def push_frame(self):
print('push frame')
global ABNORMAL_DETECT_STATUS_DELTA
# # 防止多线程时 command 未被设置
# while True:
# if len(self.command) > 0:
#
# print('set pipe info')
#
# # 管道配置
# p = sp.Popen(self.command, stdin=sp.PIPE)
# break
K.clear_session()
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC' # A "Best-fit with coalescing" algorithm, simplified from a version of dlmalloc.
config.gpu_options.per_process_gpu_memory_fraction = 1
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
yolo = YOLO()
gl.clear_detect_items()
print('clear after:', gl.DETECT_TYPE)
# for camera_event_type_item in self.detect_items:
# gl.set_detect_items(camera_event_type_item)
#
# for camera_event_type_id_item in self.detect_items_index:
# gl.set_note_event_id(camera_event_type_id_item)
gl.set_detect_items('安全帽检测')
gl.set_detect_items('安全带检测')
print(gl.DETECT_TYPE)
# print(gl.NOTE_EVENT_ID)
# deep_sort
model_filename = '/Users/apple/Desktop/Git/Track/deep_sort_yolov3_lastest/model_data/mars-small128.pb'
# model_filename = 'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
# 非极大值抑制
metric = nn_matching.NearestNeighborDistanceMetric("cosine", 0.3, None)
tracker = Tracker(metric)
cnt = 0
befor_worker_status={}
while True:
try:
if self.frame_queue.empty() != True:
#print('-----------------queue size: ', self.frame_queue.qsize())
if self.frame_queue.qsize() > 5:
frame = self.frame_queue.get()
# process fr
t3 = time.time()
#frame = cv2.resize(frame, dsize=(VIDEO_HEIGHT,VIDEO_WEIGHT), interpolation=cv2.INTER_CUBIC)
print('shape:',frame.shape)
#frame = np.rot90(frame, -2)
print('resize cost:',time.time()-t3)
if frame.shape[0] < frame.shape[1]:
frame = np.rot90(frame, -1)
image = Image.fromarray(frame[..., ::-1]) # bgr to rgb
t4=time.time()
print('image convert cost:',t4-t3)
# 人框，所有目标框
boxs, other_boxs = yolo.detect_image(image)
t5=time.time()
print(cnt,' box:',other_boxs,boxs,'detect cost:',t5-t4)
frame1=copy.deepcopy(frame)
features = encoder(frame, boxs)
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxs, features)]
# Run non-maxima suppression.
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(boxes, 1.0, scores)
detections = [detections[i] for i in indices]
tracker.predict()
tracker.update(detections)
t6=time.time()
print('track decode cost:',t6-t5)
update_flag = False
current_worker_status = {}
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 1:
continue
if track.corresponding_detect == -1:
continue
b = detections[track.corresponding_detect].tlwh
bbox = [int(b[0]), int(b[1]), int(b[0] + b[2]), int(b[1] + b[3])]
bbox[0] = min(max(0,bbox[0]),frame.shape[1])
bbox[1] = min(max(0,bbox[1]),frame.shape[0])
bbox[2] = min(max(0,bbox[2]),frame.shape[1])
bbox[3] = min(max(0,bbox[3]),frame.shape[0])
# bbox = track.to_tlbr()
frame = frame.astype(np.uint8)
frame = frame.copy()
cv2.putText(frame, str(track.track_id),(int((bbox[0]+bbox[2])/2), int(bbox[1])),cv2.FONT_HERSHEY_SIMPLEX,1, (0,255,0),2)
# 安全带/安全帽匹配
t7=time.time()
single_person_box = [int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3]), track.track_id]
match_ok,current_wear_list = person_status_match(single_person_box, other_boxs, current_worker_status)
add_flag = False
note_violation_behavior(track.track_id,current_wear_list)
if track.track_id in ABNORMAL_DETECT_STATUS_DELTA.keys() and ABNORMAL_DETECT_STATUS_DELTA[track.track_id]['count']==EVNENT_NOTE_COUNT_THRESH:
add_flag, current_wear_list, violation_item, event_id_list = status_change_note(befor_worker_status, current_worker_status, track.track_id)
update_flag = True
print(ABNORMAL_DETECT_STATUS_DELTA)
if float(int(frame.shape[0]) - bbox[3]) / float(frame.shape[0]) >= CLIMB_FLAG:
# 未带安全帽
t10 = time.time()
if add_flag or 0 in current_wear_list:
frame1 = frame1.copy()
cv2.rectangle(frame1, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), color_red,2)
cv2.putText(frame1, str(track.track_id),(int((bbox[0] + bbox[2]) / 2), int(bbox[1])), cv2.FONT_HERSHEY_SIMPLEX, 1,color_green,2)
wear_list = current_worker_status[track.track_id]
for i in range(len(wear_list)):
wear_box = other_boxs[wear_list[i][1]]
cv2.rectangle(frame1, (int(wear_box[0]), int(wear_box[1])),(int(wear_box[2]), int(wear_box[3])), color_white, 2)
if add_flag:
post_data = {
# "employId": str(track.track_id),
"cameraId": str(0),
"eventTypeId": str(0),
"photo": "pic.jpg",
"photoHeight": str(VIDEO_WEIGHT),
"photoWeight": str(VIDEO_HEIGHT)
}
self.post_queue.put((frame, post_data))
print('##添加事件 ',str(track.track_id)+'未佩戴：',violation_item)
else:
frame1 = frame1.copy()
cv2.rectangle(frame1, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), color_blue,2)
cv2.putText(frame1, str(track.track_id),(int((bbox[0] + bbox[2]) / 2), int(bbox[1])), cv2.FONT_HERSHEY_SIMPLEX, 1,color_green,2)
wear_list = current_worker_status[track.track_id]
for i in range(len(wear_list)):
wear_box = other_boxs[wear_list[i][1]]
cv2.rectangle(frame1, (int(wear_box[0]), int(wear_box[1])),(int(wear_box[2]), int(wear_box[3])), color_white, 2)
t12 = time.time()
print(track.track_id,' not add cost:',t12-t10)
continue
else:
if DEBUG:
frame1=frame1.copy()
# color = colors[int(track.track_id) % len(colors)]
# color = [i * 255 for i in color]
cv2.rectangle(frame1, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), color_blue, 2)
cv2.putText(frame1, str(track.track_id),
(int((bbox[0] + bbox[2]) / 2), int(bbox[1])), cv2.FONT_HERSHEY_SIMPLEX, 1, color_green,
2)
wear_list=current_worker_status[track.track_id]
for i in range(len(wear_list)):
wear_box=other_boxs[wear_list[i][1]]
cv2.rectangle(frame1, (int(wear_box[0]), int(wear_box[1])), (int(wear_box[2]), int(wear_box[3])), color_blue, 2)
if update_flag:
befor_worker_status = current_worker_status
t9=time.time()
print('track cost:',t9-t6)
frame1 = frame1.copy()
cv2.line(frame1,(0,int((1-CLIMB_FLAG)*frame1.shape[0])),(frame1.shape[1],int((1-CLIMB_FLAG)*frame1.shape[0])),(0,255,0),3)
image_tmp = Image.fromarray(frame1)
image_tmp.show(str(cnt))
print('fps:',1.0/(t9-t3))
print('')
print('')
print('')
# write to pipe
# p.stdin.write(frame1.tostring())
except:
pass
from yolo import YOLO
yolo = YOLO()
deepsort_preprocess = DeepSortPreprocess()
deepsort = DeepSort()
video_capture = cv2.VideoCapture('../data/PETS09-S2L1.mp4')
frame_i = -1
while True:
frame_i += 1
ret, frame = video_capture.read() # frame shape 640*480*3
if ret is not True:
break
# 检测框
image_for_yolo = Image.fromarray(frame[..., ::-1]) # bgr to rgb
boxs = yolo.detect_image(image_for_yolo)
features = deepsort_preprocess.get_features(frame, boxs)
# 追踪
track_new_id_list, track_delete_id_list, not_confirmed_detected_track, detected_track = deepsort.update(
boxs, features)
for track_data in not_confirmed_detected_track:
track_id = track_data['trackID']
track_bbox = track_data['body_box']
cv2.rectangle(frame, (int(track_bbox[0]), int(track_bbox[1])),
(int(track_bbox[2]), int(track_bbox[3])),
(255, 0, 255), 2)
cv2.putText(frame, str(track_id),
(int(track_bbox[0]), int(track_bbox[1])), 0,
5e-3 * 200, (0, 255, 0), 2)
def main(model, lang):
parser = argparse.ArgumentParser(description='model_data/trained_weights_final.h5')
# modelが保存されているpathを引数で指定してください
# parser.add_argument('--model_path', default=None, help="path to saved_mode")
parser.add_argument('--model_path', default='/home/pi/dive_groupwork/yolo_test/model_data/trained_weights_final.h5', help="path to saved_mode")
# デバッグの処理
parser.add_argument('--time_debug', default=False, help="debug_mode True or False")
# 引数を読み込み
args = parser.parse_args()
# モデル+重みを読込み
# self_model = load_model(args.model_path, compile=False)
#商品価格等の情報をbottle_masterから読み込み
# name_price_dict = bottle_master.bottle_master_dict()
# _, label_dict = bottle_master.bottle_label_dirpath()
# bottle_label = {}
# for key, value in label_dict.items():
# bottle_label[value] = key
bottle_master_df = bottle_master.bottle_master_df(model="YOLO")
"""
# 言語モードを選択
while True:
print("Please select a language!!")
print()
lang = input('Kinyarwanda :「k」,English :「e」,French :「f」,Japanese :「j」')
print()
if lang not in ['k', 'e', 'f', 'j']:
print('Please input correctly')
print()
else:
break
"""
# yolo.pyのYOLOクラスをインスタンス化
yolo = YOLO()
# yoloの初回起動
init_yolo(yolo)
while True:
# 空のdfを作成
total_df = pd.DataFrame(columns=['name', 'price'])
while True:
# 「商品を置いてenterを押してください」
while True:
scene = "scan_start"
input(translate_language(lang, scene))
print()
# 時間を図る self.time_debugに応じて対応
if args.time_debug is True:
time_start = perf_counter()
# 写真撮影をしてnumpy_arrayで受け取る
image_array = shutter2image.shutter_array()
# numpy_arrayをPILへ変換してモデルの入力用に加工
img = Image.fromarray(image_array)
# 読み込んだmodelでpridictを実行
# predicted_classes = yolo_video.main(img)
# start = timer()
# _, _, predicted_classes = yolo.detect_image(img)
predict_index_list, _, _ = yolo.detect_image(img)
if predict_index_list.any():
break
else:
scene = 'not_registered'
print(translate_language(lang, scene))
print()
# end = timer()
# print(end - start)
# 時間計測
if args.time_debug is True:
time_end = perf_counter()
print('デバッグ用、本番環境ではいらない、予測されたラベルインデックスのリスト {}'.format(predict_index_list))
scene = "time"
print(translate_language(lang, scene).format(time_end-time_start))
# 予測されたindexのリストからdfを作成
predict_df = bottle_master_df.iloc[predict_index_list, [2, 3]].reset_index(drop=True)
# 小計をdiplay
scene = "read_message"
# 10/18 translate.pyの上記sceneの文言変更
# print(translate_language(self.lang, scene).format('test_bottle', random_price))
print(translate_language(lang, scene))
print()
print(predict_df)
print()
# print('商品 : {} 値段 : {} が読み取られました'.format(name, name_price_dict[name]))
while True:
scene = "correct?"
