def __init__(self, path_to_ckpt):
self.reid = Reid()
self.path_to_ckpt = path_to_ckpt
#self.module = import_module('run')
self.detection_graph = tf.Graph()
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.default_graph = self.detection_graph.as_default()
self.sess = tf.Session(graph=self.detection_graph)
# Definite input and output Tensors for detection_graph
self.image_tensor = self.detection_graph.get_tensor_by_name(
'image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.detection_boxes = self.detection_graph.get_tensor_by_name(
'detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
self.detection_scores = self.detection_graph.get_tensor_by_name(
'detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name(
'num_detections:0')
def __init__(self, path_to_ckpt):
self.reid = Reid()
self.path_to_ckpt = path_to_ckpt
self.detection_graph = tf.Graph()
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.default_graph = self.detection_graph.as_default()
self.sess = tf.Session(graph=self.detection_graph)
self.image_tensor = self.detection_graph.get_tensor_by_name(
'image_tensor:0')
self.detection_boxes = self.detection_graph.get_tensor_by_name(
'detection_boxes:0')
self.detection_scores = self.detection_graph.get_tensor_by_name(
'detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name(
'num_detections:0')
class DetectorAPI:
def __init__(self, path_to_ckpt):
self.reid = Reid()
self.path_to_ckpt = path_to_ckpt
#self.module = import_module('run')
self.detection_graph = tf.Graph()
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.default_graph = self.detection_graph.as_default()
self.sess = tf.Session(graph=self.detection_graph)
# Definite input and output Tensors for detection_graph
self.image_tensor = self.detection_graph.get_tensor_by_name(
'image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.detection_boxes = self.detection_graph.get_tensor_by_name(
'detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
self.detection_scores = self.detection_graph.get_tensor_by_name(
'detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name(
'num_detections:0')
def processFrame(self, image):
# Expand dimensions since the trained_model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image, axis=0)
# Actual detection.
start_time = time.time()
(boxes, scores, classes,
num) = self.sess.run([
self.detection_boxes, self.detection_scores,
self.detection_classes, self.num_detections
],
feed_dict={self.image_tensor: image_np_expanded})
end_time = time.time()
print("Elapsed Time:", end_time - start_time)
im_height, im_width, _ = image.shape
boxes_list = [None for i in range(boxes.shape[1])]
for i in range(boxes.shape[1]):
boxes_list[i] = (int(boxes[0, i, 0] * im_height),
int(boxes[0, i, 1] * im_width),
int(boxes[0, i, 2] * im_height),
int(boxes[0, i, 3] * im_width))
return boxes_list, scores[0].tolist(), [
int(x) for x in classes[0].tolist()
], int(num[0])
def close(self):
self.sess.close()
self.default_graph.close()
def find(self, img, box):
cv2.imwrite('./temporaryImg.jpg', img)
past_ppl = './past_ppl'
folders = os.listdir(past_ppl)
for folder in folders:
files = os.listdir(past_ppl + '/' + folder)
for f in files:
ret = self.reid.compare('./temporaryImg.jpg',
'./past_ppl/' + folder + '/' + f)
if (ret == True):
person_no = len(files) + 1
cv2.imwrite(
past_ppl + '/' + folder + '/' + str(person_no) +
'.jpg', img)
return
l = len(folders)
os.makedirs(past_ppl + '/' + str(l))
cv2.imwrite(past_ppl + '/' + str(l) + '/1.jpg', img)
return
cv2.rectangle(image, obj[0], obj[1], (0, 255, 0), 2)
cv2.putText(image, '{}: {:.2f}'.format(obj[3], obj[2]),
(obj[0][0], obj[0][1] - 5), cv2.FONT_HERSHEY_PLAIN,
1, (255, 0, 0), 1)
box0.append(obj[0])
box1.append(obj[1])
return image, box0, box1
if __name__ == '__main__':
detector = ObjectDetectorLite()
source = 0
# source = '/home/varat/myPERSON_RE_ID/dataset_videos/video2.mp4'
reid = Reid()
if len(sys.argv) > 1:
source = sys.argv[1]
cap = cv2.VideoCapture(source)
# vs = webcamVideoStream(source).start()
frame_count = 0
tt_opencvDnn = 0
while (True):
t = time.time()
hasFrame, frame = cap.read()
# frame = vs.read()
# if not hasFrame:
# break
class DetectorAPI:
def __init__(self, path_to_ckpt):
self.reid = Reid()
self.path_to_ckpt = path_to_ckpt
self.detection_graph = tf.Graph()
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.default_graph = self.detection_graph.as_default()
self.sess = tf.Session(graph=self.detection_graph)
self.image_tensor = self.detection_graph.get_tensor_by_name(
'image_tensor:0')
self.detection_boxes = self.detection_graph.get_tensor_by_name(
'detection_boxes:0')
self.detection_scores = self.detection_graph.get_tensor_by_name(
'detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name(
'detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name(
'num_detections:0')
def process_frame(self, image):
image_np_expanded = np.expand_dims(image, axis=0)
(boxes, scores, classes,
num) = self.sess.run([
self.detection_boxes, self.detection_scores,
