# digit-detection models and setting
detect_model = "model/digit-detector/detector_model.hdf5"
recognize_model = "model/digit-detector/recognize_model.hdf5"
mean_value_for_detector = 107.524
mean_value_for_recognizer = 112.833
model_input_shape = (32,32,1)
preproc_for_detector = preproc.GrayImgPreprocessor(mean_value_for_detector)
preproc_for_recognizer = preproc.GrayImgPreprocessor(mean_value_for_recognizer)
char_detector = cls.CnnClassifier(detect_model, preproc_for_detector, model_input_shape)
char_recognizer = cls.CnnClassifier(recognize_model, preproc_for_recognizer, model_input_shape)
digit_spotter = detector.DigitSpotter(char_detector, char_recognizer, rp.MserRegionProposer())
# open the csv file of player-face-detection and find out the cloths color for each teams
df = pd.read_csv('C:/Users/shing/Desktop/player_info.csv', names =('Team','Num','Name','R','G','B'))
RDB_med = df.groupby('Team').median()
R0_med, G0_med, B0_med = RDB_med.iloc[0,1:4]
R1_med, G1_med, B1_med = RDB_med.iloc[1,1:4]
def process (input_image, params, model_params):
#oriImg = cv2.imread(input_image) # B,G,R order
oriImg = input_image
multiplier = [x * model_params['boxsize'] / oriImg.shape[0] for x in params['scale_search']]
heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
preprocessor_for_detector = preproc.GrayImgPreprocessor(
mean_value_for_detector)
preprocessor_for_recognizer = preproc.GrayImgPreprocessor(
mean_value_for_recognizer)
detector = cls.CnnClassifier(detect_model, preprocessor_for_detector,
model_input_shape)
recognizer = cls.CnnClassifier(recognize_model,
preprocessor_for_recognizer,
model_input_shape)
proposer = rp.MserRegionProposer()
# 2. create detector
det = detect.DigitSpotter(detector, recognizer, proposer)
# 3. Evaluate average precision
evaluator = eval.Evaluator(det, annotator, rp.OverlapCalculator())
recall, precision, f1_score = evaluator.run(img_files)
# recall value : 0.1757938334100322, precision value : 0.18070009460737937, f1_score : 0.1782132027058549
# 4. Evaluate MSER
detector = cls.TrueBinaryClassifier(input_shape=model_input_shape)
preprocessor = preproc.NonePreprocessor()
# Todo : detector, recognizer 를 none type 으로
det = detect.DigitSpotter(detector, recognizer, proposer)
evaluator = eval.Evaluator(det, annotator, rp.OverlapCalculator())
recall, precision, f1_score = evaluator.run(img_files, do_nms=False)
# recall value : 0.7165209387942936, precision value : 0.015586989818902604, f1_score : 0.030510268067095156