key = input(translate_language(lang, scene))
print()
if key != 'y' and key != 'n':
# 正しく入力されていません
scene = "input_error"
print(translate_language(lang, scene))
print()
else:
break
if key == 'y':
total_df = pd.concat([total_df, predict_df], axis=0).reset_index(drop=True)
print(total_df)
print()
# 小計
scene = "subtotal"
print(translate_language(lang, scene).format(total_df["price"].sum()))
print()
while True:
# 続けて商品をスキャンする場合は「y」、会計する場合は「f」、\n キャンセルする商品がある場合は「ｘ」を押して下さい。
scene = "continue?"
key = input(translate_language(lang, scene))
print()
if key != 'y' and key != 'f' and key != 'x':
# 正しく入力されていません
scene = "input_error"
print(translate_language(lang, scene))
print()
# 商品をdeleteする処理
elif key == 'x':
while True:
print(total_df)
print()
# scene設定, translate.pyへ追加
scene = 'delete'
key = input(translate_language(lang, scene).format(len(total_df)-1))
print()
key_list = [str(i) for i in range(len(total_df))] + ['n']
if key not in key_list:
# 正しく入力されていません
scene = "input_error"
print(translate_language(lang, scene))
print()
elif key == 'n':
break
else:
scene = 'delete_complete'
print(translate_language(lang, scene))
print()
print(total_df[int(key):int(key)+1])
print()
# total_dfから消去
total_df = total_df.drop(index=int(key))
# indexをreset
total_df = total_df.reset_index(drop=True)
else:
break
if key == 'f':
# 合計
print(total_df)
print()
scene = "total"
print(translate_language(lang, scene).format(total_df['price'].sum()))
print()
# ありがとうございました
scene = "thanks"
print(translate_language(lang, scene))
move = input("next step to enter :")
if move == "quit":
test = MAIN(model, lang)
test.mode_select()
else:
break
elif key == 'y':
# 次の商品を指定の位置に置いてください。
scene = "next"
print(translate_language(lang, scene))
print()
yolo = YOLO()
# 调用摄像头
capture=cv2.VideoCapture(0) # capture=cv2.VideoCapture("1.mp4")
fps = 0.0
while(True):
t1 = time.time()
# 读取某一帧
ref,frame=capture.read()
# 格式转变，BGRtoRGB
frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
# 转变成Image
frame = Image.fromarray(np.uint8(frame))
# 进行检测
frame = np.array(yolo.detect_image(frame))
# RGBtoBGR满足opencv显示格式
frame = cv2.cvtColor(frame,cv2.COLOR_RGB2BGR)
fps = ( fps + (1./(time.time()-t1)) ) / 2
print("fps= %.2f"%(fps))
frame = cv2.putText(frame, "fps= %.2f"%(fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.imshow("video",frame)
c= cv2.waitKey(30) & 0xff
if c==27:
capture.release()
break
yolo.close_session()
ymax_list = []
def get_class(classes_path):
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
for i in range(len(text_list)):
start = time.time()
print('开始检测第'+str(i+1)+'张图片。')
image_path = 'VOCdevkit/VOC2007/JPEGImages/' + text_list[i] + '.jpg'
image = Image.open(image_path)
image, label_record, score_record, top_record, left_record, bottom_record, right_record = yolo.detect_image(
image)
image.save('results_imgs/{}.png'.format(text_list[i]))
all_classes = get_class('model_data/my_classes.txt')
xmin_list.extend(left_record)
ymin_list.extend(top_record)
xmax_list.extend(right_record)
ymax_list.extend(bottom_record)
jpg = [text_list[i] + '.jpg'] * len(label_record)
jpg_list.extend(jpg)
print('第'+str(i+1)+'张图片检测完毕,检测出了'+str(len(label_record))+'个物体。')
end = time.time()
print('检测总共花费的时间为: {0:.2f}s'.format(end - start))
result = pd.DataFrame()
name_list = []
def Counting_People(path):
yolo = YOLO()
global total
global track_list
# Definition of the parameters
max_cosine_distance = 0.3
nn_budget = None
nms_max_overlap = 1.0
# deep_sort
model_filename = 'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric(
"cosine", max_cosine_distance, nn_budget)
tracker = Tracker(metric)
writeVideo_flag = True
video_capture = cv2.VideoCapture(path)
if writeVideo_flag:
# Define the codec and create VideoWriter object
w = int(video_capture.get(3))
h = int(video_capture.get(4))
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
out = cv2.VideoWriter('output.avi', fourcc, 15, (w, h))
list_file = open('detection.txt', 'w')
frame_index = -1
fps = 0.0
while True:
ret, frame = video_capture.read() # frame shape 640*480*3
if ret != True:
break
t1 = time.time()
image = Image.fromarray(frame)
boxs = yolo.detect_image(image)
# print("box_num",len(boxs))
features = encoder(frame, boxs)
# score to 1.0 here).
detections = [
Detection(bbox, 1.0, feature)
for bbox, feature in zip(boxs, features)
]
# Run non-maxima suppression.
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(
boxes, nms_max_overlap, scores)
detections = [detections[i] for i in indices]
# Call the tracker
tracker.predict()
tracker.update(detections)
for track in tracker.tracks:
if track.is_confirmed() and track.time_since_update > 1:
continue
# count=0
bbox = track.to_tlbr()
cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (255, 255, 255), 2)
cv2.putText(frame, str(track.track_id),
(int(bbox[0]), int(bbox[1])), 0, 5e-3 * 200,
(0, 255, 0), 2)
# count=count+1
# track.track_id=1
list1 = track.track_id
# print(track_list)
track_list.append(track.track_id)
# for t in track_list:
# print("Debugging",t)
# else:
# for var in list1:
# print("Debugging for TrackId value",var)
# my_list=[]
# print(track.track_id)
for det in detections:
bbox = det.to_tlbr()
cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (255, 0, 0), 2)
cv2.imshow('', frame)
if writeVideo_flag:
# save a frame
out.write(frame)
frame_index = frame_index + 1
# count=0
# count=count+1
# print(count)
list_file.write(str(frame_index) + ' ')
if len(boxs) != 0:
for i in range(0, len(boxs)):
list_file.write(
str(boxs[i][0]) + ' ' + str(boxs[i][1]) + ' ' +
str(boxs[i][2]) + ' ' + str(boxs[i][3]) + ' ')
list_file.write('\n')
fps = (fps + (1. / (time.time() - t1))) / 2
# print (my_list)
print("fps= %f" % (fps))
# Press Q to stop!
if cv2.waitKey(1) & 0xFF == ord('q'):
brea
video_capture.release()
if writeVideo_flag:
out.release()
list_file.close()
cv2.destroyAllWindows()
# print(frame_index)
# print(list_file)
# print(track_list)
total = (len(set(track_list)))
print("Total Number of People In Whole Video Are :", total)
import cv2
import numpy as np
from PIL import Image
from yolo import YOLO
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
model = YOLO(model_path="model_data/yolov3.h5",
anchors_path="model_data/yolo_anchors.txt",
classes_path="model_data/coco_classes.txt")
camera = cv2.VideoCapture(0)
while camera.isOpened():
success, frame = camera.read()
if not success:
break
img = cv2.resize(frame, (416, 416))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = Image.fromarray(img)
img = model.detect_image(img)
img = np.asarray(img)
cv2.imshow("frame", frame)
cv2.imshow("image", img)
if cv2.waitKey(1) in [ord('q'), 27]:
break
camera.release()
cv2.destroyAllWindows()
def main():
yolo = YOLO()
max_cosine_distance = 0.3
nn_budget = None
nms_max_overlap = 1.0
model_filename = 'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine",
max_cosine_distance,
nn_budget)
tracker = Tracker(metric)
parser = argparse.ArgumentParser(
description='Training codes for Openpose using Tensorflow')
parser.add_argument('--checkpoint_path',
type=str,
default='checkpoints/train/2018-12-13-16-56-49/')
parser.add_argument('--backbone_net_ckpt_path',
type=str,
default='checkpoints/vgg/vgg_19.ckpt')
parser.add_argument('--image', type=str, default=None)
# parser.add_argument('--run_model', type=str, default='img')
parser.add_argument('--video', type=str, default=None)
parser.add_argument('--train_vgg', type=bool, default=True)
parser.add_argument('--use_bn', type=bool, default=False)
parser.add_argument('--save_video', type=str, default='result/our.mp4')
args = parser.parse_args()
checkpoint_path = args.checkpoint_path
logger.info('checkpoint_path: ' + checkpoint_path)
with tf.name_scope('inputs'):
raw_img = tf.placeholder(tf.float32, shape=[None, None, None, 3])
img_size = tf.placeholder(dtype=tf.int32,
shape=(2, ),
name='original_image_size')
img_normalized = raw_img / 255 - 0.5
# define vgg19
with slim.arg_scope(vgg.vgg_arg_scope()):
vgg_outputs, end_points = vgg.vgg_19(img_normalized)
# get net graph
logger.info('initializing model...')
net = PafNet(inputs_x=vgg_outputs, use_bn=args.use_bn)
hm_pre, cpm_pre, added_layers_out = net.gen_net()
hm_up = tf.image.resize_area(hm_pre[5], img_size)
cpm_up = tf.image.resize_area(cpm_pre[5], img_size)
# hm_up = hm_pre[5]
# cpm_up = cpm_pre[5]
smoother = Smoother({'data': hm_up}, 25, 3.0)
gaussian_heatMat = smoother.get_output()
max_pooled_in_tensor = tf.nn.pool(gaussian_heatMat,
window_shape=(3, 3),
pooling_type='MAX',
padding='SAME')
tensor_peaks = tf.where(tf.equal(gaussian_heatMat, max_pooled_in_tensor),
gaussian_heatMat, tf.zeros_like(gaussian_heatMat))
logger.info('initialize saver...')
