def __init__(self, show_img=True):
self.pose_estimator = PoseEstimator()
self.object_detector = ObjectDetectionYolo()
self.object_tracker = ObjectTracker()
self.BBV = BBoxVisualizer()
self.KPV = KeyPointVisualizer()
self.IDV = IDVisualizer(with_bbox=False)
self.img = []
self.img_black = []
self.show_img = show_img
def __init__(self, path=config.video_path, show_img=True):
self.pose_estimator = PoseEstimator()
self.object_detector = ObjectDetectionYolo()
self.BBV = BBoxVisualizer()
self.IDV = IDVisualizer()
self.object_tracker = ObjectTracker()
self.video_path = path
self.cap = cv2.VideoCapture(self.video_path)
self.img = []
self.img_black = []
self.show_img = show_img
self.locator = Locator([1, 2])
def __init__(self, show_img=True):
self.object_detector = ObjectDetectionYolo(cfg=opt.yolo_cfg, weight=opt.yolo_weight)
self.object_tracker = ObjectTracker()
self.BBV = BBoxVisualizer()
self.IDV = IDVisualizer(with_bbox=False)
self.boxes = tensor([])
self.boxes_scores = tensor([])
self.img_black = np.array([])
self.frame = np.array([])
self.id2bbox = {}
self.show_img = show_img
self.CNN_model = CNNInference()
def __init__(self, video_path):
# self.BBV = BBoxVisualizer()
self.cap = cv2.VideoCapture(video_path)
self.height, self.width = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
with open("/".join(video_path.split("/")[:-1]) + "/" + video_path.split("/")[-1][:-4] + "_box.txt", "r") as bf:
self.box_txt = [line[:-1] for line in bf.readlines()]
with open("/".join(video_path.split("/")[:-1]) + "/" + video_path.split("/")[-1][:-4] + "_kps.txt", "r") as kf:
self.kps_txt = [line[:-1] for line in kf.readlines()]
with open("/".join(video_path.split("/")[:-1]) + "/" + video_path.split("/")[-1][:-4] +
"_kps_score.txt", "r") as ksf:
self.kps_score_txt = [line[:-1] for line in ksf.readlines()]
self.IDV = IDVisualizer(with_bbox=True)
self.KPV = KeyPointVisualizer()
def __init__(self, show_img=True):
self.object_detector = ObjectDetectionYolo(cfg=yolo_cfg, weight=yolo_weight)
self.object_tracker = ObjectTracker()
self.pose_estimator = PoseEstimator(pose_cfg=pose_cfg, pose_weight=pose_weight)
self.BBV = BBoxVisualizer()
self.KPV = KeyPointVisualizer()
self.IDV = IDVisualizer(with_bbox=False)
self.boxes = tensor([])
self.boxes_scores = tensor([])
self.img_black = np.array([])
self.frame = np.array([])
self.id2bbox = {}
self.kps = {}
self.kps_score = {}
self.show_img = show_img
def __init__(self, resize_size, show_img=True):
self.object_detector = ObjectDetectionYolo(cfg=config.yolo_cfg, weight=config.yolo_weight)
self.object_tracker = ObjectTracker()
self.pose_estimator = PoseEstimator(pose_cfg=config.pose_cfg, pose_weight=config.pose_weight)
self.BBV = BBoxVisualizer()
self.KPV = KeyPointVisualizer()
self.IDV = IDVisualizer()
self.boxes = tensor([])
self.boxes_scores = tensor([])
self.frame = np.array([])
self.id2bbox = {}
self.CNN_model = CNNInference()
self.kps = {}
self.kps_score = {}
self.show_img = show_img
self.resize_size = resize_size
def __init__(self, model_name, video_path, label_path):
model_name, video_path, label_path = model_name.replace(
"\\", "/"), video_path.replace("\\",
"/"), label_path.replace("\\", "/")
self.tester = self.__get_tester(model_name)
self.video_name = video_path.split("/")[-1]
self.cap = cv2.VideoCapture(video_path)
self.IDV = IDVisualizer(with_bbox=True)
self.KPV = KeyPointVisualizer()
self.height, self.width = int(self.cap.get(
cv2.CAP_PROP_FRAME_HEIGHT)), int(
self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
self.kps_dict = defaultdict(list)
self.KPSP = KPSProcessor(self.height, self.width)
