def select_images_loader(src_data_type, src_data_path):
if src_data_type == "video":
images_loader = lib_images_io.ReadFromVideo(
src_data_path, sample_interval=SRC_VIDEO_SAMPLE_INTERVAL)
elif src_data_type == "folder":
images_loader = lib_images_io.ReadFromFolder(folder_path=src_data_path)
elif src_data_type == "webcam":
if src_data_path == "":
webcam_idx = 0
elif src_data_path.isdigit():
webcam_idx = int(src_data_path)
else:
webcam_idx = src_data_path
images_loader = lib_images_io.ReadFromWebcam(SRC_WEBCAM_MAX_FPS,
webcam_idx)
return images_loader
return None
if id == 'min':
id = min(self.dict_id2clf.keys())
return self.dict_id2clf[id]
# -- Main
if __name__ == "__main__":
skeleton_detector = SkeletonDetector(OPENPOSE_MODEL, OPENPOSE_IMG_SIZE)
multiperson_tracker = Tracker()
multiperson_classifier = MultiPersonClassifier("/home/yitao/Documents/fun-project/tensorflow-related/Realtime-Action-Recognition/model/trained_classifier.pickle", ['stand', 'walk', 'run', 'jump', 'sit', 'squat', 'kick', 'punch', 'wave'])
# images_loader = select_images_loader(SRC_DATA_TYPE, SRC_DATA_PATH)
images_loader = lib_images_io.ReadFromVideo("/home/yitao/Documents/fun-project/tensorflow-related/Realtime-Action-Recognition/data_test/exercise.avi", sample_interval = 1)
frame_id = 1
while (frame_id < 10):
print("Processing %dth image" % frame_id)
img = images_loader.read_image()
humans = skeleton_detector.detect(img)
skeletons, scale_h = skeleton_detector.humans_to_skels_list(humans)
skeletons = remove_skeletons_with_few_joints(skeletons)
# print("[Yitao] len(skeletons) = %s" % len(skeletons))
# print(skeletons[0])
dict_id2skeleton = multiperson_tracker.track(skeletons)
def predict(request, time=None):
if time not in run_code.keys():
run_code[time] = True
if request.method == 'POST' and request.FILES:
upload = UploadForm(request.POST, request.FILES)
if upload.is_valid():
videoinput = request.FILES['file']
handle_uploaded_file(videoinput)
else:
upload = UploadForm()
return render(request, "index.html", {'form': upload})
# def get_folder_name(data_type):
# '''
# 根據data_type和data_path計算輸出文件夾名稱。
# 該腳本的最終輸出如下所示：
# DST_FOLDER/folder_name/video.avi
# DST_FOLDER/folder_name/skeletons/XXXXX.txt
# '''
# if data_type == "video": # /root/data/video.avi --> video
# folder_name = videoinput.name.split(".")[-2]
# return folder_name
DATA_TYPE = "video"
DATA_PATH = f"{CURR_PATH}/static/upload/{videoinput.name}"
MODEL_PATH = f"{ROOT}/model/dnn_model.h5"
# DST_FOLDER_NAME = get_folder_name(DATA_TYPE)
DST_FOLDER_NAME = videoinput.name.split(".")[-2]
output_folder = f"{ROOT}/output"
# ----設定
cfg_all = lib_commons.read_yaml(ROOT + "config/config.yaml")
cfg = cfg_all["s5_test.py"]
CLASSES = np.array(cfg_all["classes"])
# "{:05d}.txt"
SKELETON_FILENAME_FORMAT = cfg_all["skeleton_filename_format"]
# 動作識別：用於提取特徵的幀數。
WINDOW_SIZE = int(cfg_all["features"]["window_size"]) # 5
# Output folder
DST_FOLDER = output_folder + "/" + DST_FOLDER_NAME + "/"
DST_SKELETON_FOLDER_NAME = cfg["output"]["skeleton_folder_name"]
DST_VIDEO_NAME = cfg["output"]["video_name"]
# 輸出video.avi的幀率
DST_VIDEO_FPS = float(cfg["output"]["video_fps"])
# Video 設定
# 如果data_type為video，則設置採樣間隔。
# 例如，如果為3，則video的讀取速度將提高3倍。
VIDEO_SAMPLE_INTERVAL = int(
cfg["settings"]["source"]["video_sample_interval"])
# Openpose 設定
OPENPOSE_MODEL = cfg["settings"]["openpose"]["model"] # cmu
OPENPOSE_IMG_SIZE = cfg["settings"]["openpose"]["img_size"] # 656x368
# Display 設定
img_disp_desired_rows = int(