# trainable_var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='openpose_layers')
# trainable_var_list = []
trainable_var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope='openpose_layers')
if args.train_vgg:
trainable_var_list = trainable_var_list + tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope='vgg_19')
restorer = tf.train.Saver(tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope='vgg_19'),
name='vgg_restorer')
saver = tf.train.Saver(trainable_var_list)
logger.info('initialize session...')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.group(tf.global_variables_initializer()))
logger.info('restoring vgg weights...')
restorer.restore(sess, args.backbone_net_ckpt_path)
logger.info('restoring from checkpoint...')
#saver.restore(sess, tf.train.latest_checkpoint(checkpoint_dir=checkpoint_path))
saver.restore(sess, args.checkpoint_path + 'model-59000.ckpt')
logger.info('initialization done')
writeVideo_flag = True
if args.image is None:
if args.video is not None:
cap = cv2.VideoCapture(args.video)
w = int(cap.get(3))
h = int(cap.get(4))
else:
cap = cv2.VideoCapture("images/video.mp4")
#cap = cv2.VideoCapture("rtsp://*****:*****@192.168.43.51:554//Streaming/Channels/1")
#cap = cv2.VideoCapture("http://*****:*****@192.168.1.111:8081")
#cap = cv2.VideoCapture("rtsp://*****:*****@192.168.1.106:554//Streaming/Channels/1")
_, image = cap.read()
#print(_,image)
if image is None:
logger.error("Can't read video")
sys.exit(-1)
fps = cap.get(cv2.CAP_PROP_FPS)
ori_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
ori_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
#print(fps,ori_w,ori_h)
if args.save_video is not None:
fourcc = cv2.VideoWriter_fourcc(*'MP4V')
video_saver = cv2.VideoWriter('result/our.mp4', fourcc, fps,
(ori_w, ori_h))
logger.info('record vide to %s' % args.save_video)
logger.info('fps@%f' % fps)
size = [int(654 * (ori_h / ori_w)), 654]
h = int(654 * (ori_h / ori_w))
time_n = time.time()
#print(time_n)
max_boxs = 0
person_track = {}
yolo2 = YOLO2()
while True:
face = []
cur1 = conn.cursor() # 获取一个游标
sql = "select * from worker"
cur1.execute(sql)
data = cur1.fetchall()
for d in data:
# 注意int类型需要使用str函数转义
name = str(d[1]) + '_' + d[2]
face.append(name)
cur1.close() # 关闭游标
_, image_fist = cap.read()
#穿戴安全措施情况检测
img = Image.fromarray(
cv2.cvtColor(image_fist, cv2.COLOR_BGR2RGB))
image, wear = yolo2.detect_image(img)
image = np.array(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# # 获取警戒线
cv2.line(image, (837, 393), (930, 300), (0, 255, 255), 3)
transboundaryline = t.line_detect_possible_demo(image)
#openpose二维姿态检测
img = np.array(cv2.resize(image, (654, h)))
# cv2.imshow('raw', img)
img_corner = np.array(
cv2.resize(image, (360, int(360 * (ori_h / ori_w)))))
img = img[np.newaxis, :]
peaks, heatmap, vectormap = sess.run(
[tensor_peaks, hm_up, cpm_up],
feed_dict={
raw_img: img,
img_size: size
})
bodys = PoseEstimator.estimate_paf(peaks[0], heatmap[0],
vectormap[0])
image, person = TfPoseEstimator.draw_humans(image,
bodys,
imgcopy=False)
#取10右脚 13左脚
foot = []
if len(person) > 0:
for p in person:
foot_lr = []
if 10 in p and 13 in p:
foot_lr.append(p[10])
foot_lr.append(p[13])
if len(foot_lr) > 1:
foot.append(foot_lr)
fps = round(1 / (time.time() - time_n), 2)
image = cv2.putText(image,
str(fps) + 'fps', (10, 15),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1,
(255, 255, 255))
time_n = time.time()
#deep目标检测
image2 = Image.fromarray(image_fist)
boxs = yolo.detect_image(image2)
features = encoder(image, boxs)
detections = [
Detection(bbox, 1.0, feature)
for bbox, feature in zip(boxs, features)
]
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(
boxes, nms_max_overlap, scores)
detections = [detections[i] for i in indices]
if len(boxs) > max_boxs:
max_boxs = len(boxs)
# print(max_boxs)
# Call the tracker
tracker.predict()
tracker.update(detections)
for track in tracker.tracks:
if max_boxs < track.track_id:
tracker.tracks.remove(track)
tracker._next_id = max_boxs + 1
if not track.is_confirmed() or track.time_since_update > 1:
continue
bbox = track.to_tlbr()
PointX = bbox[0] + ((bbox[2] - bbox[0]) / 2)
PointY = bbox[3]
if track.track_id not in person_track:
track2 = copy.deepcopy(track)
person_track[track.track_id] = track2
else:
track2 = copy.deepcopy(track)
bbox2 = person_track[track.track_id].to_tlbr()
PointX2 = bbox2[0] + ((bbox2[2] - bbox2[0]) / 2)
PointY2 = bbox2[3]
distance = math.sqrt(
pow(PointX - PointX2, 2) +
pow(PointY - PointY2, 2))
if distance < 120:
person_track[track.track_id] = track2
else:
# print('last',track.track_id)
dis = {}
for key in person_track:
bbox3 = person_track[key].to_tlbr()
PointX3 = bbox3[0] + (
(bbox3[2] - bbox3[0]) / 2)
PointY3 = bbox3[3]
d = math.sqrt(
pow(PointX3 - PointX, 2) +
pow(PointY3 - PointY, 2))
dis[key] = d
dis = sorted(dis.items(),
key=operator.itemgetter(1),
reverse=False)
track2.track_id = dis[0][0]
person_track[dis[0][0]] = track2
tracker.tracks.remove(track)
tracker.tracks.append(person_track[track.track_id])
# 写入class
try:
box_title = face[track2.track_id - 1]
except Exception as e:
box_title = str(track2.track_id) + "_" + "unknow"
if box_title not in workers:
wid = box_title.split('_')[0]
localtime = time.asctime(time.localtime(time.time()))
workers[box_title] = wk.Worker()
workers[box_title].set(box_title, localtime,
(int(PointX), int(PointY)))
cur2 = conn.cursor() # 获取一个游标
sql2 = "UPDATE worker SET in_time='" + localtime + "' WHERE worker_id= '" + wid + "'"
cur2.execute(sql2)
cur2.close() # 关闭游标
else:
localtime = time.asctime(time.localtime(time.time()))
yoloPoint = (int(PointX), int(PointY))
foot_dic = {}
wear_dic = {}
for f in foot:
fp = []
footCenter = ((f[0][0] + f[1][0]) / 2,
(f[0][1] + f[1][1]) / 2)
foot_dis = int(
math.sqrt(
pow(footCenter[0] - yoloPoint[0], 2) +
pow(footCenter[1] - yoloPoint[1], 2)))
#print(foot_dis)
fp.append(f)
fp.append(footCenter)
foot_dic[foot_dis] = fp
#print(box_title, 'sss', foot_dic)
foot_dic = sorted(foot_dic.items(),
key=operator.itemgetter(0),
reverse=False)
workers[box_title].current_point = foot_dic[0][1][1]
workers[box_title].track_point.append(
workers[box_title].current_point)
#print(box_title,'sss',foot_dic[0][1][1])
mytrack = str(workers[box_title].track_point)
wid = box_title.split('_')[0]
#卡尔曼滤波预测
if wid not in KalmanNmae:
myKalman(wid)
if wid not in lmp:
setLMP(wid)
cpx, cpy = predict(workers[box_title].current_point[0],
workers[box_title].current_point[1],
wid)
if cpx[0] == 0.0 or cpy[0] == 0.0:
cpx[0] = workers[box_title].current_point[0]
cpy[0] = workers[box_title].current_point[1]
workers[box_title].next_point = (int(cpx), int(cpy))
workers[box_title].current_footR = foot_dic[0][1][0][0]
workers[box_title].current_footL = foot_dic[0][1][0][1]
cur3 = conn.cursor() # 获取一个游标
sql = "UPDATE worker SET current_point= '" + str(
workers[box_title].current_point
) + "' , current_footR = '" + str(
workers[box_title].current_footR
) + "',current_footL = '" + str(
workers[box_title].current_footL
) + "',track_point = '" + mytrack + "',next_point = '" + str(
workers[box_title].next_point
) + "' WHERE worker_id= '" + wid + "'"
cur3.execute(sql)
cur3.close()
#写入安全措施情况
if len(wear) > 0:
for w in wear:
wear_dis = int(
math.sqrt(
pow(w[0] - yoloPoint[0], 2) +
pow(w[1] - yoloPoint[1], 2)))
wear_dic[wear_dis] = w
wear_dic = sorted(wear_dic.items(),
key=operator.itemgetter(0),
reverse=False)
if wear_dic[0][0] < 120:
cur4 = conn.cursor() # 获取一个游标
if wear[wear_dic[0][1]] == 1:
if len(workers[box_title].wear['no helmet']
) == 0:
workers[box_title].wear[
'no helmet'].append(localtime)
sql = "INSERT INTO wear SET worker_id = '" + wid + "', type = 'no_helmet',abnormal_time = '" + localtime + "'"
cur4.execute(sql)
cur4.close() # 关闭游标
else:
if localtime not in workers[
box_title].wear['no helmet']:
workers[box_title].wear[
'no helmet'].append(localtime)
sql = "INSERT INTO wear SET worker_id = '" + wid + "', type = 'no_helmet',abnormal_time = '" + localtime + "'"
cur4.execute(sql)
cur4.close() # 关闭游标
elif wear[wear_dic[0][1]] == 2:
if len(workers[box_title].
wear['no work cloths']) == 0:
workers[box_title].wear[
'no work cloths'].append(localtime)
sql = "INSERT INTO wear SET worker_id = '" + wid + "', type = 'no work cloths',abnormal_time = '" + localtime + "'"
cur4.execute(sql)
cur4.close() # 关闭游标
else:
if localtime not in workers[
box_title].wear[
'no work cloths']:
workers[box_title].wear[
'no work cloths'].append(
localtime)
sql = "INSERT INTO wear SET worker_id = '" + wid + "', type = 'no work cloths',abnormal_time = '" + localtime + "'"
cur4.execute(sql)
cur4.close() # 关闭游标
elif wear[wear_dic[0][1]] == 3:
if len(workers[box_title].
