def demo(self, pt):
img_ori = cv2.imread(pt)
if self.resize:
img, resize_ratio, dw, dh = letterbox_resize(
img_ori, self.new_size[0], self.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(self.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] - 127.5
boxes_, scores_, labels_ = self.sess.run(
[self.boxes, self.scores, self.labels],
feed_dict={self.input_data: img})
# rescale the coordinates to the original image
if letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(new_size[1]))
for i in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[i].astype(np.int)
cv2.rectangle(img_ori, (x0, y0), (x1, y1), (0, 200, 255), 4)
res, con = HyperLPR_plate_recognition(img_ori, (x0, y0, x1, y1))
label = '置信度: {:.2f}%\n'.format(
scores_[i] * 100) + self.get_time(res)
img_ori = self.drawTest(img_ori, label, 10, 10)
cv2.imshow('result', img_ori)
cv2.waitKey(0)
def demo(input_image):
img_ori = cv2.imread(input_image)
if resize:
img, resize_ratio, dw, dh = letterbox_resize(
img_ori, new_size[0], new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] - 127.5
boxes_, scores_, labels_ = sess.run(
[boxes, scores, labels], feed_dict={input_data: img})
# rescale the coordinates to the original image
if letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori/float(new_size[0]))
boxes_[:, [1, 3]] *= (height_ori/float(new_size[1]))
print("box coords:")
print(boxes_)
print('*' * 30)
print("scores:")
print(scores_)
print('*' * 30)
print("labels:")
print(labels_)
for i in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[i]
plot_one_box(img_ori, [x0, y0, x1, y1], label=classes[labels_[
i]] + ', {:.2f}%'.format(scores_[i] * 100), color=color_table[labels_[i]])
cv2.imshow('result', img_ori)
cv2.waitKey(0)
def GetBoundingBox(self, img_ori, half):
from utils.data_aug import letterbox_resize
if img_ori.shape[2] == 4:
img_ori = img_ori[:, :, :3]
if half: # Grab left half of the image
height, width = img_ori.shape[:2]
start_row, start_col = int(0), int(0)
end_row, end_col = int(height), int(width // 2)
img_ori = img_ori[start_row:end_row, start_col:end_col]
self.height_ori, self.width_ori = img_ori.shape[:2]
# print('start resize')
if self.letterbox_resize:
img, resize_ratio, dw, dh = letterbox_resize(
img_ori, self.new_size[0], self.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(self.new_size))
# print('resize end')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
# print('run TF')
boxes_, scores_, labels_, map4_ = self.sess.run(
[self.boxes, self.scores, self.labels, self.map4],
feed_dict={self.input_data: img})
# print('after TF')
# rescale the coordinates to the original image
if self.letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(self.new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(self.new_size[1]))
return boxes_, scores_, map4_[0]
"--restore_path",
type=str,
default=
"/media/ubutnu/fc1a3be7-9b03-427e-9cc9-c4b242cefbff/YOLOv3_TensorFlow/checkpoint/model-epoch_90_step_175083_loss_0.4213_lr_1e-05",
help="The path of the weights to restore.")
args = parser.parse_args()
args.anchors = parse_anchors(args.anchor_path)
args.classes = read_class_names(args.class_name_path)
args.num_class = len(args.classes)
color_table = get_color_table(args.num_class)
img_ori = cv2.imread(args.input_image)
if args.letterbox_resize:
img, resize_ratio, dw, dh = letterbox_resize(img_ori, args.new_size[0],
args.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(args.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
with tf.Session() as sess:
input_data = tf.placeholder(tf.float32,
[1, args.new_size[1], args.new_size[0], 3],
name='input_data')
yolo_model = yolov3(args.num_class, args.anchors)
with tf.variable_scope('yolov3'):
pred_feature_maps = yolo_model.forward(input_data, False)
pred_boxes, pred_confs, pred_probs = yolo_model.predict(pred_feature_maps)
def yolodet(image_path,
anchor_path=rootpath + "/yolo/data/yolo_anchors.txt",
new_size=[416, 416],
letterbox=True,
class_name_path=rootpath + "/yolo/data/coco.names",
restore_path=rootpath + "/yolo/data/best_model"):
anchors = parse_anchors(anchor_path)
classes = read_class_names(class_name_path)
num_class = len(classes)
color_table = get_color_table(num_class)
img_ori = cv2.imread(image_path)
if letterbox:
img, resize_ratio, dw, dh = letterbox_resize(img_ori, new_size[0],
new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
with tf.Session() as sess:
input_data = tf.placeholder(tf.float32,
[1, new_size[1], new_size[0], 3],
name='input_data')
yolo_model = yolov3(num_class, anchors)
with tf.variable_scope('yolov3'):
pred_feature_maps = yolo_model.forward(input_data, False)
pred_boxes, pred_confs, pred_probs = yolo_model.predict(
pred_feature_maps)
pred_scores = pred_confs * pred_probs
boxes, scores, labels = gpu_nms(pred_boxes,
pred_scores,
num_class,
max_boxes=200,
score_thresh=0.3,
nms_thresh=0.45)
saver = tf.train.Saver()
saver.restore(sess, restore_path)
boxes_, scores_, labels_ = sess.run([boxes, scores, labels],
feed_dict={input_data: img})
# rescale the coordinates to the original image
if letterbox:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(new_size[1]))
tf.reset_default_graph()
#transform detections into 1 line (#1class,#1conf,#1xmin,#1ymin,#1max,#1ymax,#2class,#2conf,...)
