def predict_subtract_diff(prob, anlg, tran, d):
best_diff_to_u1_x = d.get("diff_to_u1_x")
best_diff_to_u1_y = d.get("diff_to_u1_y")
best_diff_to_u2_x = d.get("diff_to_u2_x")
best_diff_to_u2_y = d.get("diff_to_u2_y")
best_diff = d.get("diff")
u3 = prob.matrix[anlg.get("value")[2]]
u1_ref = prob.matrix_ref[anlg.get("value")[0]]
prediction, best_diff_to_prediction_x, best_diff_to_prediction_y = transform.subtract_diff(
u3,
best_diff_to_u1_x,
best_diff_to_u1_y,
best_diff,
u1_ref,
coords=True)
pred_data = []
for ii, opt in enumerate(prob.options):
print(prob.name, anlg.get("name"), tran.get("name"), ii)
u3_score, diff_to_opt_x, diff_to_opt_y, diff_to_u3_x, diff_to_u3_y, diff = \
asymmetric_jaccard.asymmetric_jaccard_coef(opt, u3)
opt_to_u3_x = (-diff_to_opt_x) - (-diff_to_u3_x)
opt_to_u3_y = (-diff_to_opt_y) - (-diff_to_u3_y)
u2_to_u1_x = (-best_diff_to_u2_x) - (-best_diff_to_u1_x)
u2_to_u1_y = (-best_diff_to_u2_y) - (-best_diff_to_u1_y)
if abs(opt_to_u3_x - u2_to_u1_x) > 2 or abs(opt_to_u3_y -
u2_to_u1_y) > 2:
u3_score, diff = asymmetric_jaccard.asymmetric_jaccard_coef_pos_fixed(
opt, u3, u2_to_u1_x, u2_to_u1_y)
diff_score, _, _ = jaccard.jaccard_coef(diff, best_diff)
opt_score, _, _ = jaccard.jaccard_coef(prediction, opt)
# prediction_to_opt_x = -to_pred_x + to_opt_x
# prediction_to_opt_y = -to_pred_y + to_opt_y
#
# best_diff_to_opt_x = best_diff_to_prediction_x + prediction_to_opt_x
# best_diff_to_opt_y = best_diff_to_prediction_y + prediction_to_opt_y
# best_diff_aligned, opt_aligned, _, _ = utils.align(best_diff, opt, best_diff_to_opt_x, best_diff_to_opt_y)
# u3_restored = utils.trim_binary_image(np.logical_or(best_diff_aligned, opt_aligned))
# u3_restored_score , _, _, _, _, _ = asymmetric_jaccard.asymmetric_jaccard_coef(u3, u3_restored)
score = (diff_score + u3_score + opt_score) / 3
pred_data.append({
**d, "optn": ii + 1,
"optn_score": score,
"mato_score": (d.get("mat_score") + score) / 2,
"pred": prediction
})
return pred_data
def add_diff(img, diff_to_ref_x, diff_to_ref_y, diff, ref):
"""
:param img:
:param diff_to_ref_x:
:param diff_to_ref_y:
:param diff:
:param ref:
:return:
"""
if 0 == diff.sum():
return img
iimg = img.copy()
iimg_to_img_x = 0
iimg_to_img_y = 0
while True:
_, ref_to_iimg_x, ref_to_iimg_y = jaccard.jaccard_coef(ref, iimg)
diff_to_iimg_x = diff_to_ref_x + ref_to_iimg_x
diff_to_iimg_y = diff_to_ref_y + ref_to_iimg_y
score, _ = asymmetric_jaccard.asymmetric_jaccard_coef_pos_fixed(
diff, iimg, diff_to_iimg_x, diff_to_iimg_y)
if score > 0.9:
ref_aligned, iimg_aligned, aligned_to_iimg_x, aligned_to_iimg_y = utils.align(
ref, iimg, ref_to_iimg_x, ref_to_iimg_y)
iimg_aligned = utils.erase_noise_point(
np.logical_and(iimg_aligned, np.logical_not(ref_aligned)), 4)
iimg = iimg_aligned
iimg_to_img_x = aligned_to_iimg_x + iimg_to_img_x
iimg_to_img_y = aligned_to_iimg_y + iimg_to_img_y
if iimg.sum() == 0:
return img
else:
