From 137ad719539b76d5dcba88310113537e57ab1c16 Mon Sep 17 00:00:00 2001 From: tian <11429339@qq.com> Date: Tue, 21 Jul 2026 10:25:16 +0800 Subject: [PATCH] chore: remove outdated and personal scripts --- .../onnx2rknn_prenorm.cpython-311.pyc | Bin 7115 -> 0 bytes scripts/d8_1.py | 1206 ----------------- scripts/ffmpeg-push.bat | 3 - scripts/run_on_board.sh | 56 - 4 files changed, 1265 deletions(-) delete mode 100644 scripts/__pycache__/onnx2rknn_prenorm.cpython-311.pyc delete mode 100644 scripts/d8_1.py delete mode 100644 scripts/ffmpeg-push.bat delete mode 100644 scripts/run_on_board.sh diff --git a/scripts/__pycache__/onnx2rknn_prenorm.cpython-311.pyc b/scripts/__pycache__/onnx2rknn_prenorm.cpython-311.pyc deleted file mode 100644 index deca427faa05abb5322604804b919f7e290c3557..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7115 zcmdrxZEPDycDvkNe(dr?66FsmwnE946G|s3cI?)cCD}U3j^z)^mTaKWAz*3lN~TPa z^z727@Tin$2sPznYW7Yzt9K5LX`NK9g~kPntJ}JF_cQ%dER6+1EMUO6J31EnBMj1_ zhy3cBS&E1I^2Cb^XARWn>X*xym|apVmxHfGG)wfXwcP2nNwh4o zQ#R2m@uE!kbkz-L=(ruR_vQaYq3b$xE-()u z6af$q>&*G+cXehmei?DbXyH&yXD6>AMdv1D{4t-Hj|Czk!rUM%HQhQPiSeLx_JD3P za+C2nB;Xo;LJEdrC>jieaUN5hlL8T)n~RE4I7kCcIpN_Lc)vOg;0_g{7U(23Tdt*M zE3~83JMINj(lkqCi3tE>TVRq5;*zE$on!_8m*F$bNsy?Q_IVkdn?Zrt6`hU1u(~-K zS7LF+%c56+P-lTZ5w+ng9hF)5dLVthf3&}EI2P+0>F(~ExccUy!-pr|mHXr%3dIz; zFB*x=A3#?l5q}Ixktmw$jlHY8{g>mRun4Fqo+m| zENBg$62)+@5|CyKjoX%ADN!aHAONI?3$BI|MLS+Cw6BUz@biPq+9WV;lxbRpJ;=OH4>|W2X$a7w?8EN5z%3P_<%onB@l|B{ZR8J zyz(LdSY3WwYUGKdDSKe)?abRHisyC_kfsZ+9k-LWk}D&5mq&AXV7&!bdZ6ImnLU&} z1o_OsO8;Y_Qx!T3?j6~K*@Fc4!F#IE15cUG<<2BPDQqoegtI9#yV97MOEYPvAT*>8 zp}m+_hoRzrLp^FVqGD$m5ZE26S{ss7-THzxEiek!pN+ItU$XQ&?VtyyT(#Df$*Ck$ z*CUtYh{jN$72Is4XQi_HQq*+{ZG$?@CrQXzT@p=In!sd^SNBrUJ4tbrTT^(PN?*0y zi0oBTgU(gcRF6=Duhv1!f?07@jbR4c014c$NMXTRQ*YT)Z%f*cIcb&Iq&aA!u{^9$ zwn5oql<}EWD<#pqVORf;=4Z?j&(v%2t&1R1V#o+D;5wG(I^K#<0|2j;Y^KPI5qL{Q z-2im^`QgdQi>D?oVarB67{!Do!!*4-+2ax3Cy)n&J`CQ#z}WEvI(=DZ17O=adl{h2 zLmjbt0u0k{_D_c*cwZ-5{f1ScL7*IgS6&1bQ`8foZ6%%;IyIp)HTsm_VQd6%E=^^o zO4M$y`AZD&M&Q0Ze`|h)Sz5>}RJH+P@=x(L@WFs8yavze=-O%c6q>d#cdcAl?n#dn z?9GH?<x#e*K4n@~M#-nJV`tWie=o%wyVL(2MqKW+l2(A9^g*)s1*?xP z7M6z`f2)uI2a#TcBXAb-MyhryYTdB~dIk=f8rq6!!y)-C>~3ssUuaKtDM`b@)(Tr9 z9(_xyrmWqLXe!f_#0Q(nS2GDb0qnGWwF|%<@LrSH-=UNA=0tvI%J7ra-+;FRJ`VT- zUXyO>eM6^1GUj^Y1pC@HAWKRxIu{#CyzYnd5l*u}*iVi^;z7v017Yl{$wR#rN<4<# z3Lr(Pjs3>6av|HkysuIuVu!PIH&E$9C=yaafp91xA!3YZ7^BUBSWJqDWuJ@3RK}RP znJ^$DVoF$K*zgRy+x9{((HIbg;FW^_;Ls&}*4UpqU2wQkCku|I z%y%?L8#q;52LWJ$f<5yG_!#ZT29~FDr`7Gf@D%J#x6j==x6*!hU*6uX+1pin`|k=u zW9potS1?v3VJC*@)q8M&)E=UnI#s7puxu|rBt=>@tueL72mNwtR^+|`wOO=$18S>i z`v%m!D2Vno$HyRv`&b$z5zZ^6G8TX9FpJlKq+IzL zSVoX)3b<}GRk!#l^+6q%b=2;p`G2KpPO>kg z@m7txZEbt)To)`!ORj3wH)(;t?d8%8CGPl3dInAcaVP$+wQSLJOAg;XwxsG=LhDx2 z`juJ>U$7>v5D*}if|_b{a97g0bxxp(mfX%QLjgkEz1CJYznAy@>P!3HROqTfU?zC; zC+YVd!}HQH@P&Izs@)grbfCS4131k#U2Ma3^mh0X=AIsWAy2f;MK6cK(sUpwMc{Y~ zpdilrFU$LTaXp-l77=RWVI{#qlem)TcyWvJr5@k#6V{#{C4lgK(u_zTAi*2s`G1cd z2w#moJMUw`=9hpA%b$Hk@hJ<{A| zNfbkonO~qq7MGD6fo)Z%j;}iX!ai8yx zJXZp;Cld9LAc@DgI?BWikN*lbT+y47>xWTb%THY$v;*z zv-G3lqZAqFi$@t~46Hr>#gB?N|G6^V*UujCL1%sCIFk3b|MUY80nFNSX#asxPu1Kr zpD+F#CWiapB2>=+_I-vt>o?zj_TvZ7f0=#$VFvg~w7p=i*!A%ko=2kXJL6~G@tl}f zP#~yC;^ySAaQZz70ed6{@nB**DHuX}y}YX4!HZbAu1n}LMBBW~=O8wUdg?!p&cm00 zGLrh^O90mKwgC%D z0$47?gOkDK@`ppRlGwE|-*7Y_`mq9frUPIWq6Y#*gC~Jeml*Y5iw7V|m|Ei3Jcu8lA&vCO!ud$n&^}bbCM)!4eIe z&J!YBhWi#~O=mHex+Qo;3SI?rfn@w7K1e)Dgike{hv1tc&7GJJDTYJoDvQ2sP@Jql zXpUUUh=Zu}xO+c|pz03ESX`rD-Abxp4RtPtLJd#0^Xvw8Bi4$an)`gVae-yFX&o;{`RJDD2K zvu|naTPpij8U2>pIfl`vHTJa1o-U(LuUav7zsBxY+5KhgS+&cD(c>CBuCn7B=z{f? zLPJx*@x79T!natKGyf+BB?5TL?z}yCYjEjU=2&X9Xm7}jWyR%C-tN)tp45patab71 zXY8)W?5>qdd3KM+?oru2PlbleQBBwlJ~%h{#JN*#IhuD4XwCuEI`GuhocW&SYR|Q6 zuDzPen>qzoQO&jIGne`{$9 zs7A`@iccAn!T#=xV!LzG}JGMof2HPdvEm?AT&5vXxX>28-A`5M+q%o zQj7p=2Ou?4vQkzd9k?-{nkN$%LlyYhUu#&@Si z3KqVq$&m*mYReILDl&s6pYg4a`PP+#cMs?Jy&Au_zR|IVd(@U=@Z_DxHRo~FdK}O6 z?2WVE_bvKTK9EkjGuynpGtahaY^z#+jIgHF4^i4s#P9buqPMYCyn_MW3y}yww_ 20 * 60: - # 过期重新请求 token - # print("token 已过期") - response = requests.post(getTokenUrl) - if response.status_code == 200: - data = json.loads(response.text) - if 'retCode' in data and data['retCode'] == '200': - token = data['responseBody']['token'] - current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") - tokenResult['token'] = token - tokenResult['current_time'] = current_time - else: - tokenResult['error'] = data['errorDesc'] - else: - tokenResult['error'] = response.status_code - token = tokenResult['token'] - return token - - -def send_post_request(url, token, msg, picUrl, videoUrl): - payload = { - "tenantCode": "32", - "channelNo": vod_channelNo, - "alarmContent": msg, - "alarmTime": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), - "picInfo": [ - {"url": picUrl} - ], - "videoInfo": [ - {"url": videoUrl} - ] - } - headers = { - 'X-Access-Token': token, - 'Content-Type': 'application/json' - } - # print(url) - # print(headers) - # print(payload) - response = requests.post(url, headers=headers, data=json.dumps(payload)) - #print(response) - - -def clear_folder(ip): - folder_path1 = m3u8_path+ip - # 判断文件夹是否存在 - if not os.path.exists(folder_path1): - print(f"文件夹 {folder_path1} 不存在！") - os.mkdir(folder_path1) - # return - - # 判断文件夹是否为空 - if not os.listdir(folder_path1): - print(f"文件夹 {folder_path1} 为空，无需清空！") - else: - # 清空文件夹1 - for filename in os.listdir(folder_path1): - file_path = os.path.join(folder_path1, filename) - if os.path.isfile(file_path): - os.remove(file_path) - elif os.path.isdir(file_path): - shutil.rmtree(file_path) - print(f"已清空文件夹 {folder_path1} 的内容！") - - -def restart_program(): - """重新启动当前程序""" - python = sys.executable # 获取当前 Python 解释器的路径 - # print("Restarting program...") - time.sleep(1) # 可选延迟，确保用户看到提示 - os.execl(python, python, *sys.argv) # 使用相同的参数重新启动当前脚本 - - -def verify_bbox_class(classid_list, save_flag): - if "face" in classid_list: - save_flag.append("face") - return True - if "shoe" in classid_list: - save_flag.append("shoe") - return True - if "phone" in classid_list: - save_flag.append("phone") - return True - return False - - -# after_time 距离 before_time 是否在 time_num秒 之内，若在 返回true 不在返回false -def verify_timenum(before_time, after_time, time_num): - if before_time is None: - return True - time_difference = after_time - before_time - if time_difference < time_num: - return True - else: - return False - - -# 该方法用于判断两个帧是否相同 -def compare_frames(frame1, frame2, threshold): - difference = cv2.absdiff(frame1, frame2) - diff_gray = cv2.cvtColor(difference, cv2.COLOR_BGR2GRAY) - _, thresholded_diff = cv2.threshold(diff_gray, threshold, 350, cv2.THRESH_BINARY) - - return np.sum(thresholded_diff) == 0 - - -def white_color_ratio(image): - image = image.copy() - # 将图像从BGR颜色空间转换为HSV颜色空间 - hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) - # 定义白色的HSV范围（在此示例中使用了一组简单的范围） - lower_white = np.array([0, 0, 200], dtype=np.uint8) - upper_white = np.array([180, 30, 255], dtype=np.uint8) - # 创建遮罩，将白色和白灰色区域设置为白色，其他区域设置为黑色 - white_mask = cv2.inRange(hsv_image, lower_white, upper_white) - # 计算白色和白灰色区域的像素数 - white_pixels = np.count_nonzero(white_mask) - # 计算图像中白色和白灰色的占比 - total_pixels = image.shape[0] * image.shape[1] - white_ratio = white_pixels / total_pixels - return white_ratio - - -def shoe_color_ratio(image): - # 将图像从BGR颜色空间转换为HSV颜色空间 - hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) - # 定义黑色的HSV范围（在此示例中使用了一组简单的范围） - lower_black = np.array([0, 0, 0], dtype=np.uint8) - upper_black = np.array([180, 255, 80], dtype=np.uint8) - # 创建遮罩，将黑色区域设置为白色，其他区域设置为黑色 - black_mask = cv2.inRange(hsv_image, lower_black, upper_black) - # 计算黑色区域的像素数 - black_pixels = np.count_nonzero(black_mask) - # 计算图像中黑色的占比 - total_pixels = image.shape[0] * image.shape[1] - black_ratio = black_pixels / total_pixels - return black_ratio - -def skin_color_ratio(image): - # 将图像从BGR颜色空间转换为HSV颜色空间 - # 定义肤色的HSV范围（在此示例中使用了一组简单的范围） - hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) - lower_skin = np.array([0, 20, 70], dtype=np.uint8) - upper_skin = np.array([20, 255, 255], dtype=np.uint8) - # 创建遮罩，将肤色区域设置为白色，其他区域设置为黑色 - skin_mask = cv2.inRange(hsv_image, lower_skin, upper_skin) - # 计算肤色区域的像素数 - skin_pixels = np.count_nonzero(skin_mask) - # 计算图像中人体肤色的占比 - total_pixels = image.shape[0] * image.shape[1] - skin_ratio = skin_pixels / total_pixels - return skin_ratio - - -def get_img_path_batches(batch_size, img_dir): - ret = [] - batch = [] - for root, dirs, files in os.walk(img_dir): - for name in files: - if len(batch) == batch_size: - ret.append(batch) - batch = [] - batch.append(os.path.join(root, name)) - if len(batch) > 0: - ret.append(batch) - return ret - - -# 画框 返回True 为需要报警的选项，返回Flase，为不需要报警的 -def plot_one_box(x, img, color=[0, 255, 0], label=None, line_thickness=2): - - # print("label: ", label) - - c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) - # msg = '' - - if label == 'shoe': - print('识别到鞋子') - region_of_interest = img[c1[1]:c2[1], c1[0]:c2[0]] - source = shoe_color_ratio(region_of_interest) - print(f'识别到鞋子, 比率:{source}') - # white_sorce = white_color_ratio(region_of_interest) - # 鞋子黑色面积必须大于整体框的百分之50 并且框的宽度小于40，高度小于30 - if source < 0.4: - # 画框 - color = [0, 0, 255] - cv2.rectangle(img, c1, c2, color, thickness=line_thickness, lineType=cv2.LINE_AA) - return [1, '未穿戴劳保鞋'] - - if label == 'face': - - region_of_interest = img[c1[1]:c2[1], c1[0]:c2[0]] - face_source = skin_color_ratio(region_of_interest) - - print("识别到人脸, 人脸比率: ", face_source) - # white_sorce = white_color_ratio(region_of_interest) - # print("判断存在脸，且肤色为" + str(face_source)) - # 肤色面积必须大于整体框的百分之70 框的宽度大于10 - if face_source < 0.8: - # 画框 - color = [0, 0, 255] - cv2.rectangle(img, c1, c2, color, thickness=1, lineType=cv2.LINE_AA) - - print("face") - - return [1, '未佩戴口罩'] - if label == 'phone': - - print("phone") - color = [0, 0, 255] - cv2.rectangle(img, c1, c2, color, thickness=line_thickness, lineType=cv2.LINE_AA) - return [1, '识别到手机'] - - if label == 'e-bike': - print("e-bike") - color = [0, 0, 255] - cv2.rectangle(img, c1, c2, color, thickness=line_thickness, lineType=cv2.LINE_AA) - return [1, '识别到电动车'] - - if label == 'non-Helmet': - print("non-Helmet") - color = [0, 0, 255] - cv2.rectangle(img, c1, c2, color, thickness=line_thickness, lineType=cv2.LINE_AA) - return [1, '识别到未佩戴安全帽'] - - return [0, ""] - - -# def check_save_flag(save_flag): -# # 定义需要检查的类别 -# categories = ["shoe"] -# # 找出在save_flag中的类别 -# matched_categories = [category for category in categories if category in save_flag] -# # 用'-'连接匹配的类别并返回 -# return "-".join(matched_categories) - - -# 计算图像的模糊度 -def calculate_blur(frame): - # 将图片转换为灰度图 - gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) - # 计算灰度图的方差 - blur_value = cv2.Laplacian(gray, cv2.CV_64F).var() - return blur_value - - -# 先前陌生人数 -p_s_num = 0 -class FaceRecUpload(threading.Thread): - def __init__(self, ip, frame, token): - threading.Thread.__init__(self) - self.ip = ip - self.frame = frame.copy() - # self.result = result - self.new_face = False - self.token = token - self.minio_client = client - - def run(self): - global p_s_num - - # blur = calculate_blur(self.frame) - - # print(f'\n模糊度:{blur}') - et, jpeg_frame = cv2.imencode('.jpg', self.frame) - # self.ffmpeg_frame_buffer.put(jpeg_frame.tobytes()) - now_time = time.time() - result = recognition.recognize(jpeg_frame.tobytes()) - - if 'message' not in result: - - warning = list() - - # 人脸识别检测 - for i in range(len(result['result'])): - # print(self.result['result'][i]['subjects'][0]['subject'], self.result['result'][i]['subjects'][0]['similarity']) - # print(self.result['result'][i]['box']) - x1, y1, x2, y2 = result['result'][i]['box']['x_min'], result['result'][i]['box']['y_min'], \ - result['result'][i]['box']['x_max'], result['result'][i]['box']['y_max'] - - if self.frame[x1:x2+1, y1:y2+1].size > 0: - blur = calculate_blur(self.frame[x1:x2+1, y1:y2+1]) - else: - - blur = 0 - # print(f'人脸模糊度{blur}') - if blur > 10: - if 0.3 < result['result'][i]['subjects'][0]['similarity'] < 0.4: - # 设置文本参数 - font = cv2.FONT_HERSHEY_SIMPLEX - font_scale = 1 - color = (255, 0, 0) # BGR颜色 - thickness = 2 - cv2.rectangle(self.frame, (x1, y1), (x2, y2), (255, 0, 0), 2) - cv2.putText(self.frame, f'warning {result["result"][i]["subjects"][0]["similarity"]}', - (x1, y1 - 20), font, font_scale, color, thickness, - cv2.LINE_AA) - warning.append([(x1, y1), (x2, y2)]) - # 垃圾帧丢弃 - elif result['result'][i]['subjects'][0]['similarity'] <= 0.3: - # print('\n丢弃的人脸识别帧') - pass - else: - if result['result'][i]['subjects'][0]['subject'] not in d_face: - # 记录每人第一次人脸识别记录 - d_face[result['result'][i]['subjects'][0]['subject']] = time.time() - self.new_face = True - print( - f'\n新人打卡, 员工名：{result["result"][i]["subjects"][0]["subject"]} ,相似度：{result["result"][i]["subjects"][0]["similarity"]},打卡时间{datetime.datetime.now()}') - uuid_str = str(uuid.uuid4())[:6] + str(int(time.time())) - img_object_name = f"{save_path}{result['result'][i]['subjects'][0]['subject']}_{uuid_str}_{vod_channelNo}.jpg" - cv2.imwrite(img_object_name, self.frame) - # print(f'\n/home/admin-root/haotian/jingzhu1.1/tensorrtx-master/yolov8/attendance/{datetime.datetime.now().date()}.txt') - with open( - f'{people_save_path}{datetime.datetime.now().date()}.txt', - 'a') as f: - f.write( - f'新人打卡, 员工名：{result["result"][i]["subjects"][0]["subject"]} ,相似度：{result["result"][i]["subjects"][0]["similarity"]},打卡时间{datetime.datetime.now()} \n') - else: - pass - # print('\r模糊帧丢弃', end='') - - if len(warning) > p_s_num: - p_s_num = len(warning) - uuid_str = str(uuid.uuid4())[:6] + str(int(time.time())) # 生成UUID的前6位,前6位，有不小的概率重复，再加上时间戳。 - img_object_name = f"{save_path}face_{uuid_str}_{vod_channelNo}.jpg" - cv2.imwrite(img_object_name, self.frame) - - # with open(img_object_name, 'rb') as file_data: - # file_data.seek(0, os.SEEK_END) - # file_size = file_data.tell() - # file_data.seek(0) - # self.minio_client.put_object(configData['minioConfig']['bucket_name'], - # f'{uuid_str}_{vod_channelNo}_stranger_.jpg', file_data, - # file_size) - - # os.remove(img_object_name) - upload_http_url_img = configData['minioConfig'][ - 'bucket_name'] + f'/{uuid_str}_{vod_channelNo}_stranger_.jpg' - # send_post_request(putMessageUrl, self.token, '陌生人警告', upload_http_url_img, '') - print('\n陌生人警告') - #print('陌生人图片保存完成') - elif len(warning) < p_s_num: - p_s_num = len(warning) - - self.new_face = False - - #print(f'\r人脸识别任务完成，完成时间：{time.time() - now_time}', end='') - # if blur > 750: - # - # else: - # print('\r模糊帧不做人脸识别处理', end='') - - - -class NewSaveAndUploadMP4Thread(threading.Thread): - def __init__(self, ip, frame_buffer, save_type, token, msg_list = None): - ''' - - :param ip: 摄像头标识 - :param frame_buffer: 异常帧缓存 - :param save_type: 保存类型 - ''' - threading.Thread.__init__(self) - self.ip = ip - # 初始化时将异常帧转换为队列，你单纯的赋值其实是一个对象引用，若源对象变了，这里也会变。 - # 线程start后可能不会立即得到执行，若没执行，源队列被清空了，这里的队列也会变空？ - # 所以这里先将其转换成list存储。 - self.frames = list(frame_buffer.queue) - self.save_type = save_type - self.minio_client = client - self.token = token - self.msg_list = msg_list - - def run(self): - # 所以这里是可能出现重复的啊，300次生成有10次会重复。。加个时间戳吧 - now_time = str(int(time.time())) - uuid_str = str(uuid.uuid4())[:6] + now_time # 生成UUID的前6位 - - - - # 将异常帧存储到列表中 - if self.save_type == 'picture': - print("图片上传消息", self.msg_list, "视频长度: ", len(self.frames)) - - first_frame = self.frames[0] - img_file_path = f"{save_path}{uuid_str}_{vod_channelNo}_.jpg" - cv2.imwrite(img_file_path, first_frame) - - # with open(img_file_path, 'rb') as file_data: - # file_data.seek(0, os.SEEK_END) - # file_size = file_data.tell() - # file_data.seek(0) - # self.minio_client.put_object(configData['minioConfig']['bucket_name'], f'{uuid_str}_{vod_channelNo}_.jpg', file_data, - # file_size) - - upload_http_url_img = configData['minioConfig']['bucket_name'] + f'/{uuid_str}_{vod_channelNo}_.jpg' - - msg = self.msg_list[0] if len(self.msg_list) > 0 else "" - - # upload_http_url_img = configData['minioConfig']['bucket_name'] + f'/{uuid_str}_{vod_channelNo}_.jpg' - # send_post_request(putMessageUrl, self.token, msg, upload_http_url_img, '') - # os.remove(img_object_name) - print('\n上传图片完成') - self.msg_list.clear() - elif self.save_type == 'video': - print("视频上传消息", self.msg_list, "视频长度: ", len(self.frames)) - - mp4_file_path = f'{save_path}{uuid_str}_{vod_channelNo}_.mp4' - height, width, _ = self.frames[0].shape - - # 保存视频 - out = cv2.VideoWriter(mp4_file_path, cv2.VideoWriter_fourcc(*'mp4v'), 25, (width, height), isColor=True) - for frame in self.frames: - out.write(frame) - out.release() - - mp4_object_name = f"{uuid_str}_{vod_channelNo}_.mp4" - - # self.temp_file_path = os.path.abspath(mp4_file_path) - self.temp_file_size = os.path.getsize(mp4_file_path) - - # with open(mp4_file_path, 'rb') as file_data: - # #上传到minio - # self.minio_client.put_object(configData['minioConfig']['bucket_name'], mp4_object_name, file_data, - # self.temp_file_size) - - - upload_http_url_mp4 = configData['minioConfig']['bucket_name'] + f'/{uuid_str}_{vod_channelNo}_.mp4' - msg = self.msg_list[0] - # 上传警告。 - # send_post_request(putMessageUrl, self.token, msg, '', upload_http_url_mp4) - # os.remove(mp4_file_path) - print('\n上传视频成功') - self.msg_list.clear() - - else: - print('\n异常类型') - - - - -class FramePushThread(threading.Thread): - def __init__(self, frame_buffer, process_mid): - threading.Thread.__init__(self) - self.frame_buffer = frame_buffer - self.process_mid = process_mid - - def run(self): - while True: - frame_data = self.frame_buffer.get() - self.process_mid.stdin.write(frame_data) - - -class YoLov8TRT(object): - - def __init__(self, engine_file_path): - # Create a Context on this device, - self.ctx = cuda.Device(0).make_context() - stream = cuda.Stream() - TRT_LOGGER = trt.Logger(trt.Logger.INFO) - runtime = trt.Runtime(TRT_LOGGER) - - # Deserialize the engine from file - with open(engine_file_path, "rb") as f: - engine = runtime.deserialize_cuda_engine(f.read()) - context = engine.create_execution_context() - - host_inputs = [] - cuda_inputs = [] - host_outputs = [] - cuda_outputs = [] - bindings = [] - - for binding in engine: - print('bingding:', binding, engine.get_binding_shape(binding)) - size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size - dtype = trt.nptype(engine.get_binding_dtype(binding)) - # Allocate host and device buffers - host_mem = cuda.pagelocked_empty(size, dtype) - cuda_mem = cuda.mem_alloc(host_mem.nbytes) - # Append the device buffer to device bindings. - bindings.append(int(cuda_mem)) - # Append to the appropriate list. - if engine.binding_is_input(binding): - self.input_w = engine.get_binding_shape(binding)[-1] - self.input_h = engine.get_binding_shape(binding)[-2] - host_inputs.append(host_mem) - cuda_inputs.append(cuda_mem) - else: - host_outputs.append(host_mem) - cuda_outputs.append(cuda_mem) - - # Store - self.stream = stream - self.context = context - self.engine = engine - self.host_inputs = host_inputs - self.cuda_inputs = cuda_inputs - self.host_outputs = host_outputs - self.cuda_outputs = cuda_outputs - self.bindings = bindings - self.batch_size = engine.max_batch_size - - def infer(self, image_frame): - threading.Thread.