self.label, self.test_id = self.__get_label(label_path)
self.coord = []
self.pred = defaultdict(str)
self.pred_dict = defaultdict(list)
self.res = defaultdict(bool)
self.label_dict = defaultdict(bool)
with open(
"/".join(video_path.split("/")[:-1]) + "_txt/" +
video_path.split("/")[-1][:-4] + "_box.txt", "r") as bf:
self.box_txt = [line[:-1] for line in bf.readlines()]
with open(
"/".join(video_path.split("/")[:-1]) + "_txt/" +
video_path.split("/")[-1][:-4] + "_kps.txt", "r") as kf:
self.kps_txt = [line[:-1] for line in kf.readlines()]
with open(
"/".join(video_path.split("/")[:-1]) + "_txt/" +
video_path.split("/")[-1][:-4] + "_kps_score.txt", "r") as ksf:
self.kps_score_txt = [line[:-1] for line in ksf.readlines()]
if write_video:
res_video = "/".join(
video_path.split("/")[:-1]) + "_" + model_name.split(
"/")[-1][:-4] + "/" + self.video_name
self.out = cv2.VideoWriter(res_video,
cv2.VideoWriter_fourcc(*'XVID'), 10,
store_size)
class VideoProcessor:
def __init__(self, video_path):
# self.BBV = BBoxVisualizer()
self.cap = cv2.VideoCapture(video_path)
self.height, self.width = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
with open("/".join(video_path.split("/")[:-1]) + "/" + video_path.split("/")[-1][:-4] + "_box.txt", "r") as bf:
self.box_txt = [line[:-1] for line in bf.readlines()]
with open("/".join(video_path.split("/")[:-1]) + "/" + video_path.split("/")[-1][:-4] + "_kps.txt", "r") as kf:
self.kps_txt = [line[:-1] for line in kf.readlines()]
with open("/".join(video_path.split("/")[:-1]) + "/" + video_path.split("/")[-1][:-4] +
"_kps_score.txt", "r") as ksf:
self.kps_score_txt = [line[:-1] for line in ksf.readlines()]
self.IDV = IDVisualizer(with_bbox=True)
self.KPV = KeyPointVisualizer()
def process_video(self):
cnt = 0
while True:
ret, frame = self.cap.read()
cnt += 1
if ret:
frame = cv2.resize(frame, frame_size)
id2bbox = str2boxdict(self.box_txt.pop(0))
id2kps = str2kpsdict(self.kps_txt.pop(0))
id2kpsScore = str2kpsScoredict(self.kps_score_txt.pop(0))
if id2bbox is not None:
frame = self.IDV.plot_bbox_id(id2bbox, frame)
if id2kps is not None:
kps_tensor, score_tensor = self.KPV.kpsdic2tensor(id2kps, id2kpsScore)
frame = self.KPV.vis_ske(frame, kps_tensor, score_tensor)
black_frm = self.KPV.vis_ske_black(frame, kps_tensor, score_tensor)
cv2.imshow("res", frame)
cv2.waitKey(100)
else:
self.cap.release()
cv2.destroyAllWindows()
break
class ImageProcessor(object):
def __init__(self, path=config.video_path, show_img=True):
self.pose_estimator = PoseEstimator()
self.object_detector = ObjectDetectionYolo()
self.BBV = BBoxVisualizer()
self.IDV = IDVisualizer()
self.object_tracker = ObjectTracker()
self.video_path = path
self.cap = cv2.VideoCapture(self.video_path)
self.img = []
self.img_black = []
self.show_img = show_img
self.locator = Locator([1, 2])
def process(self):
cnt = 0
while True:
ret, frame = self.cap.read()
if ret:
frame = cv2.resize(frame, config.frame_size)
with torch.no_grad():
inps, orig_img, boxes, scores, pt1, pt2 = self.object_detector.process(
frame)
if boxes is not None:
# cv2.imshow("bbox", self.BBV.visualize(boxes, copy.deepcopy(frame)))
key_points, self.img, self.img_black = self.pose_estimator.process_img(
inps, orig_img, boxes, scores, pt1, pt2)
if len(key_points) > 0:
id2ske_all, id2bbox_all = self.object_tracker.track(
boxes, key_points)
id2ske, id2bbox = self.locator.locate_user(
id2ske_all, id2bbox_all)
# process skeleton
if self.show_img:
cv2.imshow(
"id_bbox_all",
self.IDV.plot_bbox_id(
id2bbox_all, copy.deepcopy(frame)))
cv2.imshow(
"id_ske_all",
self.IDV.plot_skeleton_id(
id2ske_all, copy.deepcopy(frame)))