break
diff_to_img_x = diff_to_iimg_x + iimg_to_img_x
diff_to_img_y = diff_to_iimg_y + iimg_to_img_x
diff_aligned, img_aligned, _, _ = utils.align(diff, img, diff_to_img_x,
diff_to_img_y)
return utils.trim_binary_image(np.logical_or(img_aligned, diff_aligned))
def predict_preserving_subtract_diff(prob, anlg, tran, d):
best_diff_to_b2_x = d.get("diff_to_b2_x")
best_diff_to_b2_y = d.get("diff_to_b2_y")
best_diff_to_b3_x = d.get("diff_to_b3_x")
best_diff_to_b3_y = d.get("diff_to_b3_y")
best_diff = d.get("diff")
b4 = prob.matrix[anlg.get("value")[3]]
b5 = prob.matrix[anlg.get("value")[4]]
b2_ref = prob.matrix_ref[anlg.get("value")[0]]
prediction, best_diff_to_prediction_x, best_diff_to_prediction_y = transform.subtract_diff(
b5,
best_diff_to_b2_x,
best_diff_to_b2_y,
best_diff,
b2_ref,
coords=True)
pred_data = []
for ii, opt in enumerate(prob.options):
print(prob.name, anlg.get("name"), tran.get("name"), ii)
b5_score, diff_to_opt_x, diff_to_opt_y, diff_to_b5_x, diff_to_b5_y, diff = \
asymmetric_jaccard.asymmetric_jaccard_coef(opt, b5)
opt_to_b5_x = (-diff_to_opt_x) - (-diff_to_b5_x)
opt_to_b5_y = (-diff_to_opt_y) - (-diff_to_b5_y)
b3_to_b2_x = (-best_diff_to_b3_x) - (-best_diff_to_b2_x)
b3_to_b2_y = (-best_diff_to_b3_y) - (-best_diff_to_b2_y)
if abs(opt_to_b5_x - b3_to_b2_x) > 2 or abs(opt_to_b5_y -
b3_to_b2_y) > 2:
b5_score, diff = asymmetric_jaccard.asymmetric_jaccard_coef_pos_fixed(
opt, b5, b3_to_b2_x, b3_to_b2_y)
diff_score, _, _ = jaccard.jaccard_coef(diff, best_diff)
opt_score, _, _ = jaccard.jaccard_coef(prediction, opt)
b4_b5_aj = asymmetric_jaccard.asymmetric_jaccard_coef(b4, b5)
b4_opt_aj = asymmetric_jaccard.asymmetric_jaccard_coef(b4, opt)
preserving_score = min(b4_b5_aj[0], b4_opt_aj[0])
score = (diff_score + b5_score + diff_score + preserving_score) / 4
pred_data.append({
**d, "optn": ii + 1,
"optn_score": score,
"mato_score": (d.get("mat_score") + score) / 2,
"pred": prediction
})
return pred_data
def predict_add_diff(prob, anlg, tran, d):
best_diff_to_u1_x = d.get("diff_to_u1_x")
best_diff_to_u1_y = d.get("diff_to_u1_y")
best_diff_to_u2_x = d.get("diff_to_u2_x")
best_diff_to_u2_y = d.get("diff_to_u2_y")
best_diff = d.get("diff")
u3 = prob.matrix[anlg.get("value")[2]]
u1_ref = prob.matrix_ref[anlg.get("value")[0]]
prediction = transform.add_diff(u3, best_diff_to_u1_x, best_diff_to_u1_y,
best_diff, u1_ref)
pred_data = []
for ii, opt in enumerate(prob.options):
print(prob.name, anlg.get("name"), tran.get("name"), ii)
u3_score, diff_to_u3_x, diff_to_u3_y, diff_to_opt_x, diff_to_opt_y, diff = \
asymmetric_jaccard.asymmetric_jaccard_coef(u3, opt)
u3_to_opt_x = (-diff_to_u3_x) - (-diff_to_opt_x)
u3_to_opt_y = (-diff_to_u3_y) - (-diff_to_opt_y)
u1_to_u2_x = (-best_diff_to_u1_x) - (-best_diff_to_u2_x)
u1_to_u2_y = (-best_diff_to_u1_y) - (-best_diff_to_u2_y)
if abs(u3_to_opt_x - u1_to_u2_x) > 2 or abs(u3_to_opt_y -
u1_to_u2_y) > 2:
