row.status))
# update image holder
# load image
file = row.path.split(".")[0]
file = file + ".bmp"
if row.status == 2:
img = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
else:
img = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
# update thumbnails before manipulation
s_img = np.asarray(img, dtype=np.uint8)
s_img = Image.fromarray(s_img, 'L')
self.photo_s = ImageTk.PhotoImage(image=s_img)
self.canvas_s.itemconfig(self.image_refs, image=self.photo_s)
# resize image 3x and put label on it
img = cv2.resize(img, (576, 576))
truth = [row.xt * 3, row.yt * 3, row.wt * 3, row.ht * 3, row.angt]
img = annotator((0, 250, 0), img, *truth) # Green
img = numpy2pil(img)
self.photo = ImageTk.PhotoImage(image=img)
self.canvas.itemconfig(self.image_ref, image=self.photo)