boxes = []
for i in range(np.shape(boxes_)[0]):
boxes.append(labels_[i])
boxes.append(scores_[i])
boxes.extend(boxes_[i, :])
return boxes
def yolodet(anchor_path, image_path, new_size, letterbox, class_name_path, restore_path):
anchors = parse_anchors(anchor_path)
classes = read_class_names(class_name_path)
num_class = len(classes)
color_table = get_color_table(num_class)
img_ori = cv2.imread(image_path)
if letterbox:
img, resize_ratio, dw, dh = letterbox_resize(img_ori, new_size[0], new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
with tf.Session() as sess:
input_data = tf.placeholder(tf.float32, [1, new_size[1], new_size[0], 3], name='input_data')
yolo_model = yolov3(num_class, anchors)
with tf.variable_scope('yolov3'):
pred_feature_maps = yolo_model.forward(input_data, False)
pred_boxes, pred_confs, pred_probs = yolo_model.predict(pred_feature_maps)
pred_scores = pred_confs * pred_probs
boxes, scores, labels = gpu_nms(pred_boxes, pred_scores, num_class, max_boxes=200, score_thresh=0.3, nms_thresh=0.45)
saver = tf.train.Saver()
saver.restore(sess, restore_path)
boxes_, scores_, labels_ = sess.run([boxes, scores, labels], feed_dict={input_data: img})
# rescale the coordinates to the original image
if letterbox:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori/float(new_size[0]))
boxes_[:, [1, 3]] *= (height_ori/float(new_size[1]))
# print("box coords:")
# print(boxes_)
# print('*' * 30)
# print("scores:")
# print(scores_)
# print('*' * 30)
# print("labels:")
# print(labels_)
#
# for i in range(len(boxes_)):
# x0, y0, x1, y1 = boxes_[i]
# plot_one_box(img_ori, [x0, y0, x1, y1], label=classes[labels_[i]] + ', {:.2f}%'.format(scores_[i] * 100), color=color_table[labels_[i]])
# cv2.imshow('Detection result', img_ori)
# cv2.imwrite('detection_result.jpg', img_ori)
# cv2.waitKey(0)
tf.reset_default_graph()
return boxes_, scores_, labels_
feature_map_1 = sess.graph.get_tensor_by_name(
'yolov4tiny/head/feature_map_1:0')
feature_map_2 = sess.graph.get_tensor_by_name(
'yolov4tiny/head/feature_map_2:0')
pred_boxes, pred_confs, pred_probs = yolo_model.predict(
[feature_map_1, feature_map_2])
pred_scores = pred_confs * pred_probs
boxes, scores, labels = gpu_nms(pred_boxes,
pred_scores,
class_num,
max_boxes=200,
score_thresh=0.3,
nms_thresh=0.45)
img_ori = cv2.imread("./data/demo_data/messi.jpg")
img, resize_ratio, dw, dh = letterbox_resize(img_ori, img_size[0], img_size[1])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
boxes_, scores_, labels_ = sess.run([boxes, scores, labels],
feed_dict={input: img})
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
for i in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[i]
plot_one_box(img_ori, [x0, y0, x1, y1],
label=classes[labels_[i]] +
', {:.2f}%'.format(scores_[i] * 100),
color=color_table[labels_[i]])
def test_display_one_img(img_path):
print(img_path)
img_ori = cv2.imread(img_path)
print(img_ori.shape)
if args.letterbox_resize:
img, resize_ratio, dw, dh = letterbox_resize(img_ori, args.new_size[0],
args.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(args.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
with sess_yolo.as_default():
with graph_yolo.as_default():
boxes_, scores_, labels_ = sess_yolo.run(
[boxes, scores, labels], feed_dict={input_data: img})
# rescale the coordinates to the original image
if args.letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(args.new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(args.new_size[1]))
for j in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[j]
x0 = np.maximum(x0, 0)
y0 = np.maximum(y0, 0)
x1 = np.maximum(x1, 0)
y1 = np.maximum(y1, 0)
label_index = labels_[j]
# Crop the detected traffic signs
if x1 - x0 > 10 and y1 - y0 > 10 and labels_[j] == 0:
# img_ori_ = cv2.cvtColor(img_ori, cv2.COLOR_BGR2RGB).astype(np.float32)
img_cropped = img_ori[int(y0):int(y1), int(x0):int(x1)]
if img_cropped.shape[0] < 10 or img_cropped.shape[1] < 10:
continue
# cv2.imwrite('D:/Data/TrafficSigns/test/test_{}.png'.format(j), img_cropped)
img_cropped = cv2.resize(img_cropped,
(params.image_size, params.image_size))
img_cropped = cv2.cvtColor(img_cropped, cv2.COLOR_BGR2RGB)
img_cropped = img_cropped / 255.0
# print(img_cropped)
# np.savetxt('D:/Data/test_result/img.txt', img_cropped, fmt='%f', delimiter=',')
if img_cropped.any():
# tf.reset_default_graph()
# new_graph = tf.Graph()
with graph_triplet.as_default():
with sess_triplet.as_default():
image_input = test_input_fn(img_cropped, params)
image_input = sess_triplet.run(image_input)
label_index = sess_triplet.run(
predict_labels, feed_dict={inputs: image_input})
label_index = label_index[0] + 3
print(label_index)
# with open('D:/Data/test_result/outputs.txt', 'w') as ff:
# ff.writelines(ff)
# np.savetxt('D:/Data/test_result/outputs.txt', out, fmt='%f', delimiter=',')
plot_one_box(img_ori, [x0, y0, x1, y1],
label_index=label_index,
label=args.classes_all[label_index] +
', {:.2f}%'.format(scores_[j] * 100),
color=color_table[labels_[j]])
cv2.namedWindow('Detection result', 0)
cv2.resizeWindow('Detection result', 2400, 1800)
cv2.imshow('Detection result', img_ori)