__init__(self) - # Make self the active context, pushing it on top of the context stack. - self.ctx.push() - # Restore - stream = self.stream - context = self.context - engine = self.engine - host_inputs = self.host_inputs - cuda_inputs = self.cuda_inputs - host_outputs = self.host_outputs - cuda_outputs = self.cuda_outputs - bindings = self.bindings - # Do image preprocess - batch_image_raw = [] - batch_origin_h = [] - batch_origin_w = [] - batch_input_image = np.empty(shape=[self.batch_size, 3, self.input_h, self.input_w]) - # 方法遍历由 raw_image_generator 生成的图像，对每个图像执行预处理，并收集处理后的图像数据以及原始图像的尺寸。 - # 预处理可能包括调整大小、标准化等步骤，为推理准备适当的输入格式。 - # for i, image_raw in enumerate(image_frame): - input_image, image_raw, origin_h, origin_w = self.preprocess_image(image_frame) - batch_image_raw.append(image_frame) - batch_origin_h.append(origin_h) - batch_origin_w.append(origin_w) - np.copyto(batch_input_image[0], input_image) - batch_input_image = np.ascontiguousarray(batch_input_image) - - # Copy input image to host buffer - np.copyto(host_inputs[0], batch_input_image.ravel()) - # 记录推理开始时间 - # start = time.time() - # 将输入数据传输到GPU - cuda.memcpy_htod_async(cuda_inputs[0], host_inputs[0], stream) - # 执行异步推理 - context.execute_async(batch_size=self.batch_size, bindings=bindings, stream_handle=stream.handle) - # 将预测结果传回主机 - cuda.memcpy_dtoh_async(host_outputs[0], cuda_outputs[0], stream) - # 同步CUDA流 - stream.synchronize() - - # 并移除上下文 - self.ctx.pop() - output = host_outputs[0] - - # 后处理和结果解析： - for i in range(self.batch_size): - # for循环中 调用self.post_process方法进行后处理，提取结果框、得分和类别ID。 此处不需要特别注意 了解获得返回结果即可 - result_boxes, result_scores, result_classid = self.post_process( - output[i * 38001: (i + 1) * 38001], batch_origin_h[i], batch_origin_w[i] - ) - result_list = 0 - result_boxes_list = list() - msg_list = list() - for j in range(len(result_boxes)): - box = result_boxes[j] - # ！！！！！！！！！！！这个推理的地方 后续可判断一张图片中是否需要保存 若保存调用保存方法 - # plot_one_box 方法在原始图像上绘制检测到的每个对象的边界框和标签 - t = int(result_classid[j]) - # print("t: ", t) - if t in testclasses: - result, msg = plot_one_box( - box, - batch_image_raw[i], - label="{}".format(categories[t]) - - ) - # print("result: ", result) - # 将检测结果保存到minio中 - result_list += result - if result == 1: - result_boxes_list.append(box) - msg_list.append(msg) - return batch_image_raw, result_list, result_boxes_list, msg_list - - def destroy(self): - # Remove any context from the top of the context stack, deactivating it. - self.ctx.pop() - - def get_raw_image_zeros(self, image_path_batch=None): - """ - description: Ready data for warmup - """ - for _ in range(self.batch_size): - yield np.zeros([self.input_h, self.input_w, 3], dtype=np.uint8) - - def preprocess_image(self, raw_bgr_image): - image_raw = raw_bgr_image - h, w, c = image_raw.shape - image = cv2.cvtColor(image_raw, cv2.COLOR_BGR2RGB) - # Calculate widht and height and paddings - r_w = self.input_w / w - r_h = self.input_h / h - if r_h > r_w: - tw = self.input_w - th = int(r_w * h) - tx1 = tx2 = 0 - ty1 = int((self.input_h - th) / 2) - ty2 = self.input_h - th - ty1 - else: - tw = int(r_h * w) - th = self.input_h - tx1 = int((self.input_w - tw) / 2) - tx2 = self.input_w - tw - tx1 - ty1 = ty2 = 0 - # Resize the image with long side while maintaining ratio - image = cv2.resize(image, (tw, th)) - # Pad the short side with (128,128,128) - image = cv2.copyMakeBorder( - image, ty1, ty2, tx1, tx2, cv2.BORDER_CONSTANT, None, (128, 128, 128) - ) - image = image.astype(np.float32) - # Normalize to [0,1] - image /= 255.0 - # HWC to CHW format: - image = np.transpose(image, [2, 0, 1]) - # CHW to NCHW format - image = np.expand_dims(image, axis=0) - # Convert the image to row-major order, also known as "C order": - image = np.ascontiguousarray(image) - return image, image_raw, h, w - - def xywh2xyxy(self, origin_h, origin_w, x): - """ - description: Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - param: - origin_h: height of original image - origin_w: width of original image - x: A boxes numpy, each row is a box [center_x, center_y, w, h] - return: - y: A boxes numpy, each row is a box [x1, y1, x2, y2] - """ - y = np.zeros_like(x) - r_w = self.input_w / origin_w - r_h = self.input_h / origin_h - if r_h > r_w: - y[:, 0] = x[:, 0] - y[:, 2] = x[:, 2] - y[:, 1] = x[:, 1] - (self.input_h - r_w * origin_h) / 2 - y[:, 3] = x[:, 3] - (self.input_h - r_w * origin_h) / 2 - y /= r_w - else: - y[:, 0] = x[:, 0] - (self.input_w - r_h * origin_w) / 2 - y[:, 2] = x[:, 2] - (self.input_w - r_h * origin_w) / 2 - y[:, 1] = x[:, 1] - y[:, 3] = x[:, 3] - y /= r_h - - return y - - def post_process(self, output, origin_h, origin_w): - # Get the num of boxes detected - num = int(output[0]) - # Reshape to a two dimentional ndarray - pred = np.reshape(output[1:], (-1, 38))[:num, :] - # Do nms - boxes = self.non_max_suppression(pred, origin_h, origin_w, conf_thres=CONF_THRESH, nms_thres=IOU_THRESHOLD) - result_boxes = boxes[:, :4] if len(boxes) else np.array([]) - result_scores = boxes[:, 4] if len(boxes) else np.array([]) - result_classid = boxes[:, 5] if len(boxes) else np.array([]) - return result_boxes, result_scores, result_classid - - def bbox_iou(self, box1, box2, x1y1x2y2=True): - """ - description: compute the IoU of two bounding boxes - param: - box1: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h)) - box2: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h)) - x1y1x2y2: select the coordinate format - return: - iou: computed iou - box_iou - """ - if not x1y1x2y2: - # Transform from center and width to exact coordinates - b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2 - b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2 - b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2 - b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2 - else: - # Get the coordinates of bounding boxes - b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3] - b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3] - - # Get the coordinates of the intersection rectangle - inter_rect_x1 = np.maximum(b1_x1, b2_x1) - inter_rect_y1 = np.maximum(b1_y1, b2_y1) - inter_rect_x2 = np.minimum(b1_x2, b2_x2) - inter_rect_y2 = np.minimum(b1_y2, b2_y2) - # Intersection area - inter_area = np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, None) * \ - np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, None) - # Union Area - b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1) - b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1) - - iou = inter_area / (b1_area + b2_area - inter_area + 1e-16) - - return iou - - def non_max_suppression(self, prediction, origin_h, origin_w, conf_thres=0.5, nms_thres=0.4): - boxes = prediction[prediction[:, 4] >= conf_thres] - boxes[:, :4] = self.xywh2xyxy(origin_h, origin_w, boxes[:, :4]) - boxes[:, 0] = np.clip(boxes[:, 0], 0, origin_w - 1) - boxes[:, 2] = np.clip(boxes[:, 2], 0, origin_w - 1) - boxes[:, 1] = np.clip(boxes[:, 1], 0, origin_h - 1) - boxes[:, 3] = np.clip(boxes[:, 3], 0, origin_h - 1) - confs = boxes[:, 4] - boxes = boxes[np.argsort(-confs)] - - keep_boxes = [] - while boxes.shape[0]: - large_overlap = self.bbox_iou(np.expand_dims(boxes[0, :4], 0), boxes[:, :4]) > nms_thres - label_match = boxes[0, -1] == boxes[:, -1] - # Indices of boxes with lower confidence scores, large IOUs and matching labels - invalid = large_overlap & label_match - keep_boxes += [boxes[0]] - boxes = boxes[~invalid] - boxes = np.stack(keep_boxes, 0) if len(keep_boxes) else np.array([]) - return boxes - -def get_attendance_p(): - # 万一今天重启了，直接从文件中读取以打卡的人数，避免重复打卡 - global d_face - try: - with open(f'{people_save_path}{datetime.datetime.now().date()}.txt','r') as f: - for line in f: - d_face[line.strip().split(' ')[1][4:]] = 1 - except: - pass - - -def connect_to_rtsp_stream(url): - """ 尝试连接到 RTSP 流 """ - cap = cv2.VideoCapture(url) - cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'HEVC')) - if not cap.isOpened(): - print(f"Failed to connect to {url}") - return None - return cap - - -class rtspInputFrame(threading.Thread): - def __init__(self, video_path, rtsp_frame_buffer): - threading.Thread.