def setup():
# global yolo, all_classes
yolo = YOLO(0.6, 0.5)
file = 'data/VTAS_YoloV3_coco_classes.txt'
# file = 'data/coco_classes.txt'
all_classes = get_classes(file)
return yolo, all_classes
def run(self):
yolo = YOLO(0.3, 0.5)
file = 'data/coco_classes.txt'
all_classes = get_classes(file)
f2 = 'test8.jpg'
path2 = 'images/Snapshots/' + f2
image2 = cv2.imread(path2)
image2, boxes_multi_tracking = detect_image(image2, yolo, all_classes)
cv2.imwrite('images/res/' + f2, image2)
print("ok1")
t_prev = time.time()
while self.threading_event.wait():
(success, boxes_multi_tracking) = self.trackers.update(
self.door_to_heaven.get_frame())
self.door_to_heaven.tracker_update(boxes_multi_tracking)
if time.time() - t_prev > 1:
print(time.time() - t_prev)
t_prev = time.time()
# créer encore un nouveau thread qui effctue cette tâche
image, boxes_yolo_detection = detect_image(
self.door_to_heaven.get_frame(), yolo, all_classes)
replayDetection = self.door_to_heaven.reliable_tracking(
boxes_multi_tracking, image, boxes_yolo_detection)
def load_saved_artifacts():
print("loading saved artifacts...start")
global yolo
global all_classes
if yolo is None or all_classes is None:
yolo = YOLO(0.6, 0.5)
all_classes = get_classes('data/coco_classes.txt')
print("loading saved artifacts...done")
def detect_objects_live(yolo=YOLO(0.6, 0.5),
all_classes=get_classes('data/coco_classes.txt')):
cv2.namedWindow('window_frame')
cap = cv2.VideoCapture(0) # Webcam sourceK
while cap.isOpened():
bgr_image, gray_image, rgb_image = get_images_from_webcam(cap)
im = detect_image(bgr_image, yolo, all_classes)
cv2.imshow("detection", im)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def pictures_test():
yolo = YOLO(0.6, 0.5)
file = 'data/coco_classes.txt'
all_classes = get_classes(file)
# detect images in test floder.
for (root, dirs, files) in os.walk('images/test'):
if files:
for f in files:
print(f)
path = os.path.join(root, f)
image = cv2.imread(path)
image = detect_image(image, yolo, all_classes)
cv2.imwrite('images/res/' + f, image)
# detect vedio.
"""
def start(path):
yolo = YOLO(0.6, 0.5)
file = 'data/coco_classes.txt'
all_classes = get_classes(file)
image = cv2.imread(path)
image, classes = detect_image(image, yolo, all_classes)
# cv2.imwrite('images/res/' + f, image)
K.clear_session()
str = ""
if(classes is not None):
for cl in classes:
# print('class: '+ all_classes[cl])
str += all_classes[cl] + " "
return str
return str
def main(src):
yolo = YOLO(0.6, 0.5)
file = 'data/coco_classes.txt'
all_classes = get_classes(file)
