def _get_datapoints(self, predictions, labels):
'''
Precision and recall found for different threshold values. For each value a binary output image is made.
The threshold indicate that for a pixel value above threshold value is considered a road pixel.
This generate different values for precision and recall and highlight the trade off between precision and recall.
'''
#Results in a slack of 3 pixels.
labels_with_slack = self._apply_buffer(labels, 3)
tests = np.arange(0.0001 , 0.995, 0.01)
datapoints = []
#ttt = 0
for threshold in tests:
binary_arr = util.create_threshold_image(predictions, threshold)
#for i in range(labels.shape[0]):
# if np.max(labels[i]) > 0 and ttt < 10:
# l = labels[i]
# l2 = labels_with_slack[i]
# blank_image = Image.new("L", (32, 16))
# im = aug.from_arr_to_label(l, 16)
# im2 = aug.from_arr_to_label(l2, 16)
# blank_image.paste(im, (0,0))
# blank_image.paste(im2, (16,0))
# blank_image.show()
# ttt += 1
precision = self._get_precision(labels_with_slack, binary_arr)
pred_with_slack = self._apply_buffer(binary_arr, 3)
recall = self._get_recall(labels, binary_arr, pred_with_slack)
datapoints.append({"precision": precision, "recall": recall, "threshold": threshold})
return datapoints
def _get_datapoints(self, predictions, labels):
'''
Precision and recall found for different threshold values. For each value a binary output image is made.
The threshold indicate that for a pixel value above threshold value is considered a road pixel.
This generate different values for precision and recall and highlight the trade off between precision and recall.
'''
#Results in a slack of 3 pixels.
labels_with_slack = self._apply_buffer(labels, 3)
tests = np.arange(0.0001, 0.995, 0.01)
datapoints = []
#ttt = 0
for threshold in tests:
binary_arr = util.create_threshold_image(predictions, threshold)
#for i in range(labels.shape[0]):
# if np.max(labels[i]) > 0 and ttt < 10:
# l = labels[i]
# l2 = labels_with_slack[i]
# blank_image = Image.new("L", (32, 16))
# im = aug.from_arr_to_label(l, 16)
# im2 = aug.from_arr_to_label(l2, 16)
# blank_image.paste(im, (0,0))
# blank_image.paste(im2, (16,0))
# blank_image.show()
# ttt += 1
precision = self._get_precision(labels_with_slack, binary_arr)
pred_with_slack = self._apply_buffer(binary_arr, 3)
recall = self._get_recall(labels, binary_arr, pred_with_slack)
datapoints.append({
"precision": precision,
"recall": recall,
"threshold": threshold
})
return datapoints
all_diff = []
pred_diff = []
nr_with_road = 0
nr_with_pred = 0
best_trade_off = tradeoff
nr_of_examples = data.shape[0]
for i in range(nr_of_examples):
if (i % 1000 == 0):
print("{}%".format(i / float(nr_of_examples) * 100))
data_sample = data[i]
label_sample = labels[i]
output = evaluate(np.array([data_sample]))
output = util.create_threshold_image(output, best_trade_off)
diff = np.sum(np.abs(output[0] - label_sample)) / (
dataset_params.output_dim * dataset_params.output_dim)
has_road = not (np.max(label_sample) == 0)
pred_has_road = not (np.max(output) == 0)
if pred_has_road:
nr_with_pred += 1
if not has_road:
pred_diff.append(diff)
if has_road:
nr_with_road += 1
road_diff.append(diff)
else:
#print(diff)
all_diff = []
pred_diff = []
nr_with_road = 0
nr_with_pred = 0
best_trade_off = tradeoff
nr_of_examples = data.shape[0]
for i in range(nr_of_examples):
if(i%1000 == 0):
print("{}%".format(i/float(nr_of_examples) * 100))
data_sample = data[i]
label_sample = labels[i]
output = evaluate(np.array([data_sample]))
output = util.create_threshold_image(output, best_trade_off)
diff = np.sum(np.abs(output[0] - label_sample))/(dataset_params.output_dim*dataset_params.output_dim)
has_road = not (np.max(label_sample) == 0)
pred_has_road = not (np.max(output) == 0)
if pred_has_road:
nr_with_pred +=1
if not has_road:
pred_diff.append(diff)
if has_road:
nr_with_road +=1
road_diff.append(diff)
else:
#print(diff)
#print(np.max(output[0]))