wear['unsafe wear']) == 0:
workers[box_title].wear[
'unsafe wear'].append(localtime)
sql = "INSERT INTO wear SET worker_id = '" + wid + "', type = 'unsafe wear',abnormal_time = '" + localtime + "'"
cur4.execute(sql)
cur4.close() # 关闭游标
else:
if localtime not in workers[
box_title].wear['unsafe wear']:
workers[box_title].wear[
'unsafe wear'].append(
localtime)
sql = "INSERT INTO wear SET worker_id = '" + wid + "', type = 'unsafe wear',abnormal_time = '" + localtime + "'"
cur4.execute(sql)
cur4.close() # 关闭游标
#写入越线情况
if len(workers[box_title].track_point) > 4:
for i in range(len(transboundaryline)):
p1 = (transboundaryline[i][0],
transboundaryline[i][1])
p2 = (transboundaryline[i][2],
transboundaryline[i][3])
p3 = workers[box_title].track_point[-2]
p4 = workers[box_title].track_point[-1]
a = t.IsIntersec(p1, p2, p3, p4)
if a == '有交点':
cur5 = conn.cursor() # 获取一个游标
cur6 = conn.cursor() # 获取一个游标
cur5.execute(
"select time from transboundary where worker_id = '"
+ wid + "' ")
qurrytime = cur5.fetchone()
cur5.close() # 关闭游标
if qurrytime == None:
print('越线')
sql = "INSERT INTO transboundary SET worker_id = '" + wid + "',time = '" + localtime + "'"
cur6.execute(sql)
cur6.close() # 关闭游标
else:
temp1 = 0
for qt in qurrytime:
if qt == localtime:
temp1 = 1
if temp1 == 0:
print('越线')
sql = "INSERT INTO transboundary SET worker_id = '" + wid + "',time = '" + localtime + "'"
cur6.execute(sql)
cur6.close() # 关闭游标
if len(workers[box_title].track_point) >= 20:
workers[box_title].previous_point = workers[
box_title].track_point[-5]
conn.commit()
try:
cv2.putText(image, face[track2.track_id - 1],
(int(bbox[0]), int(bbox[1])), 0,
5e-3 * 200, (0, 255, 0), 2)
except Exception as e:
cv2.putText(image, "unknow",
(int(bbox[0]), int(bbox[1])), 0,
5e-3 * 200, (0, 255, 0), 2)
if args.video is not None:
image[27:img_corner.shape[0] +
27, :img_corner.shape[1]] = img_corner # [3:-10, :]
cv2.imshow(' ', image)
if args.save_video is not None:
video_saver.write(image)
cv2.waitKey(1)
else:
image = common.read_imgfile(args.image)
size = [image.shape[0], image.shape[1]]
if image is None:
logger.error('Image can not be read, path=%s' % args.image)
sys.exit(-1)
h = int(654 * (size[0] / size[1]))
img = np.array(cv2.resize(image, (654, h)))
cv2.imshow('ini', img)
img = img[np.newaxis, :]
peaks, heatmap, vectormap = sess.run(
[tensor_peaks, hm_up, cpm_up],
feed_dict={
raw_img: img,
img_size: size
})
cv2.imshow('in', vectormap[0, :, :, 0])
bodys = PoseEstimator.estimate_paf(peaks[0], heatmap[0],
vectormap[0])
image = TfPoseEstimator.draw_humans(image,
bodys,
imgcopy=False)
cv2.imshow(' ', image)
cv2.waitKey(0)
class Ui_MainWindow(object):
VIDEO_TYPE_OFFLINE = 0
VIDEO_TYPE_REAL_TIME = 1
STATUS_INIT = 0
STATUS_PLAYING = 1
STATUS_PAUSE = 2
video_url = ""
def __init__(self,
video_url="",
video_type=VIDEO_TYPE_OFFLINE,
auto_play=False):
self.video_url = video_url
self.video_type = video_type
self.auto_play = auto_play
self.status = self.STATUS_INIT
self.yolo = YOLO()
self.timer = VideoTimer()
self.timer.timeSignal.signal[str].connect(self.show_video_images)
self.playCapture = VideoCapture()
if self.video_url != "":
self.playCapture.open(self.video_url)
fps = self.playCapture.get(CAP_PROP_FPS)
self.timer.set_fps(fps)
self.playCapture.release()
if self.auto_play:
self.switch_video()
def reset(self):
self.timer.stop()
self.playCapture.release()
self.status = Ui_MainWindow.STATUS_INIT
def setupUi(self, MainWindow):
MainWindow.setObjectName("MainWindow")
MainWindow.resize(1086, 597)
MainWindow.setStyleSheet("background-color:rgb(76, 126, 201)")
self.centralwidget = QtWidgets.QWidget(MainWindow)
self.centralwidget.setObjectName("centralwidget")
self.frame = QtWidgets.QFrame(self.centralwidget)
self.frame.setGeometry(QtCore.QRect(0, -30, 1131, 621))
self.frame.setStyleSheet("background-color:rgb(76, 126, 201)")
self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame.setObjectName("frame")
self.label_7 = QtWidgets.QLabel(self.frame)
self.label_7.setGeometry(QtCore.QRect(130, 360, 635, 23))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.label_7.setPalette(palette)
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(10)
self.label_7.setFont(font)
self.label_7.setObjectName("label_7")
self.label_14 = QtWidgets.QLabel(self.frame)
self.label_14.setEnabled(True)
self.label_14.setGeometry(QtCore.QRect(130, 495, 646, 24))
self.label_14.setText("")
self.label_14.setObjectName("label_14")
self.label_11 = QtWidgets.QLabel(self.frame)
self.label_11.setGeometry(QtCore.QRect(130, 204, 646, 24))
self.label_11.setText("")
self.label_11.setObjectName("label_11")
self.label_10 = QtWidgets.QLabel(self.frame)
self.label_10.setGeometry(QtCore.QRect(130, 173, 16, 17))
self.label_10.setText("")
self.label_10.setObjectName("label_10")
self.label_5 = QtWidgets.QLabel(self.frame)
self.label_5.setGeometry(QtCore.QRect(130, 329, 461, 23))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.label_5.setPalette(palette)
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(10)
self.label_5.setFont(font)
self.label_5.setObjectName("label_5")
self.label_6 = QtWidgets.QLabel(self.frame)
self.label_6.setGeometry(QtCore.QRect(130, 391, 646, 23))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.label_6.setPalette(palette)
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(10)
self.label_6.setFont(font)
self.label_6.setObjectName("label_6")
self.label_13 = QtWidgets.QLabel(self.frame)
self.label_13.setGeometry(QtCore.QRect(130, 432, 646, 24))
self.label_13.setText("")
self.label_13.setObjectName("label_13")
self.label = QtWidgets.QLabel(self.frame)
self.label.setGeometry(QtCore.QRect(130, 30, 389, 136))
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(28)
font.setBold(True)
font.setWeight(75)
self.label.setFont(font)
self.label.setStyleSheet("color:white")
self.label.setObjectName("label")
self.line = QtWidgets.QFrame(self.frame)
self.line.setGeometry(QtCore.QRect(130, 422, 646, 16))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.line.setPalette(palette)
self.line.setStyleSheet("color:white")
self.line.setFrameShadow(QtWidgets.QFrame.Plain)
self.line.setLineWidth(1)
self.line.setFrameShape(QtWidgets.QFrame.HLine)
self.line.setObjectName("line")
self.label_12 = QtWidgets.QLabel(self.frame)
self.label_12.setGeometry(QtCore.QRect(130, 266, 646, 25))
self.label_12.setText("")
self.label_12.setObjectName("label_12")
self.pushButton = QtWidgets.QPushButton(self.frame)
self.pushButton.setGeometry(QtCore.QRect(840, 340, 211, 51))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipText,
brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipText,
brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipText,
brush)
self.pushButton.setPalette(palette)
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(14)
font.setBold(True)
font.setWeight(75)
self.pushButton.setFont(font)
self.pushButton.setLayoutDirection(QtCore.Qt.LeftToRight)
self.pushButton.setStyleSheet("background-color:rgb(60, 171, 255)\n"
"")
self.pushButton.setObjectName("pushButton")
self.label_3 = QtWidgets.QLabel(self.frame)
self.label_3.setGeometry(QtCore.QRect(130, 463, 71, 24))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.label_3.setPalette(palette)
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(11)
self.label_3.setFont(font)
self.label_3.setObjectName("label_3")
self.label_4 = QtWidgets.QLabel(self.frame)
self.label_4.setGeometry(QtCore.QRect(130, 235, 507, 23))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.label_4.setPalette(palette)
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(10)
self.label_4.setFont(font)
self.label_4.setObjectName("label_4")
self.label_8 = QtWidgets.QLabel(self.frame)
self.label_8.setGeometry(QtCore.QRect(130, 298, 289, 23))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.label_8.setPalette(palette)
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(10)
self.label_8.setFont(font)
self.label_8.setObjectName("label_8")
self.label_9 = QtWidgets.QLabel(self.frame)
self.label_9.setGeometry(QtCore.QRect(130, 526, 196, 24))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.label_9.setPalette(palette)
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(11)
self.label_9.setFont(font)
self.label_9.setObjectName("label_9")
self.frame_2 = QtWidgets.QFrame(self.frame)
self.frame_2.setGeometry(QtCore.QRect(20, 30, 1061, 581))
self.frame_2.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame_2.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame_2.setObjectName("frame_2")
self.label_2 = QtWidgets.QLabel(self.frame_2)
self.label_2.setGeometry(QtCore.QRect(30, -20, 371, 121))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.label_2.setPalette(palette)
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(28)
self.label_2.setFont(font)
self.label_2.setObjectName("label_2")
self.line_2 = QtWidgets.QFrame(self.frame_2)
self.line_2.setGeometry(QtCore.QRect(30, 70, 281, 16))
self.line_2.setStyleSheet("color:white")
self.line_2.setFrameShadow(QtWidgets.QFrame.Plain)
self.line_2.setLineWidth(1)
self.line_2.setFrameShape(QtWidgets.QFrame.HLine)
self.line_2.setObjectName("line_2")
self.label_15 = QtWidgets.QLabel(self.frame_2)
self.label_15.setGeometry(QtCore.QRect(30, 90, 751, 21))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText,
brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(76, 126, 201))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.label_15.setPalette(palette)
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(11)
self.label_15.setFont(font)
self.label_15.setObjectName("label_15")
self.pushButton_2 = QtWidgets.QPushButton(self.frame_2)
self.pushButton_2.setGeometry(QtCore.QRect(800, 380, 151, 61))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.pushButton_2.setPalette(palette)