# detect images in test floder.
for (root, dirs, files) in os.walk('images/test'):
if files:
for f in files:
print(f)
path = os.path.join(root, f)
image = cv2.imread(path)
image = detect_image(image, yolo, all_classes)
cv2.imwrite('images/res/' + f, image)
# detect videos one at a time in videos/test folder
video = src
detect_video(video, yolo, all_classes)
aa = "/final"
return aa
def train(img):
yolo = YOLO(0.6, 0.5)
file = 'data/coco_classes.txt'
all_classes = get_classes(file)
# image_size = 224
# img=load_img(img,target_size=(image_size,image_size))
# img_array=np.array([img_to_array(img)])
# image = cv2.imread(img)
#read image file string data
# filestr = request.files['file'].read()
#convert string data to numpy array
npimg = np.fromstring(img, np.uint8)
# convert numpy array to image
image = cv2.imdecode(npimg, cv2.IMREAD_COLOR)
# image=url_to_image(img)
# cv2.waitKey(0)
image = detect_image(image, yolo, all_classes)
return image
# train('https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1535197235311&di=4068a16bbed08b9d226304e90e83afa4&imgtype=0&src=http%3A%2F%2Fimg3.duitang.com%2Fuploads%2Fitem%2F201609%2F19%2F20160919154716_AmExk.jpeg')
tflite_interpreter.set_tensor(input_details[0]['index'], converted_image)
tflite_interpreter.invoke()
def rs(ten):
a = list(ten.shape)
a = tuple(a)
print(a)
return ten.reshape(a)
tflite_model_predictions = [
rs(tflite_interpreter.get_tensor(output_details[0]['index'])),
rs(tflite_interpreter.get_tensor(output_details[1]['index'])),
rs(tflite_interpreter.get_tensor(output_details[2]['index']))]
y = YOLO(0.6, 0.5)
w, h , c= image.shape
start = time.time()
boxes, classes, scores = y._yolo_out(tflite_model_predictions, (w,h))
end = time.time()
print('time: {0:.2f}s'.format(end - start))
all_classes = load_labels('data/coco_classes.txt')
def draw(image, boxes, scores, classes, all_classes):
for box, score, cl in zip(boxes, scores, classes):
x, y, w, h = box
print(box)
top = max(0, np.floor(x + 0.5).astype(int))
def processing_YOLOv3(main_view_videocap,
whole_view_videocap,
output_videowriter,
fps,
length,
yolo_object_threshold=0.6,
yolo_nms_threshold=0.5,
visible=False):
"""processing with YOLOv3 for FastClip 0.0.2.
# Argument:
main_view_videocap: the main view video capture.
whole_view_videocap: the whole view video capture.
output_videowriter: the output video writer.
fps: the fps of the video.
length: the total amount of frames of the video.
yolo_object_threshold: threshold for object.
yolo_nms_threshold: threshold for non maximum suppression.
# Returns:
int: tag for success or error.
0: Fastclip succeed!
1: Error: wrong filename!
2: Error: different fps!
3: Error: different resolution!
4: Error: different length!
5: Error: read frame error!
6: Error: the video is too short, which is less than 20s!
"""
total_processed_frame_count = 0
left_length = length
# Initialize YOLOv3
global yolo_model, classes_file, all_classes
if yolo_model == None:
yolo_model = YOLO(yolo_object_threshold, yolo_nms_threshold)
classes_file = 'data/coco_classes.txt'
all_classes = get_classes(classes_file)
#################################### stage 0 ####################################
# Start: Output whole view 10s
beginning_length = 10
for i in range(fps * beginning_length):
# read frame
success_main, frame_main = main_view_videocap.read()
success_whole, frame_whole = whole_view_videocap.read()
if success_main and success_whole != True:
return 5
# write to video
output_videowriter.write(frame_whole)
# visible
if visible:
cv2.imshow("test", frame_whole)
if cv2.waitKey(10) & 0xff == 27:
break
# accumulate processed frame count and left length
total_processed_frame_count += 1
left_length -= 1
print(total_processed_frame_count)
#################################### stage 1 ####################################
# Alternative view
# State to check whether the whole view could alter to main view
# Condition: main view has a person (p >= 0.90) and maintain this not less than 3 frames
# That means there is a sliding window of 3 frames
main_view_has_person_state = False
