def single_image_object_counting(input_video, detection_graph, category_index, is_color_recognition_enabled, fps, width, height):
total_passed_vehicle = 0
speed = "waiting..."
direction = "waiting..."
size = "waiting..."
color = "waiting..."
counting_mode = "..."
width_heigh_taken = True
height = 0
width = 0
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
detection_boxes = 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.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
input_frame = cv2.imread(input_video)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(input_frame, axis=0)
# Actual detection.
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# insert information text to video frame
font = cv2.FONT_HERSHEY_SIMPLEX
# Visualization of the results of a detection.
counter, csv_line, counting_mode = vis_util.visualize_boxes_and_labels_on_single_image_array(1,input_frame,
1,
is_color_recognition_enabled,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=4)
if(len(counting_mode) == 0):
cv2.putText(input_frame, "...", (10, 35), font, 0.8, (0,255,255),2,cv2.FONT_HERSHEY_SIMPLEX)
else:
cv2.putText(input_frame, counting_mode, (10, 35), font, 0.8, (0,255,255),2,cv2.FONT_HERSHEY_SIMPLEX)
cv2.imshow('object counting',input_frame)
cv2.waitKey(0)
return counting_mode
def detectSingleImage(self, input_video, detection_graph, category_index,
is_color_recognition_enabled):
counting_mode = "..."
if "http" in input_video:
img = cv2.VideoCapture(input_video)
if (img.isOpened()):
ret, input_frame = img.read()
else:
input_frame = cv2.imread(input_video)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(input_frame, axis=0)
# Actual detection.
(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})
# insert information text to video frame
font = cv2.FONT_HERSHEY_SIMPLEX
# Visualization of the results of a detection.
counter, csv_line, counting_mode = vis_util.visualize_boxes_and_labels_on_single_image_array(
1,
input_frame,
1,
is_color_recognition_enabled,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=4)
if (len(counting_mode) == 0):
cv2.putText(input_frame, "...", (10, 35), font, 0.8, (0, 255, 255),
2, cv2.FONT_HERSHEY_SIMPLEX)
else:
cv2.putText(input_frame, counting_mode, (10, 35), font, 0.8,
(0, 255, 255), 2, cv2.FONT_HERSHEY_SIMPLEX)
status = cv2.imwrite(str(input_video), input_frame)
# _,final_img = cv2.imencode(".jpeg", input_frame)
while status == False:
cv2.waitKey(100)
return counting_mode
image_expanded = np.expand_dims(image, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_expanded})
# Draw the results of the detection (aka 'visulaize the results')
counter, csv_line, counting_mode = vis_util.visualize_boxes_and_labels_on_single_image_array(
1,
image,
1,
0,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
min_score_thresh=0.3,
line_thickness=8)
boxs = np.squeeze(boxes)
score = np.squeeze(scores)
pre_class = counting_mode.split(':')
# pre_class = pre_class.replace("'","")
font = cv2.FONT_HERSHEY_SIMPLEX
if (len(counting_mode) == 0):
cv2.putText(image, "...", (10, 35), font, 0.8, (0, 255, 255), 2,
cv2.FONT_HERSHEY_SIMPLEX)
else:
cv2.putText(image, counting_mode, (10, 35), font, 0.8, (0, 255, 255), 2,
def single_image_object_counting(input_video, detection_graph, category_index,
is_color_recognition_enabled):
total_passed_vehicle = 0
speed = "waiting..."
direction = "waiting..."
size = "waiting..."
color = "waiting..."
counting_mode = "..."
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
detection_boxes = 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.
detection_scores = detection_graph.get_tensor_by_name(
'detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name(
'detection_classes:0')
num_detections = detection_graph.get_tensor_by_name(
'num_detections:0')
if "http" in input_video:
img = cv2.VideoCapture(input_video)
if (img.isOpened()):
ret, input_frame = img.read()
elif type(input_video) == numpy.ndarray:
input_frame = cv2.imdecode(input_video, cv2.IMREAD_COLOR)
else:
input_frame = cv2.imread(input_video)
print("CHRCKRKHRNCJCDZ")
print(input_video)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(input_frame, axis=0)