cv2.imwrite('detection_result.jpg', img_ori)
cv2.waitKey(0)
def test_one_img(img_path):
img_ori = cv2.imread(img_path)
img_name = img_path.strip().split('\\')[-1]
img_name = img_name.split('.')[0]
if args.letterbox_resize:
img, resize_ratio, dw, dh = letterbox_resize(img_ori, args.new_size[0],
args.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(args.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
with sess_yolo.as_default():
with graph_yolo.as_default():
boxes_, scores_, labels_ = sess_yolo.run(
[boxes, scores, labels], feed_dict={input_data: img})
# rescale the coordinates to the original image
if args.letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(args.new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(args.new_size[1]))
for j in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[j]
x0 = np.maximum(x0, 0)
y0 = np.maximum(y0, 0)
x1 = np.maximum(x1, 0)
y1 = np.maximum(y1, 0)
label_index = labels_[j]
# Crop the detected traffic signs
# the bbox of traffic signs must be big enough
if x1 - x0 > 10 and y1 - y0 > 10 and labels_[j] == 0:
# img_ori_ = cv2.cvtColor(img_ori, cv2.COLOR_BGR2RGB).astype(np.float32)
img_cropped = img_ori[int(y0):int(y1), int(x0):int(x1)]
if img_cropped.shape[0] < 10 or img_cropped.shape[1] < 10:
continue
img_cropped = cv2.resize(img_cropped,
(params.image_size, params.image_size))
img_cropped = cv2.cvtColor(img_cropped, cv2.COLOR_BGR2RGB)
img_cropped = img_cropped / 255.0
# cv2.imwrite('D:\\Data\\TrafficSigns\\test_images\\traffic_sign_cropped\\{}_{}.jpg'.format(img_name, j), img_cropped*255.0)
# img_cropped_path = 'D:\\Data\\TrafficSigns\\test_images\\traffic_sign_cropped\\{}_{}.jpg'.format(img_name, j)
# print(img_cropped_path)
# cv2.imwrite('D:\\Data\\TrafficSigns\\test_images\\traffic_sign_cropped\\1_0.jpg', img_cropped*255.0)
if img_cropped.any():
# tf.reset_default_graph()
# new_graph = tf.Graph()
with graph_triplet.as_default():
with sess_triplet.as_default():
image_input = test_input_fn(img_cropped, params)
image_input = sess_triplet.run(image_input)
label_index = sess_triplet.run(
predict_labels, feed_dict={inputs: image_input})
label_index = label_index[0] + 3
# with open('D:/Data/test_result/detect_result_self_collect.txt', 'a+') as f:
# f.write(img_path + ' ' + str(x0) + ' ' + str(y0) + ' ' + str(x1) + ' ' + str(y1) + ' ' + str(
# label_index[0]+2) + '\n')
if isinstance(label_index, np.ndarray):
label_index = label_index[0]
with open('D:\Data\TrafficSigns\\test_images/detect_result.txt',
'a+') as f:
f.write(img_path + ' ' + str(x0) + ' ' + str(y0) + ' ' + str(x1) +
' ' + str(y1) + ' ' + str(label_index) + '\n')
def estimatePose():
parser = argparse.ArgumentParser(
description="YOLO-V3 video test procedure.")
# parser.add_argument("input_video", type=str,
# help="The path of the input video.")
parser.add_argument("--anchor_path",
type=str,
default="./data/yolo_anchors.txt",
help="The path of the anchor txt file.")
parser.add_argument(
"--new_size",
nargs='*',
type=int,
default=[416, 416],
help=
"Resize the input image with `new_size`, size format: [width, height]")
parser.add_argument("--letterbox_resize",
type=lambda x: (str(x).lower() == 'true'),
default=True,
help="Whether to use the letterbox resize.")
parser.add_argument("--class_name_path",
type=str,
default="./data/my_data/YOLOPose.names",
help="The path of the class names.")
parser.add_argument("--restore_path",
type=str,
default="./data/pose_weights/lunge_best",
help="The path of the weights to restore.")
parser.add_argument("--save_video",
type=lambda x: (str(x).lower() == 'true'),
default=True,
help="Whether to save the video detection results.")
args = parser.parse_args()
args.anchors = parse_anchors(args.anchor_path)
args.classes = read_class_names(args.class_name_path)
args.num_class = len(args.classes)
color_table = get_color_table(args.num_class)
# vid = cv2.VideoCapture(args.input_video)
vid = cv2.VideoCapture('./data/demo/lunge_03.mp4')
# vid = cv2.VideoCapture(r'C:\Users\soma\SMART_Referee\SMART_Referee_DL\data\lunge\video\lunge_03.mp4')
video_frame_cnt = int(vid.get(7))
video_width = int(vid.get(3))
video_height = int(vid.get(4))
video_fps = int(vid.get(5))
trainer_pose = pd.read_csv('./data/ground_truth/output_right.csv',
header=None)
trainer_pose = trainer_pose.loc[:, [
0, 1, 2, 3, 4, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28
]]
pca_df = trainer_pose.loc[:, [
1, 2, 3, 4, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28
]]
pca_df.loc[:,
[c for c in pca_df.columns if c % 2 ==
1]] = pca_df.loc[:, [c for c in pca_df.columns
if c % 2 == 1]] * video_width / 416
pca_df.loc[:,
[c for c in pca_df.columns if c % 2 ==
0]] = pca_df.loc[:, [c for c in pca_df.columns
if c % 2 == 0]] * video_height / 416
pca_df = pca_df.astype(int)
pca_df = pca_df.replace(0, np.nan)
pca_df = pca_df.dropna()
pca_df.describe()
pca = PCA(n_components=1)
pca.fit(pca_df)
size = [video_width, video_height]
list_p = []
waist_err = 0
critical_point = 0
past_idx = 0
startTrig = 0
cntdown = 90
t = 0
TRLEN = len(trainer_pose)
modify_ankle = pca_df.iloc[0, :].values
base_rect = [(int(video_width / 4), int(video_height / 10)),