u3_score, diff = asymmetric_jaccard.asymmetric_jaccard_coef_pos_fixed(
u3, opt, u1_to_u2_x, u1_to_u2_y)
diff_score, _, _ = jaccard.jaccard_coef(diff, best_diff)
opt_score, _, _ = jaccard.jaccard_coef(opt, prediction)
score = (diff_score + opt_score + u3_score) / 3
pred_data.append({
**d, "optn": ii + 1,
"optn_score": score,
"mato_score": (d.get("mat_score") + score) / 2,
"pred": prediction
})
return pred_data
def predict(prob, d):
anlg = analogy.get_anlg(d.get("anlg_name"))
tran = transform.get_tran(d.get("tran_name"))
if 3 == len(anlg.get("value")):
best_diff_to_u1_x = d.get("diff_to_u1_x")
best_diff_to_u1_y = d.get("diff_to_u1_y")
best_diff_to_u2_x = d.get("diff_to_u2_x")
best_diff_to_u2_y = d.get("diff_to_u2_y")
best_diff = d.get("diff")
u3 = prob.matrix[anlg.get("value")[2]]
u1 = prob.matrix[anlg.get("value")[0]]
if tran.get("name") == "add_diff":
prediction = transform.add_diff(u3, best_diff_to_u1_x,
best_diff_to_u1_y, best_diff, u1)
elif tran.get("name") == "subtract_diff":
prediction = transform.subtract_diff(u3, best_diff_to_u1_x,
best_diff_to_u1_y, best_diff,
u1)
else:
prediction = transform.apply_unary_transformation(u3, tran)
elif 5 == len(anlg.get("value")):
best_b1_to_b2_x = d.get("b1_to_b2_x")
best_b1_to_b2_y = d.get("b1_to_b2_y")
b1 = prob.matrix[anlg.get("value")[0]]
b2 = prob.matrix[anlg.get("value")[1]]
b4 = prob.matrix[anlg.get("value")[3]]
b5 = prob.matrix[anlg.get("value")[4]]
_, b4_to_b1_x, b4_to_b1_y = jaccard.jaccard_coef(b4, b1)
_, b5_to_b2_x, b5_to_b2_y = jaccard.jaccard_coef(b5, b2)
b4_to_b5_x = b4_to_b1_x - (b5_to_b2_x - best_b1_to_b2_x)
b4_to_b5_y = b4_to_b1_y - (b5_to_b2_y - best_b1_to_b2_y)
prediction, _, _, _, _ = transform.apply_binary_transformation(
b4, b5, tran, b4_to_b5_x, b4_to_b5_y)
else:
raise Exception("Ryan!")
pred_data = []
for ii, opt in enumerate(prob.options):
print(prob.name, anlg.get("name"), tran.get("name"), ii)
if tran.get("name") == "add_diff":
u1_to_u2_x = (-best_diff_to_u1_x) - (-best_diff_to_u2_x)
u1_to_u2_y = (-best_diff_to_u1_y) - (-best_diff_to_u2_y)
u3_score, diff = asymmetric_jaccard.asymmetric_jaccard_coef_pos_fixed(
u3, opt, u1_to_u2_x, u1_to_u2_y)
diff_score, _, _ = jaccard.jaccard_coef(diff, best_diff)
opt_score, _, _ = jaccard.jaccard_coef(opt, prediction)
score = (diff_score + opt_score + u3_score) / 3
elif tran.get("name") == "subtract_diff":
u2_to_u1_x = (-best_diff_to_u2_x) - (-best_diff_to_u1_x)
u2_to_u1_y = (-best_diff_to_u2_y) - (-best_diff_to_u1_y)
u3_score, diff = asymmetric_jaccard.asymmetric_jaccard_coef_pos_fixed(
opt, u3, u2_to_u1_x, u2_to_u1_y)
diff_score, _, _ = jaccard.jaccard_coef(diff, best_diff)
opt_score, _, _ = jaccard.jaccard_coef(opt, prediction)
score = (diff_score + opt_score + u3_score) / 3
else:
score, _, _ = jaccard.jaccard_coef(opt, prediction)
pred_data.append({
**d, "optn": ii + 1,
"pato_score": score,
"pred": prediction
})
return pred_data