cv2.imshow(
"id_bbox_located",
self.IDV.plot_bbox_id(
id2bbox, copy.deepcopy(frame)))
cv2.imshow(
"id_ske_located",
self.IDV.plot_skeleton_id(
id2ske, copy.deepcopy(frame)))
self.__show_img()
else:
if self.show_img:
self.__show_img()
else:
# cv2.imshow("bbox", frame)
# cv2.imshow("id", frame)
self.img, self.img_black = frame, frame
if self.show_img:
self.__show_img()
cnt += 1
print(cnt)
else:
self.cap.release()
cv2.destroyAllWindows()
break
def __show_img(self):
cv2.imshow("result", self.img)
cv2.moveWindow("result", 1200, 90)
cv2.imshow("result_black", self.img_black)
cv2.moveWindow("result_black", 1200, 540)
cv2.waitKey(1)
def process_single_person(self, frame):
frame = cv2.resize(frame, config.frame_size)
with torch.no_grad():
inps, orig_img, boxes, scores, pt1, pt2 = self.object_detector.process(
frame)
key_points, img, img_black = self.pose_estimator.process_img(
inps, orig_img, boxes, scores, pt1, pt2)
return key_points[0], img, img_black
def process_multiple_person(self, frame):
frame = cv2.resize(frame, config.frame_size)
with torch.no_grad():
inps, orig_img, boxes, scores, pt1, pt2 = self.object_detector.process(
frame)
key_points, img, img_black = self.pose_estimator.process_img(
inps, orig_img, boxes, scores, pt1, pt2)
if len(key_points) > 0:
id2ske_all, id2bbox_all = self.object_tracker.track(
boxes, key_points)
id2ske, id2bbox = self.locator.locate_user(
id2ske_all, id2bbox_all)
return id2ske, img, img_black
class ImgProcessor:
def __init__(self, show_img=True):
self.pose_estimator = PoseEstimator()
self.object_detector = ObjectDetectionYolo()
self.object_tracker = ObjectTracker()
self.BBV = BBoxVisualizer()
self.KPV = KeyPointVisualizer()
self.IDV = IDVisualizer(with_bbox=False)
self.img = []
self.img_black = []
self.show_img = show_img
def init_sort(self):
self.object_tracker.init_tracker()
def __process_kp(self, kps, idx):
new_kp = []
for bdp in range(len(kps)):
for coord in range(2):
new_kp.append(kps[bdp][coord])
return {idx: new_kp}
def process_img(self, frame):
# frame = cv2.resize(frame, config.frame_size)
img = copy.deepcopy(frame)
img_black = cv2.imread('video/black.jpg')
with torch.no_grad():
inps, orig_img, boxes, scores, pt1, pt2 = self.object_detector.process(
frame)
if boxes is not None:
key_points, img, black_img = self.pose_estimator.process_img(
inps, orig_img, boxes, scores, pt1, pt2)
if config.plot_bbox:
img = self.BBV.visualize(boxes, frame)
if key_points is not []:
id2ske, id2bbox = self.object_tracker.track(
boxes, key_points)
if config.plot_id:
img = self.IDV.plot_bbox_id(id2bbox,
copy.deepcopy(img))
# img = self.IDV.plot_skeleton_id(id2ske, copy.deepcopy(img))
if config.track_idx != "all":
try:
kps = self.__process_kp(id2ske[config.track_idx],
config.track_idx)
except KeyError:
kps = {}
else:
kps = id2ske
#
# if config.plot_kps:
# vis_kps = self.KPV.dict2ls(kps)
# img = self.KPV.vis_ske(orig_img, vis_kps, kp_score)
# img_black = self.KPV.vis_ske_black(orig_img, vis_kps, kp_score)
return kps, img, img_black
else:
return {}, img, img_black
else:
return {}, frame, frame
class HumanDetection:
def __init__(self, show_img=True):
self.object_detector = ObjectDetectionYolo(cfg=opt.yolo_cfg, weight=opt.yolo_weight)
self.object_tracker = ObjectTracker()
self.BBV = BBoxVisualizer()
self.IDV = IDVisualizer(with_bbox=False)
self.boxes = tensor([])
self.boxes_scores = tensor([])
self.img_black = np.array([])
self.frame = np.array([])
self.id2bbox = {}
self.show_img = show_img
self.CNN_model = CNNInference()
def init_sort(self):
self.object_tracker.init_tracker()
def clear_res(self):
self.boxes = tensor([])