(int(video_width * 3 / 4), int(video_height * 19 / 20))]
c_knee = c_waist = c_speed = 0
if args.save_video:
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
videoWriter = cv2.VideoWriter('video_result.mp4', fourcc, video_fps,
(video_width, video_height))
with tf.Session() as sess:
input_data = tf.placeholder(tf.float32,
[1, args.new_size[1], args.new_size[0], 3],
name='input_data')
yolo_model = yolov3(args.num_class, args.anchors)
with tf.variable_scope('yolov3'):
pred_feature_maps = yolo_model.forward(input_data, False)
pred_boxes, pred_confs, pred_probs = yolo_model.predict(
pred_feature_maps)
pred_scores = pred_confs * pred_probs
boxes, scores, labels = gpu_nms(pred_boxes,
pred_scores,
args.num_class,
max_boxes=200,
score_thresh=0.3,
nms_thresh=0.45)
saver = tf.train.Saver()
saver.restore(sess, args.restore_path)
for i in range(video_frame_cnt):
ret, img_ori = vid.read()
if args.letterbox_resize:
img, resize_ratio, dw, dh = letterbox_resize(
img_ori, args.new_size[0], args.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(args.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
start_time = time.time()
boxes_, scores_, labels_ = sess.run([boxes, scores, labels],
feed_dict={input_data: img})
# rescale the coordinates to the original image
if args.letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(args.new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(args.new_size[1]))
people_pose = get_people_pose(boxes_, labels_,
base_rect) # list-dict
people_pose = np.array([p[1] for p in people_pose[0]
]).flatten() # dict-tuple -> list
people_pose = people_pose[[
0, 1, 2, 3, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27
]]
# Start Trigger
if startTrig == 2:
pass
elif startTrig == 0: # start
# 기준 박스
cv2.rectangle(img_ori, base_rect[0], base_rect[1], (0, 0, 255),
2)
if isInBox(people_pose, base_rect[0], base_rect[1]):
# t_resize_pose = resize_pose(people_pose, trainer_pose.iloc[0, 1:].values)
t_resize_pose = resize_pose(people_pose,
pca_df.iloc[0, :].values)
img_ori = draw_ground_truth(img_ori, t_resize_pose)
# img_ori = draw_ground_truth(img_ori, pca_df.iloc[0, :].values)
startTrig = isStart(people_pose,
trainer_pose.iloc[0, 1:].values, size)
cv2.imshow('image', img_ori)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
continue
else:
print("박스안에 들어와주세요!!")
continue
elif startTrig == 1:
img_ori = draw_ground_truth(img_ori, pca_df.iloc[0, :].values)
cv2.putText(img_ori, str(int(cntdown / 30)), (100, 300),
cv2.FONT_HERSHEY_SIMPLEX, 10, (255, 0, 0), 10)
cv2.imshow('image', img_ori)
cntdown -= 1
if cntdown == 0:
startTrig = 2
if cv2.waitKey(1) & 0xFF == ord('q'):
break
continue
'''check ankle : 편차 40이상 발생시 전에 값 으로 업데이트'''
people_pose = check_ankle(list_p, people_pose, modify_ankle, size)
# f = open('user.csv', 'a', encoding='utf-8', newline='')
# wr = csv.writer(f)
# wr.writerow(people_pose)
# ground truth 그리기
list_p.append(people_pose)
img_ori = draw_ground_truth(img_ori, pca_df.iloc[t, :].values)
if check_waist(people_pose):
waist_err += 1
if waist_err is 60: # waist_err는 60번 틀리면 피드백함
feedback_waist()
c_waist += 1
waist_err = 0
if trainer_pose.iloc[t, 0] == 1: # t는 특정 시점 + i frame
critical_point += 1
if critical_point % 2 == 0:
my_pose = makeMypose_df(list_p)
c_speed = check_speed(
my_pose, trainer_pose.iloc[past_idx:t + 1, 1:], pca,
c_speed)
c_knee = check_knee(people_pose, c_knee)
modify_ankle = list_p[-1]
list_p = []
past_idx = t
t += 1
if t == TRLEN:
break
# img_ori = draw_body(img_ori, boxes_, labels_)
# for i in range(len(boxes_)):
# x0, y0, x1, y1 = boxes_[i]
# plot_one_box(img_ori, [x0, y0, x1, y1], label=args.classes[labels_[i]] + ', {:.2f}%'.format(scores_[i] * 100), color=color_table[labels_[i]])
# 사용자 자세 그리기
# img_ori = draw_truth(img_ori, people_pose)
end_time = time.time()
cv2.putText(img_ori,
'{:.2f}ms'.format((end_time - start_time) * 1000),
(40, 40),
0,
fontScale=1,
color=(0, 255, 0),
thickness=2)
cv2.imshow('image', img_ori)
if args.save_video:
videoWriter.write(img_ori)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
vid.release()
cv2.destroyAllWindows()
if args.save_video:
videoWriter.release()
f = open('./data/score/result.csv', 'a', encoding='utf-8', newline='')
wr = csv.writer(f)
d = datetime.today().strftime("%Y/%m/%d")
t = datetime.today().strftime("%H:%M:%S")
wr.writerow([d, t, c_knee, c_waist, c_speed])
def test_display_one_img(img_path):
img_ori = cv2.imread(img_path)
if args.letterbox_resize:
img, resize_ratio, dw, dh = letterbox_resize(img_ori, args.new_size[0],
args.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(args.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
with sess_yolo.as_default():
with graph_yolo.as_default():
boxes_, scores_, labels_ = sess_yolo.run(
[boxes, scores, labels], feed_dict={input_data: img})
# rescale the coordinates to the original image
if args.letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(args.new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(args.new_size[1]))
for j in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[j]
x0 = np.maximum(x0, 0)
y0 = np.maximum(y0, 0)
x1 = np.maximum(x1, 0)