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(12)
self.pushButton_2.setFont(font)
self.pushButton_2.setStyleSheet("background-color:rgb(60, 171, 255)")
self.pushButton_2.setObjectName("pushButton_2")
self.label_16 = QtWidgets.QLabel(self.frame_2)
self.label_16.setGeometry(QtCore.QRect(80, 170, 611, 391))
self.label_16.setStyleSheet("background-color:white")
self.label_16.setText("")
self.label_16.setObjectName("label_16")
self.line_3 = QtWidgets.QFrame(self.frame_2)
self.line_3.setGeometry(QtCore.QRect(20, 160, 20, 401))
self.line_3.setStyleSheet("color:white")
self.line_3.setFrameShadow(QtWidgets.QFrame.Plain)
self.line_3.setFrameShape(QtWidgets.QFrame.VLine)
self.line_3.setObjectName("line_3")
self.pushButton_3 = QtWidgets.QPushButton(self.frame_2)
self.pushButton_3.setGeometry(QtCore.QRect(800, 480, 151, 61))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 120, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText,
brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(60, 171, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
self.pushButton_3.setPalette(palette)
font = QtGui.QFont()
font.setFamily("微软雅黑")
font.setPointSize(12)
self.pushButton_3.setFont(font)
self.pushButton_3.setStyleSheet("background-color:rgb(60, 171, 255)")
self.pushButton_3.setObjectName("pushButton_3")
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 1086, 26))
self.menubar.setObjectName("menubar")
MainWindow.setMenuBar(self.menubar)
self.statusbar = QtWidgets.QStatusBar(MainWindow)
self.statusbar.setObjectName("statusbar")
MainWindow.setStatusBar(self.statusbar)
self.frame_2.setVisible(False)
self.retranslateUi(MainWindow)
QtCore.QMetaObject.connectSlotsByName(MainWindow)
self.pushButton.clicked.connect(self.pushButton_clicked)
def pushButton_clicked(self):
self.frame.setVisible(True)
self.frame_2.setVisible(True)
def show_video_images(self):
if self.playCapture.isOpened():
success, frame = self.playCapture.read()
# fileName_choose, filetype = QFileDialog.getOpenFileName()
# print(fileName_choose)
# playCapture = VideoCapture()
# self.playCapture.open(fileName_choose)
# fps = playCapture.get(CAP_PROP_FPS)
if success:
frame = self.yolo.detect_image(PIL.Image.fromarray(frame))
frame = cv2.cvtColor(numpy.asarray(frame), cv2.COLOR_RGB2BGR)
print("aaaaaa")
height, width = frame.shape[:2]
if frame.ndim == 3:
rgb = cvtColor(frame, COLOR_BGR2RGB)
else:
rgb = cvtColor(frame, COLOR_GRAY2BGR)
temp_image = QImage(rgb.flatten(), width, height,
QImage.Format_RGB888)
temp_pixmap = QPixmap.fromImage(temp_image)
self.label_16.setPixmap(temp_pixmap)
self.label_16.setScaledContents(True)
print("bbbbbbbbbb")
else:
print("read failed, no frame data")
self.reset()
return
# success, frame = self.playCapture.read()
# if not success and self.video_type is VideoBox.VIDEO_TYPE_OFFLINE:
# print("play finished") # 判断本地文件播放完毕
# self.reset()
# self.playButton.setIcon(self.style().standardIcon(QStyle.SP_MediaStop))
def switch_video(self):
fileName_choose, filetype = QFileDialog.getOpenFileName()
print(fileName_choose)
self.video_url = fileName_choose
self.status = Ui_MainWindow.STATUS_INIT
if self.video_url == "" or self.video_url is None:
return
if self.status is Ui_MainWindow.STATUS_INIT:
self.playCapture.open(self.video_url)
self.timer.start()
elif self.status is Ui_MainWindow.STATUS_PLAYING:
self.timer.stop()
if self.video_type is Ui_MainWindow.VIDEO_TYPE_REAL_TIME:
self.playCapture.release()
elif self.status is Ui_MainWindow.STATUS_PAUSE:
if self.video_type is Ui_MainWindow.VIDEO_TYPE_REAL_TIME:
self.playCapture.open(self.video_url)
self.timer.start()
self.status = (Ui_MainWindow.STATUS_PLAYING,
Ui_MainWindow.STATUS_PAUSE,
Ui_MainWindow.STATUS_PLAYING)[self.status]
def retranslateUi(self, MainWindow):
_translate = QtCore.QCoreApplication.translate
MainWindow.setWindowTitle(_translate("MainWindow", "MainWindow"))
self.label_7.setText(
_translate(
"MainWindow",
"license owners,you will be able to access your downloads once you log in.Not"
))
self.label_5.setText(
_translate(
"MainWindow",
"It\'s true.We\'re fanatical about it.And we\'ve got your back."
))
self.label_6.setText(
_translate(
"MainWindow",
"If you have a question,use any of the below options to get in touch.Commercial"
))
self.label.setText(
_translate(
"MainWindow",
"<html><head/><body><p>Welcome to</p><p>Carditec website</p></body></html>"
))
self.pushButton.setText(_translate("MainWindow", "START"))
self.label_3.setText(_translate("MainWindow", "Email us"))
self.label_4.setText(
_translate(
"MainWindow",
"We mainly provides vehicle identification technology support."
))
self.label_8.setText(
_translate("MainWindow", "Not sure what to do next?Read this."))
self.label_9.setText(_translate("MainWindow", "*****@*****.**"))
self.label_2.setText(_translate("MainWindow", "LET\'S START"))
self.label_15.setText(
_translate(
"MainWindow",
"Import a picture or video,the website will analyze vehicle types and show the results."
))
self.pushButton_2.setText(_translate("MainWindow", "UPLOAD"))
self.pushButton_3.setText(_translate("MainWindow", "DOWNLOAD"))
self.pushButton_2.clicked.connect(self.switch_video)
def main():
start = time.time()
first = start
#Definition of the parameters
max_cosine_distance = 0.5#0.9 余弦距离的控制阈值
nn_budget = None
nms_max_overlap = 0.3 #非极大抑制的阈值
counter = []
#deep_sort
model_filename = 'model_data/market1501.pb'
encoder = gdet.create_box_encoder(model_filename,batch_size=1)
find_objects = ['person', 'fire_extinguisher', 'fireplug', 'car', 'bicycle', 'motorcycle']
yolo = YOLO()
for cnt in range(1, 2):
video_path = "./t1_video/t1_video_%05d" % cnt
images = os.listdir(video_path)
images.sort()
print(images[0])
trackers = []
counters = []
for idx in range(0, 6):
metric = nn_matching.NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
trackers.append(Tracker(metric))
count = []
counters.append(count)
tracker_time = 0
yolo_time = 0
for image_path in images:
# image_path = video_path + "/t1_video_%05d_%05d.jpg" % (1, fc)
t1 = time.time()
# print(video_path + "/" + image_path)
frame = cv2.imread(video_path + "/" + image_path)
image = Image.fromarray(frame[..., ::-1]) # bgr to rgb
yolo_start = time.time()
yolo_dict = yolo.detect_image(image)
yolo_end = time.time()
yolo_time += (yolo_end - yolo_start)
for idx in range(0, 6):
# print(idx)
tracker = trackers[idx]
counter = counters[idx]
boxs = yolo_dict.get(find_objects[idx])
if boxs == None:
continue
features = encoder(frame, boxs)
# score to 1.0 here).
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxs, features)]
# Run non-maxima suppression.
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(boxes, nms_max_overlap, scores)
detections = [detections[i] for i in indices]
# Call the tracker
t_start = time.time()
tracker.predict()
tracker.update(detections)
t_end = time.time()
tracker_time += (t_end - t_start)
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 1:
continue
#boxes.append([track[0], track[1], track[2], track[3]])
counter.append(int(track.track_id))
#######################################
num_person = len(set(counters[0]))
num_fire_extinguisher = len(set(counters[1]))
num_fireplug = len(set(counters[2]))
num_car = len(set(counters[3]))
num_bicycle = len(set(counters[4]))
num_motocycle = len(set(counters[5]))
ress.append(present_result(cnt, num_person,
num_fire_extinguisher,
num_fireplug,
num_car,
num_bicycle,
num_motocycle))
t1_res_cai["track1_results"] = ress
with open('t1_res_cai.json', 'w') as make_file:
json.dump(t1_res_cai, make_file, ensure_ascii=False, indent=4)
#######################################
for idx in range(0, 6):
print(len(set(counters[idx])), end=" ")
end = time.time()
print(str(':: total:%.2f yolo:%.2f tracker:%.2f' % ((end - start),yolo_time, tracker_time)))
start = end
last = time.time()
print(str(':: %.2f' % (last - first)))
images = data[0]
# labels = data[1] these are not labels. batch size
model_path = 'logs/000/ep009-loss30.814-val_loss30.951.h5'
yolo_model = yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
yolo_model.load_weights(model_path)
FLAGS = {
"model_path": model_path,
"anchors_path": anchors_path,
"classes_path": classes_path,
"score" : 0.3,
"iou" : 0.45,
"model_image_size" : (416, 416),
"gpu_num" : 1,
}
model = YOLO()
image_base_path = '/home/shuby.deshpande/workspace/keras-yolo3/data/5cc3a5ef4e436f43f7b5615f/images/*'
image_paths = glob.glob(image_base_path)
for image_path in range(len(image_paths)):
image = Image.open(image_paths[image_path])
pred = model.detect_image(image)
image_name = str(image_path)+'_pred.jpeg'
print('-'*50)
print('Predicting bbox for image:', image_path)
print('-'*50)
pred.save(image_name)
def process_video(self):
self.progressLabel.setText("Loading Yolo Model")
QtCore.QCoreApplication.processEvents()
from collections import deque
from yolo import YOLO
from yolo3 import utils
from PIL import Image
import cv2
frame_buffer = deque([], 3)
bridge_buffer = []
bridge_last = -1
extended = False
counter_ = 0
frame_counter = 0
yolo_model = YOLO()
capture = cv2.VideoCapture(self.input_path)
if not capture.isOpened():
raise IOError("Couldn't open video")
video_fps = capture.get(cv2.CAP_PROP_FPS)
video_size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
max_frame_count = capture.get(cv2.CAP_PROP_FRAME_COUNT)
out = cv2.VideoWriter(self.output_path,
cv2.VideoWriter_fourcc(*'DIVX'), video_fps,
video_size)
self.progressLabel.setText("Detecting")
while (capture.isOpened()):
ret, frame = capture.read()
frame_counter += 1
if ret:
image = Image.fromarray(frame)
bboxes_pr = yolo_model.detect_image(image)
frame_buffer.append([bboxes_pr, frame])
if len(frame_buffer) >= frame_buffer.maxlen:
img_pred = frame_buffer[0][1].copy()
if counter_ > 0:
extended = False
counter_ = 0
if extended:
counter_ += 1
if not extended and len(frame_buffer[1][0]) <= 0:
if len(frame_buffer[0][0]) > 0 or len(
frame_buffer[2][0]) > 0:
if len(frame_buffer[0][0]) > 0:
frame_buffer[1][0] = frame_buffer[0][0]
else:
frame_buffer[1][0] = frame_buffer[2][0]
extended = True
img_pred = utils.blur_img(frame_buffer[0][0], img_pred)
height, width, channel = img_pred.shape
bytesPerLine = 3 * width
qimg = QtGui.QImage(img_pred.data, width, height,
bytesPerLine,
QtGui.QImage.Format_BGR888)
self.frame.setPixmap(QtGui.QPixmap(qimg))
progress_value = int(frame_counter / max_frame_count * 100)
self.progressBar.setProperty("value", progress_value)
# Updates Picture + Progressbar
QtCore.QCoreApplication.processEvents()
bridge_buffer.append([frame_buffer[0][0], img_pred])
if len(bridge_buffer) >= 50:
if len(frame_buffer[0][0]) == len(frame_buffer[1][0]):
bridge_buffer = utils.fill_bridges(bridge_buffer)
for list in bridge_buffer:
out.write(list[1])
bridge_buffer = []
else:
bridge_buffer = utils.fill_bridges(bridge_buffer)
for list in bridge_buffer:
out.write(list[1])
bridge_buffer = []
capture.release()
out.release()
# yolo_model.close_session()
self.progressLabel.setText("Adding Audio to the Outputfile")
temp_output = self.output_path.split("/")
temp_output[-1] = temp_output[-1].split(".")[0] + "_.mp4"
temp_output = "/".join(temp_output)
os.system(
f'ffmpeg -i "{self.output_path}" -i "{self.input_path}" -map 0:v -map 1:a -c copy -shortest "{temp_output}"'
)
os.remove(self.output_path)
os.rename(temp_output, self.output_path)
self.progressLabel.setText("Finished")
break
1、如果想要进行检测完的图片的保存，利用r_image.save("img.jpg")即可保存，直接在predict.py里进行修改即可。
2、如果想要获得预测框的坐标，可以进入yolo.detect_image函数，在绘图部分读取top，left，bottom，right这四个值。
3、如果想要利用预测框截取下目标，可以进入yolo.detect_image函数，在绘图部分利用获取到的top，left，bottom，right这四个值
在原图上利用矩阵的方式进行截取。
4、如果想要在预测图上写额外的字，比如检测到的特定目标的数量，可以进入yolo.detect_image函数，在绘图部分对predicted_class进行判断，
比如判断if predicted_class == 'car': 即可判断当前目标是否为车，然后记录数量即可。利用draw.text即可写字。
'''
while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
r_image = yolo.detect_image(image)
r_image.show()
elif mode == "video":
capture = cv2.VideoCapture(video_path)
if video_save_path != "":
fourcc = cv2.VideoWriter_fourcc(*'XVID')
size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
out = cv2.VideoWriter(video_save_path, fourcc, video_fps, size)
ref, frame = capture.read()
if not ref:
raise ValueError("未能正确读取摄像头（视频），请注意是否正确安装摄像头（是否正确填写视频路径）。")
fps = 0.0
def detect_img():
yolo = YOLO()
image = Image.open('03.jpg')
r_image = yolo.detect_image(image)
yolo.close_session()
r_image.show()
from nets.yolo3 import yolo_body
from keras.layers import Input
from yolo import YOLO
from PIL import Image, ImageDraw
yolo = YOLO()
trfn = 0
carn = 0
lrx = 920
rrx = 880
carnums = 0
carlist = []
while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
r_image, i, m = yolo.detect_image(image, trfn, carn, lrx, rrx, carnums,
carlist)
draw = ImageDraw.Draw(r_image)
draw.line((260, 850, 1500, 850), fill='red', width=5)
trfn = i
carn = m
r_image.show()
# result如果之前存放的有文件，全部清除
for i in os.listdir(result_path):
path_file = os.path.join(result_path,i)
if os.path.isfile(path_file):
os.remove(path_file)
#创建一个记录检测结果的文件
txt_path =result_path + '/result.txt'
file = open(txt_path,'w')
while True:
image_array = grab_screen(region=(0, 0, 1280, 720))
# 获取屏幕，(0, 0, 1280, 720)表示从屏幕坐标（0,0）即左上角，截取往右1280和往下720的画面
array_to_image = Image.fromarray(image_array, mode='RGB') #将array转成图像，才能送入yolo进行预测
img = yolo.detect_image(array_to_image) #调用yolo文件里的函数进行检测
img = np.asarray(img) #将图像转成array
cv2.imshow('window',cv2.cvtColor(img, cv2.COLOR_BGR2RGB))#将截取的画面从另一窗口显示出来，对速度会有一点点影响，不过也就截取每帧多了大约0.01s的时间
file.write(' score: '+str(yolo.detect_image.score)+' \nlocation: top: '+str(top)+'、 bottom: '+str(bottom)+'、 left: '+str(left)+'、 right: '+str(right)+'\n')
if cv2.waitKey(25) & 0xFF == ord('q'): #按q退出，记得输入切成英语再按q
cv2.destroyAllWindows()
break
import cv2
from PIL import Image
from yolo import YOLO
import numpy as np
import os
import pandas
label_path = r'F:\dataset\kaggle\whale\bounding_boxes.csv'
image_path = r'F:\dataset\kaggle\whale\train'
file_list = os.listdir(image_path)
obj = YOLO()
for file in file_list:
filename = os.path.join(image_path, file)
img = Image.open(filename)
result = obj.detect_image(img)
cv2.imshow(file, np.asarray(result))
cv2.waitKey(0)
cv2.destroyAllWindows()
# df = pandas.read_csv(label_path)
# for data in df.values:
# filename = os.path.join(image_path,data[0])
# if os.path.exists(filename):
# img = cv2.imread(filename)
# cv2.rectangle(img,tuple(data[1:3]),tuple(data[3:]),(0,0,255))
# cv2.imshow(data[0],img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# obj.detect_image()
##############
try:
image = Image.open(tmp_path)
except Exception as e:
print(e)
continue
th_w, th_h = image.size
org_w = canvas["width"]
# オリジナルサイズ
r = org_w / th_w
result = yolo.detect_image(image)
# print(result)
##############
for obj in result:
tx = obj["x"]
ty = obj["y"]
tw = obj["w"]
th = obj["h"]
x = str(int(tx * r))
y = str(int(ty * r))
w = str(int(tw * r))
h = str(int(th * r))
#TODO: delete this file when done
import sys
sys.path.append("player_recognition")
import numpy as np
from yolo import YOLO
from PIL import Image
from mask import MaskRCNN
yolo = YOLO()
yolo_boxes = yolo.detect_image(
Image.open("player_recognition/sports_images/liverpool-chelsea.jpeg"))
mask_rcnn = MaskRCNN()
mask_boxes = mask_rcnn.model.detect([
np.asarray(
Image.open("player_recognition/sports_images/liverpool-chelsea.jpeg"))
])
video_size = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
DETECT_EVERY_N_FRAMES = round(video_fps) # detect every second
out = cv2.VideoWriter(video_output_path, video_FourCC, video_fps,
video_size)
yolo = YOLO()
region_list = []
illegal_list = []
# vehicle detection for frame 0
return_value, cur_img_cv = vid.read()
cur_img_cv = cv2.cvtColor(cur_img_cv, cv2.COLOR_BGR2RGB)
image = Image.fromarray(
cur_img_cv) # transfer OpenCV format to PIL.Image format
image_canvas, out_boxes, out_scores, out_classes = yolo.detect_image(image)
for i in range(len(out_boxes)):
class_name = yolo.class_names[out_classes[i]]
if class_name in VEHICLES:
region = Region(out_boxes[i], class_name)
region_list.append(region)
idx = 1 # frame no.
result = None
while True:
if DRAW_ON_DETECTION_RESULTS == False:
image_canvas = image
pre_img_cv = cv2.cvtColor(
class SpineYolo(object):
def __init__(self, _args):
self.training_data_path = os.path.expanduser(_args.train_data_path)
self.validation_data_path = os.path.expanduser(_args.val_data_path)
self.classes_path = os.path.expanduser(_args.classes_path)
self.anchors_path = os.path.expanduser(_args.anchors_path)
self.model_path = os.path.expanduser(_args.model_path)
self.log_dir = os.path.join('logs', '000')
self.yolo_detector = None
def detect_input_images(self):
while True:
img_path = input('Input image or image list filename:')
if os.path.splitext(img_path)[1] == '.txt':
self.detect_images_from_file_list(img_path)
break
try:
image = Image.open(img_path)
except:
print('Open Error! Try again!')
continue
else:
r_image = self.yolo_detector.detect_image(image)
r_image.show()
self.yolo_detector.close_session()
def detect(self):
self.yolo_detector = YOLO(**{"model_path": self.model_path})
self.detect_input_images()
def detect_images_from_file_list(self, img_path):
with open(img_path) as f:
lines = f.readlines()
for line in lines:
try:
img_file = line.strip().split()[0]
image = Image.open(img_file)
except:
print('Couldn''t load image file: {}'.format(img_file))
continue
r_image = self.yolo_detector.detect_image(image)
r_image.show()
def train_yolo(self, training_data_to_use=1):
parsed_training_data = get_lines_from_annotation_file(self.training_data_path)
parsed_validation_data = get_lines_from_annotation_file(self.validation_data_path)
training_samples = round(len(parsed_training_data) * training_data_to_use)
parsed_training_data = parsed_training_data[:training_samples]
if training_data_to_use != 1:
self.set_log_dir(os.path.join('logs', 'training_samples_{}'.format(training_samples)))
train_spine_yolo(parsed_training_data, parsed_validation_data, self.log_dir, self.classes_path,
self.anchors_path, self.model_path)
def set_log_dir(self, log_dir):
self.log_dir = log_dir
def set_training_data_path(self, path):
self.training_data_path = path
def set_validation_data_path(self, path):
self.validation_data_path = path
def set_model_path(self, path):
self.model_path = path
def prepare_image_data(self, images_path, is_labeled=False, train_test_split=0.8):
spine_data_preparer = SpineImageDataPreparer()
spine_data_preparer.set_initial_directory(images_path)
spine_data_preparer.set_labeled_state(is_labeled)
spine_data_preparer.set_train_test_split(train_test_split)
spine_data_preparer.run()
def read(stack) :
print('Process to read: %s' % os.getpid())
yolo = YOLO()
# Definition of the parameters
max_cosine_distance = 0.3
nn_budget = None
nms_max_overlap = 1.0
# deep_sort
model_filename = 'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
tracker = Tracker(metric)
max_boxs = 0
face = ['17208019']
#目标上一帧的点
history = {}
#id和标签的字典
person = {}
#赋予新标签的id列表
change = []
while True:
if len(stack) != 0:
frame = stack.pop()
t1 = time.time()
frame_count = 0
localtime = time.asctime(time.localtime(time.time()))
utils.draw(frame,line.readline())
# 获取警戒线
transboundaryline = line.readline()
utils.draw(frame, transboundaryline)
img = Image.fromarray(frame)
#img.save('frame.jpg')
'''
cv2.line(frame, (837, 393), (930, 300), (0, 255, 255), 3)
transboundaryline = t.line_detect_possible_demo(frame)
'''
# image = Image.fromarray(frame)
image = Image.fromarray(frame[..., ::-1]) # bgr to rgb
boxs = yolo.detect_image(image)
# print("box_num",len(boxs))
features = encoder(frame, boxs)
