def draw_iqa(img, q, target_a, pred_a):
fig, ax = tfplot.subplots(figsize=(6, 6))
ax.imshow(img)
ax.set_title(question2str(q))
ax.set_xlabel(
answer2str(target_a) + answer2str(pred_a, 'Predicted'))
return fig
def update3(self):
q_idx = self.comboBox.currentIndex()
c_idx = self.comboBox_2.currentIndex()
# find all qv, a, for selected object in this image
qv_list = []
a_list = []
img = None
for i in range(NUM_Q * NUM_SHAPE):
img, q, a = self.getitem(self.currentImgIdx + i)
qv_list.append(q)
a_list.append(a)
img = img
qv = np.array(qv_list)
a = np.array(a_list)
idx = np.where(qv[:, c_idx] == 1)[0]
if len(idx) <= 0:
error = QtWidgets.QErrorMessage(self.widget)
error.showMessage("Selected Object is not in the image!")
return
this_qv = qv[idx]
this_a = a[idx]
# select question and answer for this objects
cur_qv = this_qv[q_idx]
cur_a = this_a[q_idx]
img = img / 255.
# query for answer
pa, ta = self.RN.predict(img, cur_qv, cur_a)
predicted_answer = answer2str(pa)
true_answer = answer2str(ta)
QtWidgets.QMessageBox.information(
self.widget, "Prediction Result",
"Predicted Answer: %s | True Answer: %s" %
(predicted_answer, true_answer))
def draw_iqa(img, q, target_a, pred_a, weights):
d = self.d
H, W = img.shape[:2]
weights = weights.reshape(d*d, d*d)
weights_a2b = np.mean(weights, axis=1).reshape(4,4)
weights_b2a = np.mean(np.transpose(weights), axis=1).reshape(4,4)
mean_w = (weights_a2b + weights_b2a) / 2
mean_w = mean_w / np.max(mean_w)
# print(mean_w.shape, img.shape)
# print("===========")
fig, ax = tfplot.subplots(figsize=(6, 6))
ax.imshow(img, extent=[0,H,0,W])
mid = ax.imshow(mean_w, cmap='jet',
alpha=0.5, extent=[0, H, 0, W])
fig.colorbar(mid)
ax.set_title(question2str(q))
ax.set_xlabel(answer2str(target_a)+answer2str(pred_a, 'Predicted'))
return fig