y1 = np.maximum(y1, 0)
label_index = labels_[j]
# Crop the detected traffic signs
if x1 - x0 > 10 and y1 - y0 > 10 and labels_[j] == 0:
img_ori_ = cv2.cvtColor(img_ori,
cv2.COLOR_BGR2RGB).astype(np.float32)
img_cropped = img_ori_[int(y0):int(y1), int(x0):int(x1)]
if img_cropped.any():
tf.reset_default_graph()
new_graph = tf.Graph()
with new_graph.as_default():
with tf.Session(graph=new_graph) as new_sess:
siamese_model = SiameseNet()
siamese_model.load_weights(
'/home/tracy/PycharmProjects/SiameseNet/checkpoint/RGBscaled/best/my_model'
)
img1, img2 = dataloader(img_cropped)
label_pred, label_score, _ = siamese_model.prediction(
img1, img2)
label_pred_, label_score_ = new_sess.run(
[label_pred, label_score])
# with sess_siam.as_default():
# with sess_siam.graph.as_default():
# img1, img2 = dataloader(img_cropped)
# label_pred, label_score, _ = siamese_model.prediction(img1, img2)
# label_pred_, label_score_ = sess_siam.run([label_pred, label_score])
# cv2.imwrite('/home/tracy/YOLOv3_TensorFlow/temp/' + str(i) + '_' + str(j) + '.jpg', img_cropped)
# print("Writting %s"%img)
# test_one_img('/home/tracy/data/TrafficSign_test/Images1/' + img)
# print('Done writing %s'%img)
# Choose the one label with highest score
pred_labels = np.nonzero(label_pred_)
pred_scores = label_score_[pred_labels]
# print("pred_scores: ", pred_scores)
if len(pred_scores) > 0:
label_index = np.argmax(pred_scores)
label_index = pred_labels[0][label_index] + 2
# labels_[j] = label_index
plot_one_box(img_ori, [x0, y0, x1, y1],
label_index=label_index,
label=args.classes_all[label_index] +
', {:.2f}%'.format(scores_[j] * 100),
color=color_table[labels_[j]])
cv2.namedWindow('Detection result', 0)
cv2.resizeWindow('Detection result', 2400, 1800)
cv2.imshow('Detection result', img_ori)
cv2.imwrite('detection_result.jpg', img_ori)
cv2.waitKey(0)
async def detection(self, img_ori, mode, detection_marker):
if self.letterbox_resizes:
img, resize_ratio, dw, dh = letterbox_resize(
img_ori, self.new_size[0], self.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(self.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
start = time.time()
boxes_, scores_, labels_ = self.sess.run(
[self.boxes, self.scores, self.labels],
feed_dict={self.input_data: img})
end = time.time()
print(end - start)
# rescale the coordinates to the original image
if self.letterbox_resizes:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(self.new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(self.new_size[1]))
# sort -- tracker for each person
dets = []
if len(boxes_) > 0:
for i in range(len(boxes_)):
x, y, w, h = boxes_[i]
dets.append([x, y, x + w, y + h, scores_[i]])
# np.set_printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)})
dets = np.asarray(dets)
tracks = self.tracker.update(dets)
new_boxes = []
indexIDs = []
previous = self.memory.copy()
self.memory = {}
for track in tracks:
new_boxes.append([track[0], track[1], track[2], track[3]])
indexIDs.append(int(track[4]))
self.memory[indexIDs[-1]] = new_boxes[-1]
if len(new_boxes) > 0:
i = 0
for box in new_boxes:
x = int(box[0])
y = int(box[1])
w = int(box[2])
h = int(box[3])
color = [
int(c) for c in self.COLORS[indexIDs[i] % len(self.COLORS)]
]
if indexIDs[i] in previous:
previous_box = previous[indexIDs[i]]
(x2, y2) = (int(previous_box[0]), int(previous_box[1]))
(w2, h2) = (int(previous_box[2]), int(previous_box[3]))
p0 = (int(x + 10), int(y + 100))
p1 = (int(x2 + 10), int(y2 + 100))
cv2.line(img_ori, p0, p1, color, 3) # tracker line
if intersect(p0, p1,
(detection_marker.X1, detection_marker.Y1),
(detection_marker.X2, detection_marker.Y2)):
self.counter += 1
if mode == 'PH':
if self.classes[
labels_[i]] == 'PHV' or self.classes[
labels_[i]] == 'PH':
pass
else:
self.violation += 1
elif mode == 'PV':
if self.classes[
labels_[i]] == 'PHV' or self.classes[
labels_[i]] == 'PV':
pass
else:
self.violation += 1
elif self.classes[labels_[i]] != mode:
self.violation += 1
i += 1
cv2.line(img_ori, (detection_marker.X1, detection_marker.Y1),
(detection_marker.X2, detection_marker.Y2), (0, 255, 255), 3)
for i in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[i]
plot_one_box(img_ori, [x0, y0, x1, y1],
label=self.classes[labels_[i]] +
', {:.2f}%'.format(scores_[i] * 100),
color=self.color_table[labels_[i]])
if mode == 'PH':
if self.classes[labels_[i]] == 'PHV' or self.classes[
labels_[i]] == 'PH':
pass
else:
cv2.putText(img_ori, 'Please wear: a helmet', (550, 40), 0,
1, (0, 0, 255), 2)
elif mode == 'PV':
if self.classes[labels_[i]] == 'PHV' or self.classes[
labels_[i]] == 'PV':
pass
else:
cv2.putText(img_ori, 'Please wear: a safety vest',
(550, 40), 0, 1, (0, 0, 255), 2)
elif mode == 'PLC' and self.classes[labels_[i]] != 'PLC':
cv2.putText(img_ori, 'Please wear: a lab coat', (550, 40), 0,
1, (0, 0, 255), 2)
elif mode == 'PHV' and self.classes[labels_[i]] != 'PHV':
cv2.putText(img_ori, 'Please wear: a helmet and a safety vest',
(550, 40), 0, 1, (0, 0, 255), 2)
elif self.classes[labels_[i]] != mode:
cv2.putText(img_ori, 'Please wear: ' + str(mode), (550, 40), 0,
1, (0, 0, 255), 2)