cfg["settings"]["display"]["desired_rows"]) # 480
# -- Detector, tracker, classifier
skeleton_detector = SkeletonDetector(OPENPOSE_MODEL, OPENPOSE_IMG_SIZE)
multiperson_tracker = Tracker()
multiperson_classifier = MultiPersonClassifier(MODEL_PATH, CLASSES,
WINDOW_SIZE)
# -- Image reader and displayer
images_loader = lib_images_io.ReadFromVideo(
DATA_PATH, sample_interval=VIDEO_SAMPLE_INTERVAL)
# 網頁上不顯示
# img_displayer = lib_images_io.ImageDisplayer()
# cv2.namedWindow('cv2_display_window', cv2.WINDOW_NORMAL)
# cv2.resizeWindow('cv2_display_window',570,460)
# cv2.moveWindow("cv2_display_window", 900, 275)
# -- Init output
# output folder
os.makedirs(DST_FOLDER, exist_ok=True)
os.makedirs(DST_FOLDER + DST_SKELETON_FOLDER_NAME, exist_ok=True)
# video writer
# video_writer = lib_images_io.VideoWriter(
# DST_FOLDER + DST_VIDEO_NAME, DST_VIDEO_FPS)
# -- Read images and process
try:
ith_img = -1
while images_loader.has_image() and run_code[time]:
# -- Read image
img = images_loader.read_image()
ith_img += 1
img_disp = img.copy()
print(f"\nProcessing {ith_img}th image ...")
# -- Detect skeletons
humans = skeleton_detector.detect(img)
skeletons, scale_h = skeleton_detector.humans_to_skels_list(
humans) # skeletons shape : (1, 36)
# -- Track people
# dict_id2skeleton => {id : skeletons}
dict_id2skeleton = multiperson_tracker.track(skeletons)
# -- Recognize action of each person
if len(dict_id2skeleton):
dict_id2label = multiperson_classifier.classify(
dict_id2skeleton)
# -- Draw
img_disp = draw_result_img(img_disp, ith_img, humans,
dict_id2skeleton, skeleton_detector,
multiperson_classifier,
img_disp_desired_rows, dict_id2label,
scale_h)
# 將BGR照片轉為RGB
img_rgb = cv2.cvtColor(img_disp, cv2.COLOR_BGR2RGB)
# -- Display image, and write to video.avi
# 網頁上不顯示
# img_displayer.display(img_disp, wait_key_ms=1)
global img_tmp_dict
img_tmp_dict[time] = img_rgb
# video_writer.write(img_disp)
# -- Get skeleton data and save to file
skels_to_save = get_the_skeleton_data_to_save_to_disk(
dict_id2skeleton, dict_id2label)
result = np.array(skels_to_save)
global list_result
list_result = {i + 1: result[i, 1] for i in range(result.shape[0])}
print(list_result)
# save the result to txt files for all frame
lib_commons.save_listlist(
DST_FOLDER + DST_SKELETON_FOLDER_NAME +
SKELETON_FILENAME_FORMAT.format(ith_img), skels_to_save)
# global dict_result
# global status_a
# global status_h
# global status_o
# for uid, action in list_result.items():
# if uid not in dict_result.keys():
# dict_result[uid] = []
# dict_result[uid].append(action)
# else:
# if len(dict_result[uid]) > 15:
# dict_result[uid].pop(0)
# dict_result[uid].append(action)
# else:
# dict_result[uid].append(action)
# count_kick = dict_result[uid].count("kick")
# count_punch = dict_result[uid].count("punch")
# count_sos = dict_result[uid].count("sos")
# count_opendoor = dict_result[uid].count("opendoor")
# if count_kick > 10 or count_punch > 10:
# status_a = True
# lineNotifyMessage(msg="發現疑似違規行為")
# dict_result[uid].clear()
# else :
# status_a = False
# if count_sos > 10:
# status_h = True
# lineNotifyMessage(msg="有人發出求救訊號")
# dict_result[uid].clear()
# else :
# status_h = False
# if count_opendoor > 10:
# status_o = True
# dict_result[uid].clear()
# lineNotifyMessage(msg="請檢查隨身物品是否攜帶")
# else :
# status_o = False
return render(request, "index.html", {'form': upload})
except Exception as e:
print(e)
finally:
# video_writer.stop()
print("Program ends")
list_result = None