# Actual detection.
(boxes, scores, classes,
num) = sess.run([
detection_boxes, detection_scores, detection_classes,
num_detections
],
feed_dict={image_tensor: image_np_expanded})
# insert information text to video frame
font = cv2.FONT_HERSHEY_SIMPLEX
# Visualization of the results of a detection.
counter, csv_line, counting_mode = vis_util.visualize_boxes_and_labels_on_single_image_array(
1,
input_frame,
1,
is_color_recognition_enabled,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=4)
if (len(counting_mode) == 0):
cv2.putText(input_frame, "...", (10, 35), font, 0.8,
(0, 255, 255), 2, cv2.FONT_HERSHEY_SIMPLEX)
else:
cv2.putText(input_frame, counting_mode, (10, 35), font, 0.8,
(0, 255, 255), 2, cv2.FONT_HERSHEY_SIMPLEX)
#cv2.imshow('tensorflow_object counting_api',input_frame)
#cv2.waitKey(0)
# status = cv2.imwrite(str(input_video),input_frame)
_, final_img = cv2.imencode(".jpeg", input_frame)
# while status == False :
# cv2.waitKey(100)
return counting_mode, final_img
def food_detection():
MODEL_NAME = 'ssdlite_mobilenet_v2_coco_2018_05_09'
# object
PATH_TO_CKPT = os.path.join(CWD_PATH, MODEL_NAME, 'food_detection',
'frozen_inference_graph.pb')
# Label
PATH_TO_LABELS = os.path.join(CWD_PATH, 'data', 'food_detection',
'labelmap.pbtxt')
# Path to image
take_photo()
NUM_CLASSES = 1
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name(
'detection_classes:0')
# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
image = cv2.imread(PATH_TO_IMAGE)
# image_dpi = Image.open(PATH_TO_IMAGE)
image_expanded = np.expand_dims(image, axis=0)
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_expanded})
counter, csv_line, counting_mode = vis_util.visualize_boxes_and_labels_on_single_image_array(
1,
image,
1,
0,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
min_score_thresh=0.3,
line_thickness=8)
pre_food_class = counting_mode.split(':')
sess.close()
tf.reset_default_graph()
cv2.destroyAllWindows()
return len(pre_food_class)
def fish_detection(): #ทำนายปลา
MODEL_NAME = 'ssdlite_mobilenet_v2_coco_2018_05_09'
# IMAGE_NAME = 'image_fish.png'
y_summax = 0
x_summax = 0
# object
PATH_TO_CKPT = os.path.join(CWD_PATH, MODEL_NAME, 'fish_detection',
'frozen_inference_graph.pb')
# Label
PATH_TO_LABELS = os.path.join(CWD_PATH, 'data', 'fish_detection',
'labelmap.pbtxt')
# Path to image
take_photo()
NUM_CLASSES = 1
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
# Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name(
'detection_classes:0')
# Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
image = cv2.imread(PATH_TO_IMAGE)
image_dpi = Image.open(PATH_TO_IMAGE)
image_expanded = np.expand_dims(image, axis=0)
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_expanded})
counter, csv_line, counting_mode = vis_util.visualize_boxes_and_labels_on_single_image_array(
1,
image,
1,
0,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
min_score_thresh=0.3,
line_thickness=8)
boxs = np.squeeze(boxes)
score = np.squeeze(scores)
pre_class = counting_mode.split(':')
# print(pre_class)
# pre_class = pre_class.replace("'","")
font = cv2.FONT_HERSHEY_SIMPLEX
if (len(counting_mode) == 0):
cv2.putText(image, "...", (10, 35), font, 0.8, (0, 255, 255), 2,
cv2.FONT_HERSHEY_SIMPLEX)
else:
cv2.putText(image, counting_mode, (10, 35), font, 0.8, (0, 255, 255),
2, cv2.FONT_HERSHEY_SIMPLEX)
height, width, d = image.shape
for i in range(0, len(score)):
# print(boxes[[0], [i]])
if (score[i] >= 0.3):
ymin, xmin, ymax, xmax = boxs[i]
x1 = int(xmin * width)
x2 = int(xmax * width)
y1 = int(ymin * height)
y2 = int(ymax * height)
xmax, ymax = convert_pixel2inch(x2, y2, image_dpi)
y_summax += ymax
x_summax += xmax
pre_class[2] = pre_class[2].strip()
if int(pre_class[2]) > 1:
x_avg_summax = x_summax / len(score)
y_avg_summax = y_summax / len(score)
print(str(xmax), str(ymax), str(pre_class[2]))
sess.close()
tf.reset_default_graph()
cv2.destroyAllWindows()
return xmax, ymax, int(pre_class[2])
