print("press 'q' to finish the data collecting")
feature_vectors = []
while (cap.isOpened()):
ret, frame = cap.read()
if (not ret):
break
yolo.set_image(frame)
yolo.start()
cv2.imshow('k-means training processing', yolo.img_detection)
for i in range(len(yolo.final_objects)):
if (yolo.final_classes_names[i] == class_name):
feature_vectors.append(
nhog.HOG(im.fromarray(yolo.final_objects[i])).vector)
if (cv2.waitKey(1) & 0xFF == ord('q')):
break
cap.release()
cv2.destroyAllWindows()
if (len(feature_vectors) == 0):
print(
"Nothing you want detected! Pleas adjust your camera or the object position!"
)
else:
samples = []
for vector in feature_vectors:
def start(self):
is_on = False
cap = cv2.VideoCapture(0)
ret, last_frame = cap.read()
# set up model object
yolov2 = YOLONetv2()
yolo = YOLONet()
while(cap.isOpened()):
ret, frame = cap.read()
if (not ret):
break
mean_difference = self.diffimage(last_frame, frame)
if(mean_difference > self.key_frame_threshold and is_on):
detect_time = self.detected_time()
yolo.set_image(frame)
yolov2.set_image(frame)
yolo.detect_from_image()
yolov2.start()
cv2.imshow('yolov2', yolov2.img_detection)
cv2.imshow('yolov1', yolo.final_with_bounding_box)
if(not self.last_key_flag):
self.last_key_flag = True
last_key_features = []
for detected_object in yolov2.final_objects:
try:
last_key_features.append(nhog.HOG(im.fromarray(detected_object)).vector)
except ValueError:
# undo everthing
last_key_features = []
self.last_key_flag = False
break
else:
curr_features = []
for frame_object in yolov2.final_objects:
try:
curr_features.append(nhog.HOG(im.fromarray(frame_object)).vector)
except ValueError:
curr_features = []
self.last_key_flag = False
break
compare_times = min(len(last_key_features), len(curr_features))
similarities = []
for i in range(compare_times):
similarities.append(nhog.calc_similarity(last_key_features[i], curr_features[i]))
#print("similarity of object %d is %f" % (i, similarities[i]))
last_key_features = curr_features
else:
self.last_key_flag = False
cv2.imshow('yolov2', frame)
cv2.imshow('yolov1', frame)
last_frame = frame
if (cv2.waitKey(1) &0xFF == ord('q')):
print("pressed q")
break
elif(cv2.waitKey(1) &0xFF == ord('s')):
print("pressed s")
is_on = not is_on
cap.release()
cv2.destroyAllWindows()
def show_frame_rd(self):
_, last_frame = self.cap.read()
is_last_keyframe = False
last_key_features = []
while (self.is_switch_on):
if (self.is_video_on_3):
try:
_, frame = self.cap.read()
except BaseException:
continue
mean_difference = self.diffimage(last_frame, frame)
if (self.is_detection_on_3
and mean_difference > self.key_frame_threshold):
self.yolo_v3.set_image(frame)
self.yolo_v3.start()
detected_time = self.detected_time()
# compare the objects with those in the last key frame
if (not is_last_keyframe):
is_last_keyframe = True
num_objects = len(self.yolo_v3.final_objects)
for i in range(
num_objects): # self.yolo_v3.final_objects:
try:
last_key_features.append(
nhog.HOG(
im.fromarray(self.yolo_v3.
final_objects[i])).vector)
self.db.insert(detected_time, self.yolo_v3.final_classes_names[i], \
'yolo v3', self.yolo_v3.final_relative_positions[i][0], self.yolo_v3.final_relative_positions[i][1],\
self.yolo_v3.final_relative_positions[i][2], self.yolo_v3.final_relative_positions[i][3],\
nhog.HOG(im.fromarray(self.yolo_v3.final_objects[i])).vector)
self.message_listbox.insert(
'end',
"[%s]yolo v3 store an object tuple into the database"
% detected_time)
except ValueError:
# undo everthing
last_key_features = []
is_last_keyframe = False
break
else:
curr_features = []
for frame_object in self.yolo_v3.final_objects:
try:
curr_features.append(
nhog.HOG(
im.fromarray(frame_object)).vector)
except ValueError:
curr_features = []
is_last_keyframe = False
break
compare_times = min(len(last_key_features),
len(curr_features))
similarities = []
for i in range(compare_times):
similarities.append(
nhog.calc_similarity(last_key_features[i],
curr_features[i]))
# print("similarity of object %d is %f" % (i, similarities[i]))
self.message_listbox.insert(
'end',
"[%s]yolo v3 detected %s %d, similarity is %f"
% (detected_time,
self.yolo_v3.final_classes_names[i], i,
similarities[i]))
if (similarities[i] < 0.5):
self.db.insert(detected_time, self.yolo_v3.final_classes_names[i], \
'yolo v3', self.yolo_v3.final_relative_positions[i][0], self.yolo_v3.final_relative_positions[i][1],\
self.yolo_v3.final_relative_positions[i][2], self.yolo_v3.final_relative_positions[i][3],\
nhog.HOG(im.fromarray(self.yolo_v3.final_objects[i])).vector)
self.message_listbox.insert(
'end',
"[%s]yolo v3 store an object tuple into the database"
% detected_time)
last_key_features = copy.deepcopy(curr_features)
cv2image = cv2.cvtColor(self.yolo_v3.img_detection,
cv2.COLOR_BGR2RGBA)
else:
cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)
is_last_keyframe = False
last_key_features = []
img = Image.fromarray(cv2image)
imgtk = ImageTk.PhotoImage(image=img)
self.video_rd.imgtk = imgtk
self.video_rd.configure(image=imgtk)
last_frame = frame