def load_model():
detector_model_h5 = "ocr_detector.h5"
detector_model = get_detector()
detector_model.load_weights(detector_model_h5)
recognizer_model_h5 = "ocr_recognizer.h5"
recognizer_model = get_recognizer()
recognizer_model.load_weights(recognizer_model_h5)
return detector_model, recognizer_model
def load_model():
detector_model_h5 = "/Users/wdavis4/__pycache__/lecture0/sudoku_solver/solver/ocr_detector.h5"
detector_model = get_detector()
detector_model.load_weights(detector_model_h5)
recognizer_model_h5 = "/Users/wdavis4/__pycache__/lecture0/sudoku_solver/solver/ocr_recognizer.h5"
recognizer_model = get_recognizer()
recognizer_model.load_weights(recognizer_model_h5)
return detector_model, recognizer_model
ax.plot(bx, by, "-b", linewidth=1)
ax.text(minc, minr, str(char["char"]), fontsize=24)
plt.savefig(plot_path, bbox_inches="tight", pad_inches=0)
grid = infer_rows_and_cols(all_chars)
if print_result:
for r in grid:
print(r)
return grid
if __name__ == "__main__":
detector_model_h5 = "ocr_detector.h5"
detector_model = get_detector()
detector_model.load_weights(detector_model_h5)
recognizer_model_h5 = "ocr_recognizer.h5"
recognizer_model = get_recognizer()
recognizer_model.load_weights(recognizer_model_h5)
img = cv2.imread("example6.png")
img_to_grid(img,
detector_model,
recognizer_model,
plot_path="plot.png",
print_result=False)