__init__(self) - self.video_path = video_path - self.rtsp_frame_buffer = rtsp_frame_buffer # rtsp输入缓冲区的队列 - - def run(self): - cap = connect_to_rtsp_stream(self.video_path) - while True: - - if cap is None: - cap = connect_to_rtsp_stream(self.video_path) - time.sleep(5) - else: - # 从视频流中读取一帧 - ret, frame = cap.read() - # 如果帧读取成功，则 ret 为 True - if not ret: - print("无法读取帧，尝试重新连接...") - # 关闭当前的 VideoCapture 对象 - cap.release() - # 尝试重新连接 - while True: - cap = connect_to_rtsp_stream(self.video_path) - if cap is not None: - break - # 等待一段时间后再尝试连接 - time.sleep(5) # 休眠5秒 - - else: - if rtsp_frame_buffer.full(): - continue # 如果队列满了，就丢弃最早的帧 这个地方可能有线程安全问题 - frame = cv2.resize(frame, (1280, 720)) - rtsp_frame_buffer.put(frame) - # 释放资源 - cap.release() - cv2.destroyAllWindows() - - -class inferThread(threading.Thread): - def __init__(self, yolov8_wrapper, video_path, ip, rtsp_frame_buffer, tokenResult, ffmpeg_buffer_size=300, - save_buffer_size=2500): - threading.Thread.__init__(self) - self.ip = ip - self.yolov8_wrapper = yolov8_wrapper - self.video_path = video_path - self.rtsp_frame_buffer = rtsp_frame_buffer - self.tokenResult = tokenResult - self.ffmpeg_buffer_size = ffmpeg_buffer_size - self.ffmpeg_frame_buffer = queue.Queue(maxsize=ffmpeg_buffer_size) # ffmpeg输出到m3u6的队列，输出切片缓冲区用于存储视频帧 - self.save_frame_buffer = queue.Queue(maxsize=save_buffer_size) # 用于保存视频的缓存 - - self.msg_buffer = list() # 保存报错原因 - - self.frame_count = 0 # 帧计数器 - self.start_warning_time = None # 记录告警开始时间 - self.latest_warning_time = None # 记录告警结束时间 - self.old_update_warning_time = time.time() - 3600 # 上一次成功告警的时间 - - # 两帧一次目标检测，5帧一次人脸识别，时间不够啊 - self.det_gap = 0 - self.face_gap = 0 - - self.p_num_error = 0 - - # 上一帧的检测框作为没有检测帧的默认结果。只要位置不发生太大变化，结果不会差别太大。 - self.p_box_list = list() - - def run(self): - - print('检测线程启动') - - frame_push_thread = FramePushThread(self.ffmpeg_frame_buffer, pipeline_mid) - frame_push_thread.start() - - # 存的是目标检测的上一次结果图片 - previous_frame = None - previous_fram_face = None - # save_flag = [] - self.tokenResult['token'] = 'token' - self.tokenResult['current_time'] = '2024-01-01 00:00:00' - - # 这里没必要每一帧都检测吧，直接隔1帧检测一帧. - while True: - frame = rtsp_frame_buffer.get() - self.face_gap += 1 - self.det_gap += 1 - # 人脸识别 - if self.face_gap > 4: - # 上传token - # token = get_token(self.tokenResult) - token = {} - # 直接将帧加入到ffmpeg缓存中 - ret, jpeg_frame = cv2.imencode('.jpg', frame) - self.ffmpeg_frame_buffer.put(jpeg_frame.tobytes()) - if previous_fram_face is not None: - result_same = compare_frames(previous_fram_face, frame, 100) - else: - result_same = False - if not result_same: - face_save = FaceRecUpload(self.ip, frame, token) - face_save.start() - # print('\r 人脸识别任务', end='') - self.face_gap = 0 - # pass - # 异常检测 - elif self.det_gap > 1: - self.det_gap = 0 - if previous_frame is not None: - result_same = compare_frames(previous_frame, frame, 100) - else: - result_same = False - - if not result_same: - - # token = get_token(self.tokenResult) - # print(token) - token = {} - - batch_image_raw, r_list, box_list, msg_list = self.yolov8_wrapper.infer(frame) - - # print("r_list: ", r_list) - # print("p_num_error", self.p_num_error) - - # 上一帧检测结果 - self.p_box_list = box_list - - previous_frame = frame - - # 将处理完的帧加入到 ffmpeg缓存中 - ret, jpeg_frame = cv2.imencode('.jpg', frame) - self.ffmpeg_frame_buffer.put(jpeg_frame.tobytes()) - - now_time = time.time() - - if r_list > self.p_num_error: - self.p_num_error = r_list - self.save_frame_buffer.put(frame) - self.msg_buffer.append(msg_list[0]) - self.frame_count += 1 - print(f'\n异常人数增长, 当前异常帧数量: {self.frame_count}', end='') - - - # 开始时间记录 - self.start_warning_time = now_time - - if self.save_frame_buffer.full(): - - print('\n告警队列满-执行保存') - save_thread = NewSaveAndUploadMP4Thread(self.ip, self.save_frame_buffer, 'video', token, self.msg_buffer) - save_thread.start() - - self.frame_count = 0 - self.save_frame_buffer.queue.clear() - # self.msg_buffer.clear() - - self.start_warning_time = None - - # 记录当前警告时间 - self.latest_warning_time = now_time - - # 当前异常人数不变。 - elif r_list == self.p_num_error and self.p_num_error != 0: - - # token = get_token(self.tokenResult) - token = {} - - # 记录当前警告时间 - self.latest_warning_time = now_time - - # 判断当前的报警时间是否在10秒内 - upload = verify_timenum(self.start_warning_time, now_time, 10) - - # 在10秒内，将当前异常帧加入异常缓存 - if upload: - self.save_frame_buffer.put(frame) - - self.frame_count += 1 - print(f'\r检测到异常,异常开始时间{self.start_warning_time} ,将当前异常帧加入异常帧缓存，当前异常数量：{self.p_num_error},当前异常帧数量: {self.frame_count} ' - f'', end='') - - # 异常帧缓存满了 - if self.save_frame_buffer.full(): - print('\n告警缓存满，保存为视频') - - # 上传视频/图片 - save_thread = NewSaveAndUploadMP4Thread(self.ip, self.save_frame_buffer, 'video', token, self.msg_buffer) - save_thread.start() - # save_flag.clear() - self.frame_count = 0 - self.save_frame_buffer.queue.clear() - # self.msg_buffer.clear() - # self.old_update_warning_time = now_time - self.start_warning_time = now_time - - else: - - #print(f'\r检测到异常帧，但异常人数没变化，当前异常人数为{r_list}。', end='') - - if not