self.detection_classes, self.num_detections
],
feed_dict={self.image_tensor: image_np_expanded})
im_height, im_width, _ = image.shape
boxes_list = [None for _ in range(boxes.shape[1])]
for i in range(boxes.shape[1]):
boxes_list[i] = (int(boxes[0, i, 0] * im_height),
int(boxes[0, i, 1] * im_width),
int(boxes[0, i, 2] * im_height),
int(boxes[0, i, 3] * im_width))
return boxes_list, scores[0].tolist(), [
int(x) for x in classes[0].tolist()
], int(num[0])
def close(self):
self.sess.close()
self.default_graph.close()
def find(self, img, boxes_cur, boxes_prev, box):
cv2.imwrite('./temporaryImg.jpg', img)
past_ppl = './past_ppl'
folders = os.listdir(past_ppl)
for folder in folders:
files = os.listdir(past_ppl + '/' + folder)
same = 0
diff = 0
num_of_files = len(files)
for f in range(num_of_files):
if f % 10 != 0:
continue
ret = self.reid.compare(
'./temporaryImg.jpg',
'./past_ppl/' + folder + '/' + str(f + 1) + '.jpg')
if ret:
same += 1
else:
diff += 1
p = 100 * float(same) / float(same + diff)
if p > 70:
person_no = len(files) + 1
cv2.imwrite(
past_ppl + '/' + folder + '/' + str(person_no) + '.jpg',
img)
boxes_cur[int(folder)][0] = box
boxes_prev[int(folder)] = -1
return folder
person_no = len(folders)
os.makedirs(past_ppl + '/' + str(person_no))
cv2.imwrite(past_ppl + '/' + str(person_no) + '/1.jpg', img)
boxes_cur.append([box])
return person_no
class DetectorAPI:
def __init__(self, path_to_ckpt):
self.reid = Reid()
self.path_to_ckpt = path_to_ckpt
#self.module = import_module('run')
self.detection_graph = tf.Graph()
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.default_graph = self.detection_graph.as_default()
self.sess = tf.Session(graph=self.detection_graph)
# Definite input and output Tensors for detection_graph
self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
self.detection_boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
self.detection_scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
self.detection_classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
self.num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')
def processFrame(self, image):
# Expand dimensions since the trained_model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image, axis=0)
# Actual detection.
start_time = time.time()
(boxes, scores, classes, num) = self.sess.run(
[self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections],
feed_dict={self.image_tensor: image_np_expanded})
end_time = time.time()
#print("Elapsed Time:", end_time-start_time)
im_height, im_width,_ = image.shape
boxes_list = [None for i in range(boxes.shape[1])]
for i in range(boxes.shape[1]):
boxes_list[i] = (int(boxes[0,i,0] * im_height),
int(boxes[0,i,1]*im_width),
int(boxes[0,i,2] * im_height),
int(boxes[0,i,3]*im_width))
return boxes_list, scores[0].tolist(), [int(x) for x in classes[0].tolist()], int(num[0])
def close(self):
self.sess.close()
self.default_graph.close()
'''
Version0 serves as ground truth .It finds a match for the person and returns the matching folder number. If no match is found, it creates a new folder and returns the match for that.
'''
def version0(self, img):
cv2.imwrite('./temporaryImg_test0.jpg',img)
past_ppl = './past_ppl_test_version0'
folders = os.listdir(past_ppl)
for folder in folders:
files = os.listdir(past_ppl + '/' + folder)
for f in files:
ret = self.reid.compare('./temporaryImg_test0.jpg' , './past_ppl_test_version0/' + folder + '/' + f)
if(ret == True):
person_no = len(files) + 1
cv2.imwrite(past_ppl + '/' + folder + '/' + str(person_no) + '.jpg',img)
return int(folder)
l = len(folders)
os.makedirs(past_ppl + '/' + str( l ) )
cv2.imwrite(past_ppl + '/' + str( l ) + '/1.jpg',img)
return l
'''
Version0 serves as ground truth
'''
def isSamePerson(self, img1, img2):
cv2.imwrite('./temporaryImg_test0.jpg',img1)
cv2.imwrite('./temporaryImg_test1.jpg',img2)
ret = self.reid.compare('./temporaryImg_test0.jpg' , './temporaryImg_test1.jpg')
return ret
def find(self, img, boxes_cur, box):
cv2.imwrite('./temporaryImg_test1.jpg',img)
past_ppl = './past_ppl_test_version1'
folders = os.listdir(past_ppl)
for folder in folders:
files = os.listdir(past_ppl + '/' + folder)
for f in files:
ret = self.reid.compare('./temporaryImg_test1.jpg' , './past_ppl_test_version1/' + folder + '/' + f)
#ret = run(past_ppl + '/' + folder + '/' + f , './temporaryImg.jpg')
if(ret == True):
person_no = len(files) + 1
cv2.imwrite(past_ppl + '/' + folder + '/' + str(person_no) + '.jpg',img)
boxes_cur[ int(folder) ] = box
return int(folder)
l = len(folders)
os.makedirs(past_ppl + '/' + str( l ) )
cv2.imwrite(past_ppl + '/' + str( l ) + '/1.jpg',img)
boxes_cur.append( box )
return -1
'''
This function is called when the tracking mechanism has tracked a person.
This code tests if the track was correct or not
'''
def tester(self, img_cur, img_prev):
cv2.imwrite('./temporaryImg_cur.jpg',img_cur)
cv2.imwrite('./temporaryImg_prev.jpg',img_prev)
return self.reid.compare('./temporaryImg_cur.jpg' , './temporaryImg_prev.jpg')