# print({'TotalViolation': self.violation,'TotalPeople':self.counter})
# cv2.putText(img_ori, mode+' Mode', (300, 40), 0,
# fontScale=1, color=(0, 255, 0), thickness=2)
# cv2.putText(img_ori, 'People Count: '+ str(self.counter), (40, 620), 0,
# 1, (255,255,255), 2)
# cv2.putText(img_ori, 'Violation Count: '+ str(self.violation), (40, 660), 0,
# 1, (255,255,255), 2)
return {
'TotalViolation': self.violation,
'TotalPeople': self.counter
}, img_ori
def video_detect(input_args):
vid = cv2.VideoCapture(input_args.input_video)
video_frame_cnt = int(vid.get(7))
video_width = int(vid.get(3))
video_height = int(vid.get(4))
video_fps = int(vid.get(5))
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
video_writer = cv2.VideoWriter(pred_args.output_video, fourcc, video_fps, (video_width, video_height))
with tf.Session() as sess:
input_data = tf.placeholder(tf.float32, [1, pred_args.new_size[1], pred_args.new_size[0], 3], name='input_data')
yolo_model = yolov3(pred_args.num_class, pred_args.anchors)
with tf.variable_scope('yolov3'):
pred_feature_maps = yolo_model.forward(input_data, False)
pred_boxes, pred_confs, pred_probs = yolo_model.predict(pred_feature_maps)
pred_scores = pred_confs * pred_probs
boxes, scores, labels = gpu_nms(
pred_boxes, pred_scores, pred_args.num_class,
max_boxes=200, score_thresh=0.3, nms_thresh=0.45
)
saver = tf.train.Saver()
saver.restore(sess, pred_args.weight_path)
for i in range(video_frame_cnt):
ret, img_ori = vid.read()
if input_args.use_letterbox_resize:
img, resize_ratio, dw, dh = letterbox_resize(img_ori, pred_args.new_size[0], pred_args.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(pred_args.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
start_time = time.time()
boxes_, scores_, labels_ = sess.run([boxes, scores, labels], feed_dict={input_data: img})
end_time = time.time()
if input_args.use_letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(pred_args.new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(pred_args.new_size[1]))
for i in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[i]
plot_one_box(img_ori, [x0, y0, x1, y1],
label=pred_args.classes[labels_[i]] + ', {:.2f}%'.format(scores_[i] * 100),
color=pred_args.color_table[labels_[i]])
cv2.putText(
img_ori, '{:.2f}ms'.format((end_time - start_time) * 1000),
(40, 40), 0, fontScale=1, color=(0, 255, 0), thickness=2
)
cv2.imshow('Detection result', img_ori)
video_writer.write(img_ori)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
vid.release()
video_writer.release()
def img_detect(input_args):
"""
图片检测
:param input_args:
:return:
"""
img_ori = cv2.imread(input_args.input_image) # opencv 打开
if input_args.use_letterbox_resize:
img, resize_ratio, dw, dh = letterbox_resize(img_ori, pred_args.new_size[0], pred_args.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(pred_args.new_size))
# img 转RGB, 转float, 归一化
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
sess = tf.Session()
input_data = tf.placeholder(
tf.float32, [1, pred_args.new_size[1], pred_args.new_size[0], 3], name='input_data'
)
with tf.variable_scope('yolov3'):
yolo_model = yolov3(pred_args.num_class, pred_args.anchors)
pred_feature_maps = yolo_model.forward(input_data, False)
pred_boxes, pred_confs, pred_probs = yolo_model.predict(pred_feature_maps)
pred_scores = pred_confs * pred_probs
boxes, scores, labels = gpu_nms(
pred_boxes, pred_scores, pred_args.num_class,
max_boxes=200, score_thresh=0.3, nms_thresh=0.45)
saver = tf.train.Saver()
saver.restore(sess, pred_args.weight_path)
boxes_, scores_, labels_ = sess.run([boxes, scores, labels], feed_dict={input_data: img})
# 还原坐标到原图
if input_args.use_letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(pred_args.new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(pred_args.new_size[1]))
print('box coords:', boxes_, '\n' + '*' * 30)
print('scores:', scores_, '\n' + '*' * 30)
print('labels:', labels_)
for i in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[i]
plot_one_box(
img_ori, [x0, y0, x1, y1],
label=pred_args.classes[labels_[i]] + ', {:.2f}%'.format(scores_[i] * 100),
color=pred_args.color_table[labels_[i]]
)
cv2.imshow('Detection result', img_ori)
cv2.imwrite(pred_args.output_image, img_ori)
cv2.waitKey(0)
sess.close()
def single_image_test(imgname):
parser = argparse.ArgumentParser(
description="YOLO-V3 test single image test procedure.")
parser.add_argument("--input_image",
type=str,
default="./static/uploads/beforeimg/" + imgname,
help="The path of the input image.")
parser.add_argument("--anchor_path",
type=str,
default="./data/yolo_anchors.txt",
help="The path of the anchor txt file.")
parser.add_argument(
"--new_size",
nargs='*',
type=int,
default=[416, 416],
help=
"Resize the input image with `new_size`, size format: [width, height]")
parser.add_argument("--letterbox_resize",
type=lambda x: (str(x).lower() == 'true'),
default=True,
help="Whether to use the letterbox resize.")
parser.add_argument("--class_name_path",
type=str,
default="./data/coco.names",
help="The path of the class names.")
parser.add_argument("--restore_path",
type=str,
default="./data/darknet_weights/yolov3.ckpt",
help="The path of the weights to restore.")