self.boxes_scores = tensor([])
self.img_black = np.array([])
self.frame = np.array([])
self.id2bbox = {}
def visualize(self):
self.img_black = cv2.imread('video/black.jpg')
if config.plot_bbox and self.boxes is not None:
self.frame = self.BBV.visualize(self.boxes, self.frame, self.boxes_scores)
# cv2.imshow("cropped", (torch_to_im(inps[0]) * 255))
if config.plot_id and self.id2bbox is not None:
self.frame = self.IDV.plot_bbox_id(self.id2bbox, self.frame)
# frame = self.IDV.plot_skeleton_id(id2ske, copy.deepcopy(img))
return self.frame, self.img_black
def process_img(self, frame, gray=False):
self.clear_res()
self.frame = frame
with torch.no_grad():
if gray:
gray_img = gray3D(copy.deepcopy(frame))
box_res = self.object_detector.process(gray_img)
else:
box_res = self.object_detector.process(frame)
self.boxes, self.boxes_scores = self.object_detector.cut_box_score(box_res)
if box_res is not None:
self.id2bbox = self.object_tracker.track(box_res)
return self.id2bbox
def classify_whole(self):
out = self.CNN_model.predict(self.img_black)
idx = out[0].tolist().index(max(out[0].tolist()))
pred = opt.CNN_class[idx]
print("The prediction is {}".format(pred))
def classify(self, frame):
for box in self.id2bbox.values():
x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
x1 = 0 if x1 < 0 else x1
y1 = 0 if y1 < 0 else y1
x2 = frame.shape[1] if x2 > frame.shape[1] else x2
y2 = frame.shape[0] if y2 > frame.shape[0] else y2
img = np.asarray(frame[y1:y2, x1:x2])
# cv2.imshow("cut", img)
# cv2.imwrite("img/tmp/0.jpg", img)
out = self.CNN_model.predict(img)
idx = out[0].tolist().index(max(out[0].tolist()))
pred = opt.CNN_class[idx]
print(pred)
text_location = (int((box[0]+box[2])/2)), int((box[1])+50)
cv2.putText(frame, pred, text_location, cv2.FONT_HERSHEY_SIMPLEX, 1, (100, 100, 255), 2)
class HumanDetection:
def __init__(self, resize_size, show_img=True):
self.object_detector = ObjectDetectionYolo(cfg=config.yolo_cfg, weight=config.yolo_weight)
self.object_tracker = ObjectTracker()
self.pose_estimator = PoseEstimator(pose_cfg=config.pose_cfg, pose_weight=config.pose_weight)
self.BBV = BBoxVisualizer()
self.KPV = KeyPointVisualizer()
self.IDV = IDVisualizer()
self.boxes = tensor([])
self.boxes_scores = tensor([])
self.frame = np.array([])
self.id2bbox = {}
self.CNN_model = CNNInference()
self.kps = {}
self.kps_score = {}
self.show_img = show_img
self.resize_size = resize_size
def init(self):
self.object_tracker.init_tracker()
def clear_res(self):
self.boxes = tensor([])
self.boxes_scores = tensor([])
self.frame = np.array([])
self.id2bbox = {}
self.kps = {}
self.kps_score = {}
def visualize(self):
img_black = np.full((self.resize_size[1], self.resize_size[0], 3), 0).astype(np.uint8)
if config.plot_bbox and self.boxes is not None:
self.BBV.visualize(self.boxes, self.frame)
if config.plot_kps and self.kps is not []:
self.KPV.vis_ske(self.frame, self.kps, self.kps_score)
self.KPV.vis_ske_black(img_black, self.kps, self.kps_score)
if config.plot_id and self.id2bbox is not None:
self.IDV.plot_bbox_id(self.id2bbox, self.frame)
self.IDV.plot_skeleton_id(self.kps, self.frame)
return self.frame, img_black
def process_img(self, frame, gray=False):
self.clear_res()
self.frame = frame
with torch.no_grad():
if gray:
gray_img = gray3D(copy.deepcopy(frame))
box_res = self.object_detector.process(gray_img)
else:
box_res = self.object_detector.process(frame)
self.boxes, self.boxes_scores = self.object_detector.cut_box_score(box_res)
if box_res is not None:
self.id2bbox = self.object_tracker.track(box_res)
self.id2bbox = eliminate_nan(self.id2bbox)
boxes = self.object_tracker.id_and_box(self.id2bbox)
