TABLE_DETECTION/inference.py

import copy
import random
from glob import glob

import cv2
from sklearn.model_selection import train_test_split

from image import gen
from metrics import precision, recall, f1
from table_line import model, focal_loss
import numpy as np
random.seed(42)



def run(is_test=1):
    # paths = glob('train/dataset-line/6/*.json')
    paths = glob('C:/Table_Label/clean_data/*.json')
    paths = random.sample(paths, 676)
    print("len(paths)", len(paths))

    model_path = "./models/e17-f10.93.h5"
    # model_path = "./models/table-line_old.h5"

    model.load_weights(model_path)

    data_loader = gen(paths, batchsize=1, linetype=2, is_test=True)
    i = 0
    data_len = len(paths)
    p_score_total = 0
    r_score_total = 0
    f1_score_total = 0
    for X, Y, img_np in data_loader:
        if i >= len(paths):
            break

        pred = model.predict(X)
        pred = pred.astype(np.float64)

        if is_test:
            if i >= 6000:
                cv2.namedWindow("1", 0)
                cv2.resizeWindow("1", 1000, 800)
                cv2.imshow("1", img_np)
                draw_pixel(pred[0], 0.2, is_test)

        p_score = round(precision(Y, pred).numpy(), 3)
        r_score = round(recall(Y, pred).numpy(), 3)
        f1_score = round(f1(Y, pred).numpy(), 3)
        print("precision", p_score, "recall", r_score, "f1", f1_score, "batch", i, data_len)
        p_score_total += p_score
        r_score_total += r_score
        f1_score_total += f1_score
        i += 1
    print("precision avg", p_score_total/data_len)
    print("recall avg", r_score_total/data_len)
    print("f1 avg", f1_score_total/data_len)


def draw_pixel(pred, prob=0.2, is_test=1):
    if not is_test:
        return
    else:
        import matplotlib.pyplot as plt
        _array = []
        for _h in range(len(pred)):
            _line = []
            for _w in range(len(pred[_h])):
                _prob = pred[_h][_w]
                if _prob[0] > prob:
                    _line.append((0, 0, 255))
                elif _prob[1] > prob:
                    _line.append((255, 0, 0))
                else:
                    _line.append((255, 255, 255))
            _array.append(_line)
        plt.axis('off')
        plt.imshow(np.array(_array))
        plt.show()
        return


if __name__ == "__main__":
    run()