# score to 1.0 here).
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxs, features)]
# Run non-maxima suppression.
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(boxes, nms_max_overlap, scores)
detections = [detections[i] for i in indices]
if len(boxs) > max_boxs:
max_boxs = len(boxs)
# Call the tracker
tracker.predict()
tracker.update(detections)
#一帧信息
info = {}
target = []
for track in tracker.tracks:
#一帧中的目标
per_info = {}
if not track.is_confirmed() or track.time_since_update > 1:
continue
if track.track_id not in person:
person[track.track_id] = str(track.track_id)
bbox = track.to_tlbr()
PointX = bbox[0] + ((bbox[2] - bbox[0]) / 2)
PointY = bbox[3]
dis = int(PointX) - 1200
try:
if dis<15:
if track.track_id not in change:
person[track.track_id] = face.pop(0)
change.append(track.track_id)
except:
print('非法入侵')
#当前目标
if track.track_id not in change:
per_info['worker_id'] = 'unknow'+str(track.track_id)
else:
per_info['worker_id'] = person[track.track_id]
#当前目标坐标
yoloPoint = (int(PointX), int(PointY))
per_info['current_point'] = yoloPoint
# 卡尔曼滤波预测
if per_info['worker_id'] not in utils.KalmanNmae:
utils.myKalman(per_info['worker_id'])
if per_info['worker_id'] not in utils.lmp:
utils.setLMP(per_info['worker_id'])
cpx, cpy = utils.predict(yoloPoint[0], yoloPoint[1], per_info['worker_id'])
if cpx[0] == 0.0 or cpy[0] == 0.0:
cpx[0] = yoloPoint[0]
cpy[0] = yoloPoint[1]
if frame_count>20:
per_info['next_point'] = (int(cpx), int(cpy))
else:
per_info['next_point'] = yoloPoint
# 写入越线情况
if per_info['worker_id'] in history:
per_info['transboundary'] = 'no'
#print(transboundaryline)
line1 = [per_info['next_point'],history[per_info['worker_id']]]
a = line.IsIntersec2(transboundaryline,line1)
if a == '有交点':
print('越线提醒')
per_info['transboundary'] = 'yes'
history[per_info['worker_id']] = per_info['current_point']
frame_count = frame_count+1
#print(per_info)
#画目标框
#cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (255, 255, 255), 2)
cv2.putText(frame, per_info['worker_id'], (int(bbox[0]), int(bbox[1])), 0, 5e-3 * 200, (0, 255, 0), 2)
target.append(per_info)
info['time'] = localtime
#info['frame'] = str(img.tolist()).encode('base64')
info['frame'] = 'frame'
info['target'] = target
#写入josn
info_json = json.dumps(info)
info_queue.put(info_json)
getInfo(info_queue)
cv2.imshow("img", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
cv2.rectangle(image, (x_minimo, y_minimo + int(y_min)),
(x_maximo, y_maximo + int(y_min)), (0, 0, 255), 1)
dni1, dni2, cel1, cel2 = dni_rect_from_ce(c_x, c_y + y_min, cv2, image,
traslation_dict, {})
try:
# Getting DNI area
dniGray = cv2.cvtColor(frame[dni1[1]:dni2[1], dni1[0]:dni2[0]],
cv2.COLOR_BGR2GRAY)
# Preprocessing dni area
dniGrayTresh = deleting_horizontal_lines(cv2, dniGray.copy())
# Getting the model output
pil_dni = Image.fromarray(dniGrayTresh)
dni_img, dni_boxes, dni_scores, dni_classes = yolo.detect_image(
pil_dni)
# Ordering boxes by axis x
boxes, scores, classes, cxs, cys = ordering_cx(
dni_boxes, dni_scores, dni_classes)
# Filtering erronues boxes
valid_ixs = filter_near_cxs(cxs, scores)
valid_cys = np.take(cys, valid_ixs)
prom = valid_cys.sum() / valid_cys.size
desv = (((valid_cys - prom)**2).sum() / valid_cys.size)**(1 / 2)
valid_ixs = filter_far_from_cys(valid_ixs, cys, desv, prom)
b = []
for i in valid_ixs:
class SignDetector(object):
def __init__(self):
FLAGS = {
"model_path": sys.path[1] + cf.sign_weight_h5_path,
"anchors_path": sys.path[1] + cf.sign_anchor_path,
"classes_path": sys.path[1] + cf.sign_class_name_path,
"score": 0.9,
"iou": 0.45,
"model_image_size": (416, 416),
"gpu_num": 1,
}
self.yolo = YOLO(FLAGS)
return
def signDetector(self, image):
im_pil = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
out_boxes, out_scores, out_classes = self.yolo.detect_image(im_pil)
has_sign = False
box = None
if len(out_boxes) > 0:
rs = True
# print('Found', len(out_boxes), 'boxes', out_boxes, out_scores, out_classes))
c = np.argmax(out_scores)
class_id = out_classes[c]
predicted_class = self.yolo.class_names[class_id]
top, left, bottom, right = out_boxes[c]
score = out_scores[c]
left = int(left)
right = int(right)
top = int(top)
bottom = int(bottom)
w = right - left
h = bottom - top
if w >= 40 and h >= 40:
label = 'Sign {} - {} ({},{})'.format(predicted_class, score,
w, h)
box = [
left + detect_sign_region['left'], top, w, h,
predicted_class, score, label
]
cv2.rectangle(image, (left, top), (right, bottom), blue)
return predicted_class, box
return None, None
def signRecognize(self):
cf.k += 1
# if cf.k >= 0 and cf.k % cf.sign_detect_step == 0:
if True:
# print('signRecognize', cf.k, cf.sign_detect_step, cf.signTrack)
if cf.signTrack != -1 and abs(cf.signTrack - cf.k) >= 10:
cf.sign = []
cf.signTrack = -1
print("clear")
# cf.maxspeed = maxspeed
img_detect_sign = cf.img_rgb_raw[
detect_sign_region['top']:detect_sign_region['bottom'],
detect_sign_region['left']:detect_sign_region['right']]
cf.signSignal = None
result, box = self.signDetector(img_detect_sign)
if result != None:
# cf.sign.append(result)
# # cf.speed = 30
# # cf.maxspeed = 30 # khi phat hien bien bao thi giam toc do
# cf.signTrack = cf.k
# print('Sign', cf.sign)
# if self.acceptSign(result):
if result:
if result == 'thang':
# print("THANG")
return "thang_certain", box
elif result == 'phai':
# print("PHAI")
return "phai_certain", box
return result, box
return None, None
def acceptSign(self, value):
if len(cf.sign) >= 2:
for v in cf.sign:
if v != value:
cf.sign = []
return False
cf.sign = []
cf.k = -100 # bo qua 100 frame tiep theo
return True
else:
cf.k = cf.k - cf.sign_detect_step + 1 # xem xet 2 frame lien tiep
return None
help='num img 2 show',
default=1)
parser.add_argument('-r',
"--root",
type=str,
help='root dir filled with *.jpg',
default='VOCdevkit/VOC2007/JPEGImages')
parser.add_argument('-i', "--filename", type=str, help='filename', default='')
args = parser.parse_args()
yolo = YOLO(args.model_path, args.conf, args.cuda)
if args.num2show == 1:
image = Image.open(os.path.join(args.root, args.filename))
res, cls, score = yolo.detect_image(image)
print(cls, score)
# r_image.show()
else:
print('结果将会保存到temp.png')
files = os.listdir(args.root)
idx = [
int(len(os.listdir(args.root)) * random.random())
for i in range(args.num2show)
]
imgs = [Image.open(os.path.join(args.root, files[id])) for id in idx]
ress, clss, scores = [], [], []
print(len(imgs))
for img in imgs:
res, cls, score = yolo.detect_image(img)
def read_cam():
# On versions of L4T previous to L4T 28.1, flip-method=2
# Use the Jetson onboard camera
yolo = YOLO()
#cap = cv2.VideoCapture("nvcamerasrc ! video/x-raw(memory:NVMM), width=(int)1280, height=(int)720,format=(string)I420, framerate=(fraction)30/1 ! nvvidconv flip-method=0 ! video/x-raw, format=(string)BGRx ! videoconvert ! video/x-raw, format=(string)BGR ! appsink")
cap = cv2.VideoCapture(0)
if cap.isOpened():
windowName = "CannyDemo"
cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)
cv2.resizeWindow(windowName, 1280, 720)
cv2.moveWindow(windowName, 0, 0)
cv2.setWindowTitle(windowName, "Canny Edge Detection")
showWindow = 3 # Show all stages
showHelp = True
font = cv2.FONT_HERSHEY_PLAIN
helpText = "'Esc' to Quit, '1' for Camera Feed, '2' for Canny Detection, '3' for All Stages. '4' to hide help"
edgeThreshold = 40
showFullScreen = False
while True:
if cv2.getWindowProperty(
windowName,
0) < 0: # Check to see if the user closed the window
# This will fail if the user closed the window; Nasties get printed to the console
break
ret_val, frame = cap.read()
cv2_im = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
pil_im = Image.fromarray(cv2_im)
pil_image, num_boxes = yolo.detect_image(pil_im)
#if num_boxes > 0:
#pil_image.save('detected_image_' + str(i) + '.jpg')
open_cv_image = np.array(pil_image)
# Convert RGB to BGR
edges = open_cv_image[:, :, ::-1].copy()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(hsv, (7, 7), 1.5)
#edges=cv2.Canny(blur,0,edgeThreshold)