# Read and detect window 0
window_0_state = False
success_main, frame_main_0 = main_view_videocap.read()
success_whole, frame_whole_0 = whole_view_videocap.read()
boxes0, classes0, scores0 = detect_image_4_results(frame_main_0,
yolo_model, all_classes)
if classes0 is not None:
if classes0[0] == 0 and scores0[0] >= 0.90:
window_0_state = True
# Read and detect window 1
window_1_state = False
success_main, frame_main_1 = main_view_videocap.read()
success_whole, frame_whole_1 = whole_view_videocap.read()
boxes1, classes1, scores1 = detect_image_4_results(frame_main_1,
yolo_model, all_classes)
if classes1 is not None:
if classes1[0] == 0 and scores1[0] >= 0.90:
window_1_state = True
# Read and detect window 2
window_2_state = False
success_main, frame_main_2 = main_view_videocap.read()
success_whole, frame_whole_2 = whole_view_videocap.read()
boxes2, classes2, scores2 = detect_image_4_results(frame_main_2,
yolo_model, all_classes)
if classes2 is not None:
if classes2[0] == 0 and scores2[0] >= 0.90:
window_2_state = True
# check reading state
if success_main and success_whole != True:
return 6
# change window state
main_view_has_person_state = window_0_state and window_1_state and window_2_state
print("state:", main_view_has_person_state)
tail_length = 10
while left_length > fps * tail_length:
random_length_main = random.randrange(5 * fps, 10 * fps, fps)
random_length_whole = random.randrange(10 * fps, 20 * fps, fps)
# if left whole video is too short
if left_length <= fps * tail_length + random_length_main:
break
print("Main begin:")
# main view
main_view_count = 0
while main_view_count < random_length_main and left_length > fps * tail_length:
# output
output_videowriter.write(frame_main_0)
main_view_count += 1
# visible
if visible:
cv2.imshow("test", frame_main_0)
if cv2.waitKey(10) & 0xff == 27:
break
# update state
frame_main_0 = frame_main_1
frame_whole_0 = frame_whole_1
window_0_state = window_1_state
frame_main_1 = frame_main_2
frame_whole_1 = frame_whole_2
window_1_state = window_2_state
success_main, frame_main_2 = main_view_videocap.read()
success_whole, frame_whole_2 = whole_view_videocap.read()
boxes2, classes2, scores2 = detect_image_4_results(
frame_main_2, yolo_model, all_classes)
window_2_state = False
if classes2 is not None:
if classes2[0] == 0 and scores2[0] >= 0.90:
window_2_state = True
main_view_has_person_state = window_0_state and window_1_state and window_2_state
print("state:", main_view_has_person_state)
# accumulate processed frame count and left length
total_processed_frame_count += 1
left_length -= 1
print(total_processed_frame_count)
print("Main\n")
if main_view_count >= fps * 5 and main_view_has_person_state == False:
break
# whole view
print("Whole begin:")
whole_view_count = 0
while left_length > fps * tail_length:
# output
output_videowriter.write(frame_whole_0)
whole_view_count += 1
# visible
if visible:
cv2.imshow("test", frame_whole_0)
if cv2.waitKey(10) & 0xff == 27:
break
# update state
frame_main_0 = frame_main_1
frame_whole_0 = frame_whole_1
window_0_state = window_1_state
frame_main_1 = frame_main_2
frame_whole_1 = frame_whole_2
window_1_state = window_2_state
success_main, frame_main_2 = main_view_videocap.read()
success_whole, frame_whole_2 = whole_view_videocap.read()
boxes2, classes2, scores2 = detect_image_4_results(
frame_main_2, yolo_model, all_classes)
window_2_state = False
if classes2 is not None:
if classes2[0] == 0 and scores2[0] >= 0.90:
window_2_state = True
main_view_has_person_state = window_0_state and window_1_state and window_2_state
print("state:", main_view_has_person_state)
# accumulate processed frame count and left length
total_processed_frame_count += 1
left_length -= 1
print(total_processed_frame_count)
print("Whole\n")
if whole_view_count > random_length_whole and main_view_has_person_state == True:
break
#################################### stage 2 ####################################
# End: Output whole view 10s
# output the surplus frames
output_videowriter.write(frame_whole_0)