inps, pt1, pt2 = crop_bbox(frame, boxes)
if inps is not None:
kps, kps_score, kps_id = self.pose_estimator.process_img(inps, boxes, pt1, pt2)
self.kps, self.kps_score = self.object_tracker.match_kps(kps_id, kps, kps_score)
return self.kps, self.id2bbox, self.kps_score
def classify_whole(self, pred_img, show_img):
pred = self.CNN_model.classify_whole(pred_img, show_img)
return pred
def classify(self, pred_img, show_img, id2bbox):
preds = self.CNN_model.classify(pred_img, id2bbox)
self.CNN_model.visualize(show_img, preds)
return preds
class HumanDetection:
def __init__(self, show_img=True):
self.object_detector = ObjectDetectionYolo(cfg=yolo_cfg, weight=yolo_weight)
self.object_tracker = ObjectTracker()
self.pose_estimator = PoseEstimator(pose_cfg=pose_cfg, pose_weight=pose_weight)
self.BBV = BBoxVisualizer()
self.KPV = KeyPointVisualizer()
self.IDV = IDVisualizer(with_bbox=False)
self.boxes = tensor([])
self.boxes_scores = tensor([])
self.img_black = np.array([])
self.frame = np.array([])
self.id2bbox = {}
self.kps = {}
self.kps_score = {}
self.show_img = show_img
def init_sort(self):
self.object_tracker.init_tracker()
def clear_res(self):
self.boxes = tensor([])
self.boxes_scores = tensor([])
self.frame = np.array([])
self.id2bbox = {}
self.kps = {}
self.kps_score = {}
def visualize(self):
img_black = cv2.imread('video/black.jpg')
if config.plot_bbox and self.boxes is not None:
self.frame = self.BBV.visualize(self.boxes, self.frame, self.boxes_scores)
# cv2.imshow("cropped", (torch_to_im(inps[0]) * 255))
if config.plot_kps and self.kps is not []:
self.frame = self.KPV.vis_ske(self.frame, self.kps, self.kps_score)
img_black = self.KPV.vis_ske_black(self.frame, self.kps, self.kps_score)
if config.plot_id and self.id2bbox is not None:
self.frame = self.IDV.plot_bbox_id(self.id2bbox, self.frame)
# frame = self.IDV.plot_skeleton_id(id2ske, copy.deepcopy(img))
return self.frame, img_black
def process_img(self, frame, gray=False):
self.clear_res()
self.frame = frame
with torch.no_grad():
if gray:
gray_img = gray3D(copy.deepcopy(frame))
box_res = self.object_detector.process(gray_img)
else:
box_res = self.object_detector.process(frame)
self.boxes, self.boxes_scores = self.object_detector.cut_box_score(box_res)
if box_res is not None:
self.id2bbox = self.object_tracker.track(box_res)
boxes = self.object_tracker.id_and_box(self.id2bbox)
inps, pt1, pt2 = crop_bbox(frame, boxes)
kps, kps_score, kps_id = self.pose_estimator.process_img(inps, boxes, pt1, pt2)
self.kps, self.kps_score = self.object_tracker.match_kps(kps_id, kps, kps_score)
return self.kps, self.id2bbox, self.kps_score
class Tester:
def __init__(self, model_name, video_path, label_path):
model_name, video_path, label_path = model_name.replace(
"\\", "/"), video_path.replace("\\",
"/"), label_path.replace("\\", "/")
self.tester = self.__get_tester(model_name)
self.video_name = video_path.split("/")[-1]
self.cap = cv2.VideoCapture(video_path)
self.IDV = IDVisualizer(with_bbox=True)
self.KPV = KeyPointVisualizer()
self.height, self.width = int(self.cap.get(
cv2.CAP_PROP_FRAME_HEIGHT)), int(
self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
self.kps_dict = defaultdict(list)
self.KPSP = KPSProcessor(self.height, self.width)
self.label, self.test_id = self.__get_label(label_path)
self.coord = []
self.pred = defaultdict(str)
self.pred_dict = defaultdict(list)
self.res = defaultdict(bool)
self.label_dict = defaultdict(bool)
with open(
"/".join(video_path.split("/")[:-1]) + "_txt/" +
video_path.split("/")[-1][:-4] + "_box.txt", "r") as bf:
self.box_txt = [line[:-1] for line in bf.readlines()]