if showWindow == 3: # Need to show the 4 stages
# Composite the 2x2 window
# Feed from the camera is RGB, the others gray
# To composite, convert gray images to color.
# All images must be of the same type to display in a window
"""
frameRs=cv2.resize(frame, (640,360))
hsvRs=cv2.resize(hsv,(640,360))
vidBuf = np.concatenate((frameRs, cv2.cvtColor(hsvRs,cv2.COLOR_GRAY2BGR)), axis=1)
blurRs=cv2.resize(blur,(640,360))
edgesRs=cv2.resize(edges,(640,360))
#vidBuf1 = np.concatenate( (cv2.cvtColor(blurRs,cv2.COLOR_GRAY2BGR),cv2.cvtColor(edgesRs,cv2.COLOR_GRAY2BGR)), axis=1)
vidBuf1 = np.concatenate((cv2.cvtColor(blurRs,cv2.COLOR_GRAY2BGR),edgesRs), axis=1)
vidBuf = np.concatenate( (vidBuf, vidBuf1), axis=0)
"""
edgesRs = cv2.resize(edges, (640, 360))
vidBuf = edgesRs
if showWindow == 1: # Show Camera Frame
displayBuf = frame
elif showWindow == 2: # Show Canny Edge Detection
displayBuf = edges
elif showWindow == 3: # Show All Stages
displayBuf = vidBuf
if showHelp == True:
cv2.putText(displayBuf, helpText, (11, 20), font, 1.0,
(32, 32, 32), 4, cv2.LINE_AA)
cv2.putText(displayBuf, helpText, (10, 20), font, 1.0,
(240, 240, 240), 1, cv2.LINE_AA)
cv2.imshow(windowName, displayBuf)
key = cv2.waitKey(10)
if key == 27: # Check for ESC key
cv2.destroyAllWindows()
break
elif key == 49: # 1 key, show frame
cv2.setWindowTitle(windowName, "Camera Feed")
showWindow = 1
elif key == 50: # 2 key, show Canny
cv2.setWindowTitle(windowName, "Canny Edge Detection")
showWindow = 2
elif key == 51: # 3 key, show Stages
cv2.setWindowTitle(
windowName,
"Camera, Gray scale, Gaussian Blur, Canny Edge Detection")
showWindow = 3
elif key == 52: # 4 key, toggle help
showHelp = not showHelp
elif key == 44: # , lower canny edge threshold
edgeThreshold = max(0, edgeThreshold - 1)
print('Canny Edge Threshold Maximum: ', edgeThreshold)
elif key == 46: # , raise canny edge threshold
edgeThreshold = edgeThreshold + 1
print('Canny Edge Threshold Maximum: ', edgeThreshold)
elif key == 74: # Toggle fullscreen; This is the F3 key on this particular keyboard
# Toggle full screen mode
if showFullScreen == False:
cv2.setWindowProperty(windowName, cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
else:
cv2.setWindowProperty(windowName, cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_NORMAL)
showFullScreen = not showFullScreen
else:
print("camera open failed")
def main(file_name, starting_value):
yolo = YOLO()
file_name = file_name
starting_value = starting_value
print("Starting in....")
for i in list(range(5))[::-1]:
print(i+1)
time.sleep(1)
training_data = []
paused = False
while(True):
if not paused:
image_array = grab_screen(region=(0, 30, 1280, 750))
array_to_image = Image.fromarray(image_array, mode='RGB')
r_image, left_top_x, left_top_y, right_bottom_x, right_bottom_y, predict_classes = yolo.detect_image(
array_to_image)
img = np.asarray(r_image)
img = collision_detection(img, left_top_x, left_top_y, right_bottom_x, right_bottom_y, predict_classes)
new_screen, original_image = process_img(img)
screen = preprocess_screen(original_image)
keys = key_check()
output = keys_to_output(keys)
training_data.append([screen, output])
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
if len(training_data) % 100 == 0:
print(len(training_data))
if len(training_data) == 500:
np.save(file_name, training_data)
print('SAVED')
training_data = []
starting_value += 1
file_name = 'F:/.../self-driving-in-GTA5/collect_data/training_data-{}.npy'.format( # Change it to your path
starting_value)
keys = key_check()
if 'T' in keys:
if paused:
paused = False
print('Unpaused!')
time.sleep(1)
else:
print('Pausing!')
paused = True
time.sleep(1)
class NebrusScreenDemo(QtGui.QMainWindow):
def __init__(self, parent=None):
super(NebrusScreenDemo, self).__init__(parent)
#### Create Gui Elements ###########
self.mainbox = QtGui.QWidget()
self.screen_resolution = app.desktop().screenGeometry()
kw = 0.4
kh = 0.3
#self.setFixedSize(screen_resolution.width(), int(screen_resolution.height() * 0.936)) # 0.636))
self.setFixedSize(int(self.screen_resolution.width() * kh), int(self.screen_resolution.height() * kw)) # 0.636))
self.setCentralWidget(self.mainbox)
self.mainbox.setLayout(QtGui.QGridLayout())
# QGridLayout#addWidget(): (QWidget * widget, int fromRow, int fromColumn, int rowSpan, int columnSpan, Qt::Alignment alignment = 0)
# self.canvas = pg.GraphicsLayoutWidget()
# self.mainbox.layout().addWidget(self.canvas, 0, 1, 1, 6) # last param = number of buttons + 1
self.image_view = pg.ImageView()
self.mainbox.layout().addWidget(self.image_view, 0, 0, 1, 1)
self.image_width = int(self.screen_resolution.width() - self.screen_resolution.width() * kw)
self.image_height = int(self.screen_resolution.height() - self.screen_resolution.height() * kh)
# sct = mss()
# # pdb.set_trace() ###
# monitor = self.get_mss_monitor()
# image = np.array(sct.grab(monitor))
# image = np.flip(image[:, :, :3], 2)
# image = np.rot90(image, axes=(-2, -1))
# self.image_view.setImage(image, xvals=np.linspace(1., 3., image.shape[0]))
# self.image_view.autoRange()
# Grab frame
self.frame_window = QtGui.QMainWindow()
monitor = self.get_mss_monitor()
self.frame_window.setFixedSize(monitor["width"], monitor["height"])
self.frame_window.move(monitor["left"], monitor["top"])
self.move(0, 0)
self.frame_window.setWindowOpacity(0.5)
self.frame_window.setAttribute(Qt.WA_NoSystemBackground, True)
self.frame_window.show()
self.sct = mss()
print("Monitor: %s" % (str(self.get_mss_monitor())))
self.yolo = YOLO(**vars(FLAGS))
self.update()
def detect_screen(self):
sct = mss()
# pdb.set_trace() ###
monitor = {'top': int(self.screen_resolution.height() * 0.3), 'left': int(self.screen_resolution.width() * kw), 'width': int(self.screen_resolution.width() * self.screen_resolution.width() * kw), 'height': int(self.screen_resolution.height() * self.screen_resolution.height() * kh)}
image = np.array(sct.grab(monitor))
image = np.flip(image[:, :, :3], 2)
frame = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(frame)
image.show()
def detect_img(self, yolo):
while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
r_image = yolo.detect_image(image)
r_image.show()
yolo.close_session()
def detect_video(self, yolo, video_path, output_path=""):
import cv2
vid = cv2.VideoCapture(video_path)
if not vid.isOpened():
raise IOError("Couldn't open webcam or video")
video_FourCC = int(vid.get(cv2.CAP_PROP_FOURCC))
video_fps = vid.get(cv2.CAP_PROP_FPS)
video_size = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
isOutput = True if output_path != "" else False
if isOutput:
print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size))
out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)
accum_time = 0
curr_fps = 0
fps = "FPS: ??"
prev_time = timer()
while True:
return_value, frame = vid.read()
image = Image.fromarray(frame)
image = yolo.detect_image(image)
result = np.asarray(image)
curr_time = timer()
exec_time = curr_time - prev_time
prev_time = curr_time
accum_time = accum_time + exec_time
curr_fps = curr_fps + 1
if accum_time > 1:
accum_time = accum_time - 1
fps = "FPS: " + str(curr_fps)
curr_fps = 0
cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.50, color=(255, 0, 0), thickness=2)
cv2.namedWindow("result", cv2.WINDOW_NORMAL)
cv2.imshow("result", result)
if isOutput:
out.write(result)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
yolo.close_session()
def get_mss_monitor(self):
kw = 0.4
kh = 0.3
dpi = 1
monitor = {'top': int(self.screen_resolution.height() * dpi * 0.3),
'left': int(self.screen_resolution.width() * dpi * kw),
'width': int(self.screen_resolution.width() * dpi - self.screen_resolution.width() * dpi * kw),
'height': int(self.screen_resolution.height() * dpi - self.screen_resolution.height() * dpi * kh)}
return monitor
def update(self):
try:
# pdb.set_trace() ###
monitor = self.get_mss_monitor()
image = np.array(self.sct.grab(monitor))
#frame = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#image_ax = Image.fromarray(frame)
#image = np.flip(image[:, :, :3], 2)
#image = np.fliplr(image)
#image = scipy.ndimage.rotate(image, 90)
frame = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_ax = Image.fromarray(frame)
# image_ax = Image.fromarray(image)
tf_image = self.yolo.detect_image(image_ax)
tf_image = np.asarray(tf_image)
tf_image = np.fliplr(tf_image)
tf_image = scipy.ndimage.rotate(tf_image, 90)
self.image_view.setImage(tf_image)
# self.image_view.setImage(image)
# self.image_view.autoRange()
QtCore.QTimer.singleShot(50, self.update)
# self.counter += 1
except KeyboardInterrupt:
print("Exiting gracefully...")
# self.decode_thread.join()
# self.filter_thread.join()
# self.update_threaad.terminate()
# self.multi_pulse_thread.terminate()
except BaseException as e:
print(e)
print("update thread: %s" % (str(e)))
raise e
QtCore.QTimer.singleShot(1, self.update)
def keyPressEvent(self, e):
print(str(e.key()))
if e.key() == Qt.Key_F5:
self.close()
#预测
from yolo import YOLO
from PIL import Image
import os
import cv2
import numpy as np
yolo = YOLO()
file_path = "img"
for file in os.listdir(file_path):
filename = os.path.join(file_path, file)
try:
image = Image.open(filename)
except:
print('Open Error! Try again!')
continue
else:
#获取图像和框位置
r_image = yolo.detect_image(image)
#输出预测的框
#for value in r_image.values():
#print(value)
frame = np.array(r_image)
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
cv2.imwrite("result/result_1/" + file, frame)
#r_image.show()
#r_image.close()
#r_image.saveas(gcf,"img/")
txt = "output/totalCount.txt" # 将要输出保存的文件地址
if not os.path.exists('output/imageSeg'):
os.makedirs('output/imageSeg')
for dir in diclass.keys():
path_dir = 'output/imageSeg/' + dir
if not os.path.exists(path_dir):
os.makedirs(path_dir)
while (True):
# 读取某一帧
ref, frame = capture.read()
# 进行检测
frame, class_dict = yolo.detect_image(frame)
with open(txt, "w") as f:
for k in class_dict.keys():
f.write(k + ': ' + str(class_dict[k])) # 将字符串写入文件中
f.write("\n") # 换行
videoWrite.write(frame)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(10, 10))
plt.imshow(frame)
plt.axis('on')
plt.show()
c = cv2.waitKey(30) & 0xff