output_videowriter.write(frame_whole_1)
output_videowriter.write(frame_whole_2)
# read new frame
success_main, frame_main = main_view_videocap.read()
success_whole, frame_whole = whole_view_videocap.read()
if success_main and success_whole != True:
return 5
while left_length > 0:
# write to video
output_videowriter.write(frame_whole)
# visible
if visible:
cv2.imshow("test", frame_whole)
if cv2.waitKey(10) & 0xff == 27:
break
# read frame
success_main, frame_main = main_view_videocap.read()
success_whole, frame_whole = whole_view_videocap.read()
if success_main and success_whole != True:
return 5
# accumulate processed frame count and left length
total_processed_frame_count += 1
left_length -= 1
print(total_processed_frame_count)
output_videowriter.write(frame_whole)
#################################################################################
return 0
boxes, num_class, accuracy = yolo.predict(feed_img, img.shape)
for box, num, acc in zip(boxes, num_class, accuracy):
x, y, w, h = tuple(map(int, box))
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 3)
cv2.putText(img, classes[num] + ':' + str(acc), (x, y),
cv2.FONT_HERSHEY_SCRIPT_SIMPLEX, 1, (0, 255, 0), 3,
cv2.LINE_AA)
return img
#------
#Loading Model
#1st parameter: %Threshold for object detection
#2nd parameter: %Threshold for box detection
yolo = YOLO(0.5, 0.5)
#-----
#Display Video
cap = cv2.VideoCapture('D:/projects/highway.mp4')
_, frame = cp.read()
plt.imshow(detect(frame))
while True:
_, frame = cap.read()
frame = detect(frame, coco_classes, yolo)
cv2.imshow('obj_detection', frame)
if cv2.waitKey(30) & 0xff == 27:
break
cap.release()
cv2.destroyAllWindows()
image = detect_image(frame, yolo, all_classes)
cv2.imshow("detection", image)
# Save the video frame by frame
vout.write(image)
if cv2.waitKey(110) & 0xff == 27:
break
vout.release()
camera.release()
if __name__ == '__main__':
# Need to increase threshold and reduce NMS to get decent results with tiny
yolo = YOLO(0.75, 0.2, name='data/yolov3-tiny.h5')
file = 'data/coco_classes.txt'
all_classes = get_classes(file)
# detect images in test floder.
for (root, dirs, files) in os.walk('images/test'):
if files:
for f in files:
print(f)
path = os.path.join(root, f)
image = cv2.imread(path)
image = detect_image(image, yolo, all_classes)
cv2.imwrite('images/res/tiny_' + f, image)
# # detect videos one at a time in videos/test folder
# video = 'library1.mp4'
#cv2.imshow("detection", image)
# Save the video frame by frame
vout.write(image)
if cv2.waitKey(1) & 0xff == 27:
break
vout.release()
camera.release()
print("done")
# In[19]:
yolo = YOLO(0.25, 0.5)
file = 'data/coco_classes.txt'
all_classes = get_classes(file)
# ### Detecting Images
# In[35]:
# DEFINE MISSION:
# choose objects from classes list: data/coco_classes.txt
bad_guy = "cat"
forbidden_place = "toilet"
with open("data/coco_classes.txt") as f:
if bad_guy and forbidden_place in f.read():
print("Objects are valid")
if __name__ == '__main__':
args = parser.parse_args()
if args.model_path is not None:
model_path = os.path.expanduser(args.model_path)
assert model_path.endswith(
'.h5'), 'Model path {} is not a .h5 file'.format(model_path)
else:
model_path = 'data/yolov3-416.h5'
if args.classes_path is not None:
classes_path = os.path.expanduser(args.classes_path)
assert classes_path.endswith(
'.txt'), 'Classes path {} is not a .txt file'.format(classes_path)
else:
classes_path = 'data/coco_classes.txt'
yolo = YOLO(model_path, 0.6, 0.5)
all_classes = get_classes(classes_path)
# detect images in test floder.
for (root, dirs, files) in os.walk('images/test'):
if files:
for f in files:
print(f)
path = os.path.join(root, f)
image = cv2.imread(path)
image = detect_image(image, yolo, all_classes)
cv2.imwrite('images/res/' + f, image)
def detectobj():
import time
import numpy as np
from model.yolo_model import YOLO
def process_image(img):
image = cv2.resize(img, (416, 416), interpolation=cv2.INTER_CUBIC)
image = np.array(image, dtype='float32')
image /= 255.