args = parser.parse_args()
args.anchors = parse_anchors(args.anchor_path)
args.classes = read_class_names(args.class_name_path)
args.num_class = len(args.classes)
color_table = get_color_table(args.num_class)
img_ori = cv2.imread(args.input_image)
if args.letterbox_resize:
img, resize_ratio, dw, dh = letterbox_resize(img_ori, args.new_size[0],
args.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(args.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
with tf.Session() as sess:
input_data = tf.placeholder(tf.float32,
[1, args.new_size[1], args.new_size[0], 3],
name='input_data')
yolo_model = yolov3(args.num_class, args.anchors)
with tf.variable_scope('yolov3'):
pred_feature_maps = yolo_model.forward(input_data, False)
pred_boxes, pred_confs, pred_probs = yolo_model.predict(
pred_feature_maps)
pred_scores = pred_confs * pred_probs
boxes, scores, labels = gpu_nms(pred_boxes,
pred_scores,
args.num_class,
max_boxes=200,
score_thresh=0.3,
nms_thresh=0.45)
saver = tf.train.Saver()
saver.restore(sess, args.restore_path)
boxes_, scores_, labels_ = sess.run([boxes, scores, labels],
feed_dict={input_data: img})
# rescale the coordinates to the original image
if args.letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(args.new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(args.new_size[1]))
print("box coords:")
print(boxes_)
print('*' * 30)
print("scores:")
print(scores_)
print('*' * 30)
print("labels:")
print(labels_)
for i in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[i]
plot_one_box(img_ori, [x0, y0, x1, y1],
label=args.classes[labels_[i]] +
', {:.2f}%'.format(scores_[i] * 100),
color=color_table[labels_[i]])
#cv2.imshow('Detection result', img_ori)
cv2.imwrite('static/uploads/afterimg/' + imgname, img_ori)
#cv2.waitKey(0)
doc = []
doc.append("发现：")
item = ["安全帽", "未带安全帽的人"]
if (len(labels_) == 0):
doc.append("什么都没有发现。")
else:
for i in range(len(labels_)):
doc.append(item[labels_[i]] + ",范围：" + str(boxes_[i]) +
",可能性为：" + str(scores_[i]))
return doc
def detect_in_video(video_path):
# VideoWriter is the responsible of creating a copy of the video
# used for the detections but with the detections overlays. Keep in
# mind the frame size has to be the same as original video.
# out = cv2.VideoWriter('../temp/' + 'WIN_20191218_11_03_57_Pro.mp4', cv2.VideoWriter_fourcc(
# 'M', 'J', 'P', 'G'), 10, (1280, 720))
if is_yolo:
print('yolo!')
configuration = tf.ConfigProto(device_count={"GPU": 0})
sess = tf.Session(config=configuration)
input_data = tf.placeholder(tf.float32,
[1, new_size[1], new_size[0], 3],
name='input_data')
yolo_model = yolov3(num_class, anchors)
with tf.variable_scope('yolov3'):
pred_feature_maps = yolo_model.forward(input_data, False)
pred_boxes, pred_confs, pred_probs = yolo_model.predict(
pred_feature_maps)
pred_scores = pred_confs * pred_probs
boxes, scores, labels = gpu_nms(pred_boxes,
pred_scores,
num_class,
max_boxes=1,
score_thresh=0.2,
nms_thresh=0.45)
saver = tf.train.Saver()
saver.restore(sess, restore_path)
else:
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='')
configuration = tf.ConfigProto(device_count={"GPU": 0})
sess = tf.Session(config=configuration, graph=detection_graph)
# 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')
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)
frame_statistics = []
frame_id = 1
is_skip_frame = True
frame_skip_count = 0
# Создать директорию с кадрами для заданного видео
video_base_name = os.path.basename(video_path)
video_name = os.path.splitext(video_base_name)[0]
video_dir = join(os.path.dirname(video_path), video_name)
images_dir = "images"
video_images_dir = join(video_dir, images_dir)
if not os.path.exists(video_images_dir):
os.makedirs(video_images_dir)
else:
# Удалить все кадры из целевой директории
remove_files_in_dir(video_images_dir)
video_images_dir_rat = join(video_images_dir, 'rat')
video_images_dir_mouse = join(video_images_dir, 'mouse')
os.makedirs(video_images_dir_rat, exist_ok=True)
os.makedirs(video_images_dir_mouse, exist_ok=True)
remove_files_in_dir(video_images_dir_rat)
remove_files_in_dir(video_images_dir_mouse)
# Загрузка видео
cap = cv2.VideoCapture(video_path)
video_frame_cnt = int(cap.get(7))
video_width = int(cap.get(3))
video_height = int(cap.get(4))
video_fps = int(cap.get(5))
# Узнать разрешение видео
video_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
video_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
# Указать разрешение картинок
cur_dir = os.getcwd()
os.chdir(video_images_dir)
while cap.isOpened():
# Read the frame
ret, frame = cap.read()
if frame is not None:
# Recolor the frame. By default, OpenCV uses BGR color space.
# This short blog post explains this better:
# https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/
# color_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if not is_skip_frame:
if is_yolo:
print('yoloo!!')