with open(
"/".join(video_path.split("/")[:-1]) + "_txt/" +
video_path.split("/")[-1][:-4] + "_kps.txt", "r") as kf:
self.kps_txt = [line[:-1] for line in kf.readlines()]
with open(
"/".join(video_path.split("/")[:-1]) + "_txt/" +
video_path.split("/")[-1][:-4] + "_kps_score.txt", "r") as ksf:
self.kps_score_txt = [line[:-1] for line in ksf.readlines()]
if write_video:
res_video = "/".join(
video_path.split("/")[:-1]) + "_" + model_name.split(
"/")[-1][:-4] + "/" + self.video_name
self.out = cv2.VideoWriter(res_video,
cv2.VideoWriter_fourcc(*'XVID'), 10,
store_size)
def __get_label(self, path):
with open(path, "r") as lf:
labels, ids = defaultdict(list), []
for line in lf.readlines():
[idx, label] = line[:-1].split(":")
labels[idx] = [l for l in label.split(" ")]
ids.append(idx)
return labels, ids
def __get_tester(self, model):
if "ConvLSTM" in model:
return ConvLSTMPredictor(model, len(cls))
if "BiLSTM" in model:
return BiLSTMPredictor(model, len(cls))
if "ConvGRU" in model:
return ConvGRUPredictor(model, len(cls))
if 'LSTM' in model:
if lstm:
return LSTMPredictor(model)
else:
print("lstm is not usable")
if "TCN" in model:
return TCNPredictor(model, len(cls))
def __detect_kps(self):
refresh_idx = []
for k, v in self.kps_dict.items():
if len(v) == seq_length:
pred = self.tester.predict(np.array(v).astype(np.float32))
self.pred[k] = cls[pred]
self.pred_dict[str(k)].append(cls[pred])
# print("Predicting id {}".format(k))
refresh_idx.append(k)
for idx in refresh_idx:
self.kps_dict[idx] = []
def __put_pred(self, img):
for idx, (k, v) in enumerate(self.pred.items()):
cv2.putText(img, "id{}: {}".format(k,
v), (30, int(40 * (idx + 1))),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
return img
def __put_cnt(self, img):
for idx, (k, v) in enumerate(self.kps_dict.items()):
cv2.putText(img, "id{} cnt: {}".format(k, len(v)),
(300, int(30 * (idx + 2))), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 255, 0), 2)
return img
def __get_target_pred(self):
return {
key: value
for key, value in self.pred_dict.items() if key in self.test_id
}
def __compare(self):
self.pred_dict = self.__get_target_pred()
assert self.pred_dict.keys() == self.label.keys()
for k in self.pred_dict.keys():
label, pred = self.label[k], self.pred_dict[k]
assert len(label) == len(pred)
for idx, (l, p) in enumerate(zip(label, pred)):
if l != "pass":
sample_str = self.video_name[:
-4] + "_id{}_frame{}-{}".format(
k, 30 * idx, 30 *
(idx + 1) - 1)
self.res[sample_str] = l == p
self.label_dict[sample_str] = l
def test(self):
cnt = 0
while True:
cnt += 1
ret, frame = self.cap.read()
if ret:
frame = cv2.resize(frame, config.size)
id2bbox = str2boxdict(self.box_txt.pop(0))
id2kps = str2kpsdict(self.kps_txt.pop(0))
id2kpsScore = str2kpsScoredict(self.kps_score_txt.pop(0))
if id2bbox is not None:
frame = self.IDV.plot_bbox_id(id2bbox, frame)
if id2kps is not None:
kps_tensor, score_tensor = self.KPV.kpsdic2tensor(
id2kps, id2kpsScore)
frame = self.KPV.vis_ske(frame, kps_tensor, score_tensor)
if id2kps:
for key, v in id2kps.items():
# coord = self.__normalize_coordinates(kps[key])
coord = self.KPSP.process_kp(v)
self.kps_dict[key].append(coord)
self.__detect_kps()
img = frame
img = cv2.resize(img, store_size)
img = self.__put_pred(img)
cv2.putText(img, "Frame cnt: {}".format(cnt), (300, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1)
img = self.__put_cnt(img)
cv2.imshow("res", img)
cv2.waitKey(2)
if write_video:
self.out.write(img)
else:
self.cap.release()
# IP.init_sort()
self.out.release()
cv2.destroyAllWindows()
break
self.__compare()
return self.res