image = np.expand_dims(image, axis=0)
return image
def get_classes(file):
with open(file) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def draw(image, boxes, scores, classes, all_classes):
for box, score, cl in zip(boxes, scores, classes):
x, y, w, h = box
top = max(0, np.floor(x + 0.5).astype(int))
left = max(0, np.floor(y + 0.5).astype(int))
right = min(image.shape[1], np.floor(x + w + 0.5).astype(int))
bottom = min(image.shape[0], np.floor(y + h + 0.5).astype(int))
cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
cv2.putText(image, '{0} {1:.2f}'.format(all_classes[cl], score),
(top, left - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.6,
(0, 0, 255), 1, cv2.LINE_AA)
print('class: {0}, score: {1:.2f}'.format(all_classes[cl], score))
print('box coordinate x,y,w,h: {0}'.format(box))
print()
def detect_image(image, yolo, all_classes):
pimage = process_image(image)
start = time.time()
boxes, classes, scores = yolo.predict(pimage, image.shape)
end = time.time()
print('time: {0:.2f}s'.format(end - start))
if boxes is not None:
draw(image, boxes, scores, classes, all_classes)
return image
yolo = YOLO(0.6, 0.5)
file = 'data/coco_classes.txt'
all_classes = get_classes(file)
f = path.get()
f = f.split('.')
f = f[0] + '_edited.jpg'
#path = 'C:/Users/OMKAR/YOLOv3/images/'+f
image = cv2.imread(path.get())
image = detect_image(image, yolo, all_classes)
cv2.imwrite(f, image)
imgpath = f
inputimg = resizeimg(imgpath) #we get resized image in PIL format
#inputimg = Image.open(inputimg) #takes input image in any format
inputimg = ImageTk.PhotoImage(
inputimg) #converts the image into tkinter supported format
panel.configure(image=inputimg)
panel.image = inputimg
path + '/Distortions of images {0} to {1}.npy'.format(
i, i + self.batch_size), dists)
for j in range(len(X_adv)):
io.imsave(
path + '/Best example of {1} Distortion {2}.png'.format(
self.confidence, i + j, dists[j]), X_adv[j])
r.extend(X_adv)
ds.extend(dists)
return np.array(r), np.array(ds)
if __name__ == '__main__':
sess = tf.InteractiveSession()
init = tf.global_variables_initializer()
sess.run(init)
ORACLE = YOLO(0.6, 0.5) # The auguments do not matter.
X_test = []
for (root, dirs, files) in os.walk('../Datasets/COCO/val2017/'):
if files:
for f in files:
print(f)
path = os.path.join(root, f)
image = cv2.imread(path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # RGB
image = process_image(image)
#image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
X_test.append(image)
EXAMPLE_NUM -= 1
if EXAMPLE_NUM == 0:
break
X_test = np.concatenate(X_test, axis=0)
res, frame = camera.read()
if not res:
break
image = detect_image(frame, yolo, all_classes)
cv2.imshow("detection", image)
if cv2.waitKey(110) & 0xff == 27:
break
camera.release()
if __name__ == '__main__':
yolo = YOLO(0.6, 0.5)
file = 'data/coco_classes.txt'
all_classes = get_classes(file)
# detect images in test floder.
for (root, dirs, files) in os.walk('images/test'):
if files:
for f in files:
print(f)
path = os.path.join(root, f)
image = cv2.imread(path)
image = detect_image(image, yolo, all_classes)
cv2.imwrite('images/res/' + f, image)
# detect vedio.
video = 'E:/video/car.flv'
image = detect_image(frame, yolo, all_classes)
cv2.imshow("detection", image)
# Save the video frame by frame
vout.write(image)
if cv2.waitKey(110) & 0xff == 27:
break
vout.release()
camera.release()
if __name__ == '__main__':
yolo = YOLO(0.4, 0.4)
file = 'data/coco_classes.txt'
all_classes = get_classes(file)
# detect images in test floder.
for (root, dirs, files) in os.walk('images/test'):
if files:
for f in files:
print(f)
path = os.path.join(root, f)
image = cv2.imread(path)
image = detect_image(image, yolo, all_classes)
cv2.imwrite('images/res/' + f, image)
# detect videos one at a time in videos/test folder
video = 'library1.mp4'