if is_letterbox_resize:
img, resize_ratio, dw, dh = letterbox_resize(
frame, new_size[0], new_size[1])
else:
height_ori, width_ori = frame.shape[:2]
img = cv2.resize(frame, tuple(new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
start_time = time.time()
boxes_, scores_, labels_ = sess.run(
[boxes, scores, labels], feed_dict={input_data: img})
end_time = time.time()
# rescale the coordinates to the original image
if is_letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] -
dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] -
dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori / float(new_size[0]))
boxes_[:, [1, 3]] *= (height_ori / float(new_size[1]))
for i in range(len(boxes_)):
if scores_[i] == max(scores_):
x0, y0, x1, y1 = boxes_[i]
plot_one_box(frame, [x0, y0, x1, y1],
label=classes_yolo[labels_[i]] +
', {:.2f}%'.format(scores_[i] * 100),
color=color_table[labels_[i]])
rodent_confidence = scores_[i]
rodent_class_id = labels_[i] + 1
rodent_class_name = classes_yolo[labels_[i]]
if rodent_confidence >= .20:
frame_statistics.append({
'frame_id':
frame_id,
'confidence':
rodent_confidence,
'rodent_class_id':
rodent_class_id,
'rodent_class_name':
rodent_class_name,
})
# Сохранить кадр
frame_name = rodent_class_name + '/image' + str(
frame_id) + '.jpg'
cv2.imwrite(frame_name, frame)
# Сохранить xml-файл
#scores = np.squeeze(scores[0])
#bbox_coords = boxes[0]
#writer = Writer('.', video_width, video_height)
#writer.addObject(rodent_class_name, bbox_coords[1] * video_width,
#bbox_coords[0] * video_height, bbox_coords[3] * video_width,
#bbox_coords[2] * video_height)
#writer.save('image' + str(frame_id) + '.xml')
#else:
# Сохранить кадр
#frame_name = 'image' + str(frame_id) + '.jpg'
#cv2.imwrite(frame_name, frame)
cv2.putText(frame,
'{:.2f}ms'.format(
(end_time - start_time) * 1000), (40, 40),
0,
fontScale=1,
color=(0, 255, 0),
thickness=2)
else:
image_np_expanded = np.expand_dims(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})
# Visualization of the results of a detection.
# note: perform the detections using a higher threshold
vis_util.visualize_boxes_and_labels_on_image_array(
frame,
np.squeeze(boxes[0]),
np.squeeze(classes[0]).astype(np.int32),
np.squeeze(scores[0]),
category_index,
use_normalized_coordinates=True,
line_thickness=8,
max_boxes_to_draw=1,
min_score_thresh=.20)
# rodent_confidence = np.squeeze(scores[0])[0]
# rodent_class_id = np.squeeze(classes[0]).astype(np.int32)[0]
# rodent_class_name = category_index[rodent_class_id]['name']
# if rodent_confidence > .20:
# frame_statistics.append({'frame_id': frame_id,
# 'confidence': rodent_confidence,
# 'rodent_class_id': rodent_class_id,
# 'rodent_class_name': rodent_class_name,
# })
#
# # Сохранить кадр
# frame_name = rodent_class_name + '/image' + str(frame_id) + '.jpg'
# cv2.imwrite(frame_name, frame)
#
# # Сохранить xml-файл
# scores = np.squeeze(scores[0])
# for i in range(min(1, np.squeeze(boxes[0]).shape[0])):
# if scores is None or scores[i] > .20:
# boxes = tuple(boxes[i].tolist())
#
# bbox_coords = boxes[0]
# writer = Writer('.', video_width, video_height)
# writer.addObject(rodent_class_name, bbox_coords[1] * video_width,
# bbox_coords[0] * video_height, bbox_coords[3] * video_width,
# bbox_coords[2] * video_height)
# writer.save('image' + str(frame_id) + '.xml')
# else:
# # Сохранить кадр
# frame_name = 'image' + str(frame_id) + '.jpg'
# cv2.imwrite(frame_name, frame)
cv2.imshow('frame', cv2.resize(frame, (800, 600)))
output_rgb = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
# out.write(output_rgb
# Пропустить кадр, если необходимо
if is_skip_frame:
while 1:
key = cv2.waitKey(1)
if key == 32: # Нажата клавиша "space"
frame_skip_count += 1
print("Вы пропустили " + str(frame_skip_count) +
" кадр")
break
elif key == 113 or key == 233: # Нажата клавиша 'q' ('й')
is_skip_frame = False
break
if cv2.waitKey(1) & 0xFF == ord('q'):
break
frame_id += 1
# out.release()
os.chdir(cur_dir)
cap.release()
cv2.destroyAllWindows()
statistics = {
'frame_count': frame_id, # Количество кадров
'frame_skip_count': frame_skip_count, # Количество пропущенных кадров
'frame_rodent_count': 0, # Количество кадров с грызуном
'frame_rat_count': 0, # Количество кадров с крысой
'frame_mouse_count': 0, # Количество кадров с мышью
'sum_confidence_rat': 0, # Сумма вероятностей крысы на видео
'sum_confidence_mouse': 0, # Сумма вероятностей мыши на видео
'mean_confidence_rat': 0, # Средняя вероятность крысы на видео
'mean_confidence_mouse': 0 # Средняя вероятность мыши на видео
}
for frame_statistic in frame_statistics:
if frame_statistic['rodent_class_name'] == 'rat':
statistics['frame_rodent_count'] += 1
statistics['frame_rat_count'] += 1
statistics['sum_confidence_rat'] += frame_statistic['confidence']
statistics['mean_confidence_rat'] = statistics[
'sum_confidence_rat'] / statistics['frame_rat_count']
elif frame_statistic['rodent_class_name'] == 'mouse':
statistics['frame_rodent_count'] += 1
statistics['frame_mouse_count'] += 1
statistics['sum_confidence_mouse'] += frame_statistic['confidence']
statistics['mean_confidence_mouse'] = statistics[
'sum_confidence_mouse'] / statistics['frame_mouse_count']
print('----->>> Результаты обнаружения <<<-----')
print('Количество кадров: ' + str(statistics['frame_count']))
print('Количество пропущенных кадров: ' +
str(statistics['frame_skip_count']))
print('Количество кадров с грызуном: ' +
str(statistics['frame_rodent_count']))
print('Количество кадров с крысой: ' + str(statistics['frame_rat_count']))
print('Количество кадров с мышью: ' + str(statistics['frame_mouse_count']))
print('Средняя вероятность крысы на видео: ' +
str(statistics['mean_confidence_rat']))
print('Средняя вероятность мыши на видео: ' +
str(statistics['mean_confidence_mouse']))
