def vdoLoop(self):
with self.graph.as_default():
while not self.stopEvent.is_set():
obj = {}
frame = cv2.imread(
f'/home/palm/PycharmProjects/seven/data1/1/1.jpg')
draw = frame.copy()
image = preprocess_image(frame)
image, scale = resize_image(image, min_side=720, max_side=1280)
boxes, scores, labels = self.model.predict_on_batch(
np.expand_dims(image, axis=0))
boxes /= scale
for box, score, label in zip(boxes[0], scores[0], labels[0]):
if score < 0.9:
break
b = box.astype(int)
draw = add_bbox(draw, b, label, self.labels_to_names,
score)
if label not in obj:
obj[label] = 0
obj[label] += 1
blk = ImageTk.PhotoImage(
Image.fromarray(cv2.resize(draw, (360, 640))[..., ::-1]))
if self.weight > 10:
color = 'chartreuse3' if abs(
self.get_weight(obj) -
self.weight) < 20 else 'orangered'
else:
color = 'cornflower blue'
if self.panel is None:
self.panel = tk.Label(image=blk,
borderwidth=0,
highlightthickness=3,
highlightbackground=color)
self.panel.image = blk
self.panel.grid(row=0, column=1, padx=2, pady=2)
else:
self.panel.configure(image=blk,
highlightthickness=3,
relief="solid",
highlightbackground=color)
self.panel.image = blk
self.predictionLabel.config(state='normal')
self.predictionLabel.delete(1.0, tk.END)
self.predictionLabel.insert(
tk.END, f"Obj{' ' * 7}Qty{' ' * 7}Ttl wt. \n")
os.makedirs(
f'/home/palm/PycharmProjects/seven2/xmls/readjusted/{set_name}/',
exist_ok=True)
shutil.copy(
os.path.join(anns_path, i[:-4] + '.xml'),
f'/home/palm/PycharmProjects/seven2/xmls/readjusted/{set_name}/'
+ i[:-4] + '.xml')
continue
image = read_image_bgr(os.path.join(folder, i))
start_time = time.time()
# copy to draw ong
draw = image.copy()
draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
# preprocess image for network
image = preprocess_image(image)
image, scale = resize_image(image, min_side=720, max_side=1280)
# process image
boxes, scores, labels = prediction_model.predict_on_batch(
np.expand_dims(image, axis=0))
# correct for image scale
boxes /= scale
root = ET.Element('annotation')
ET.SubElement(root, 'filename').text = i
ET.SubElement(root, 'path').text = os.path.join(folder, i)
size = ET.SubElement(root, 'size')
ET.SubElement(size, 'width').text = str(draw.shape[1])
ET.SubElement(size, 'height').text = str(draw.shape[0])
def preprocess_image(self, inputs):
""" Takes as input an image and prepares it for being passed through the network.
"""
return preprocess_image(inputs, mode='tf')
for file in os.listdir('/media/palm/BiggerData/mine/new/v/'):
cap = cv2.VideoCapture(
os.path.join('/media/palm/BiggerData/mine/new/v/', file))
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
out = cv2.VideoWriter(f'/media/palm/BiggerData/mine/out/{file}.mp4',
fourcc, 20.0, (1333, 750))
f = 0
while cap.isOpened():
ret, frame = cap.read()
if frame is None:
break
frame = cv2.resize(frame, (1333, 750))
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
image = preprocess_image(frame)
boxes, scores, labels = prediction_model.predict_on_batch(
np.expand_dims(image, axis=0))
for box, score, label in zip(boxes[0], scores[0], labels[0]):
# scores are sorted so we can break
if score < 0.5:
break
b = box.astype(int)
frame = add_bbox(frame, b, label, ['crane', 'car', 'human'],
score)
out.write(frame)
cap.release()
out.release()
cv2.destroyAllWindows()