self.save_frame_buffer.empty(): - print('\n10s 视频开始保存') - save_thread = NewSaveAndUploadMP4Thread(self.ip, self.save_frame_buffer, - 'video', token, self.msg_buffer) - save_thread.start() - # save_flag.clear() - self.frame_count = 0 - self.save_frame_buffer.queue.clear() - # self.msg_buffer.clear() - # self.old_update_warning_time = now_time - self.start_warning_time = now_time - 11 - - elif r_list == self.p_num_error and self.p_num_error == 0: - pass - #print(f'\r无异常', end='') - elif r_list < self.p_num_error: - # 间隔小于4秒的认为是误判 - change_flag = verify_timenum(self.latest_warning_time, now_time, 4) - if not change_flag: - - self.p_num_error = r_list - print(f'\n异常人数减少,当前异常人数：{self.p_num_error},当前异常帧数量:{self.frame_count}') - - # 将当前缓存中的异常帧上传 - if self.save_frame_buffer.qsize()> 20: - - # 4秒后才保存视频,这时msg_list已经是空的了. - save_thread = NewSaveAndUploadMP4Thread(self.ip, self.save_frame_buffer, 'video', token, self.msg_buffer) - save_thread.start() - elif self.save_frame_buffer.qsize()> 0: - save_thread = NewSaveAndUploadMP4Thread(self.ip, self.save_frame_buffer, 'picture', token, self.msg_buffer) - save_thread.start() - - - self.frame_count = 0 - self.save_frame_buffer.queue.clear() - # self.msg_buffer.clear() - - self.start_warning_time = None - else: - pass - # print('\r程序可能的误判', end='') - else: - # 一样的帧直接加入到ffmpeg缓存中 - # print('\r相同帧不需要推理', end='') - ret, jpeg_frame = cv2.imencode('.jpg', frame) - self.ffmpeg_frame_buffer.put(jpeg_frame.tobytes()) - else: - # 什么都不干的帧,直接以上一次的推理结果画框。 - for i in range(len(self.p_box_list)): - c1, c2 = (int(self.p_box_list[i][0]), int(self.p_box_list[i][1])), (int(self.p_box_list[i][2]), int(self.p_box_list[i][3])) - cv2.rectangle(frame, c1, c2, (0, 0, 255), 2) - uuid_str = str(uuid.uuid4())[:6] + str(int(time.time())) - # img_object_name = f"/home/admin-root/haotian/jingzhu1.1/tensorrtx-master/yolov8/mp4/skip_{uuid_str}_{vod_channelNo}.jpg" - # cv2.imwrite(img_object_name, frame) - ret, jpeg_frame = cv2.imencode('.jpg', frame) - self.ffmpeg_frame_buffer.put(jpeg_frame.tobytes()) - - - - - -if __name__ == "__main__": - print( - "=============================================================================================================") - # load custom plugin and engine - PLUGIN_LIBRARY = f"{engine_path}libmyplugins.so" - engine_file_path = f"{engine_path}best.engine" - # time.sleep() - - # 执行python代码命令行 参数判断操作（不做处理） - if len(sys.argv) > 1: - engine_file_path = sys.argv[1] - if len(sys.argv) > 2: - PLUGIN_LIBRARY = sys.argv[2] - clear_folder(ip) - # 加载动态链接库 - ctypes.CDLL(PLUGIN_LIBRARY) - # 手动输入训练时的类别, 不需要了直接从配置文件中读 - print("当前类别", categories) - # categories = ["shoe"] - # 加载模型文件 - yolov8_wrapper1 = YoLov8TRT(engine_file_path) - - # 重启后自动根据文件，加载当天打卡信息。根据程序设置，每天凌晨0点更新打卡数据 - get_attendance_p() - - try: - thread_rtsp = rtspInputFrame(vod_path, - rtsp_frame_buffer) - thread_det_1 = inferThread(yolov8_wrapper1, vod_path, ip, rtsp_frame_buffer, tokenResult) - thread_rtsp.start() - thread_det_1.start() - thread_rtsp.join() - thread_det_1.join() - - finally: - # destroy the instance - thread_det_1.destroy() diff --git a/scripts/ffmpeg-push.bat b/scripts/ffmpeg-push.bat deleted file mode 100644 index f8a8e09..0000000 --- a/scripts/ffmpeg-push.bat +++ /dev/null @@ -1,3 +0,0 @@ -@echo off -chcp 65001 >nul -"C:\Users\Tellme\AppData\Local\Microsoft\WinGet\Packages\Gyan.FFmpeg_Microsoft.Winget.Source_8wekyb3d8bbwe\ffmpeg-8.0.1-full_build\bin\ffmpeg.exe" -re -stream_loop -1 -i C:\Users\Tellme\Pictures\人脸库\reg_008_unk_011_多人_正面_黑色鞋_白色鞋_1.mp4 -c copy -rtsp_transport tcp -f rtsp rtsp://10.0.0.49:8554/cam diff --git a/scripts/run_on_board.sh b/scripts/run_on_board.sh deleted file mode 100644 index 85bbbaf..0000000 --- a/scripts/run_on_board.sh +++ /dev/null @@ -1,56 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -usage() { - cat <<'EOF' -Usage: run_on_board.sh --host --user --build-dir - -Copies build artifacts (pass the install directory for best results) to the RK3588 board and runs sanity commands. -EOF -} - -HOST="" -USER="root" -BUILD_DIR="build/rk3588" - -while [[ $# -gt 0 ]]; do - case "$1" in - --host) - HOST="$2"; shift 2;; - --user) - USER="$2"; shift 2;; - --build-dir) - BUILD_DIR="$2"; shift 2;; - -h|--help) - usage; exit 0;; - *) - echo "Unknown arg: $1" >&2 - usage; exit 1;; - esac -done - -if [[ -z "$HOST" ]]; then - echo "--host is required" >&2 - exit 1 -fi - -ARTIFACT_DIR="$BUILD_DIR" -if [[ ! -d "$ARTIFACT_DIR" ]]; then - echo "Build directory $ARTIFACT_DIR not found" >&2 - exit 1 -fi - -REMOTE_DIR="/tmp/safesight_media" - -scp -r "$ARTIFACT_DIR" "$USER@$HOST:$REMOTE_DIR" - -ssh "$USER@$HOST" <