def ShowKeypoints(keypoints, log_path, experiment_name, number_of_clusters,
dataset_name):
dataset = Database(dataset_name, number_of_clusters)
count = 0
image_names = list(keypoints.keys())
random.shuffle(image_names)
fig, subplots = plt.subplots(8, 8, figsize=(15, 15))
subplots = subplots.reshape(-1)
fig.subplots_adjust(wspace=0, hspace=0)
for s in subplots:
s.set_axis_off()
while count < 8 * 8:
imagepoints = keypoints[image_names[count]]
image = dataset.getimage_FAN(dataset, image_names[count])
ax = subplots[count]
ax.imshow(image)
ax.scatter(4 * imagepoints[:, 0], 4 * imagepoints[:, 1])
count += 1
filename = GetLogsPath(experiment_name, log_path) / 'Keypoints.jpg'
fig.savefig(filename)
LogText(f"Keypoint images created in {filename}", experiment_name,
log_path)
def ShowVisualRes(keypoints, log_path, experiment_name, number_of_clusters,
dataset_name):
fig = plt.figure(figsize=(34, 55))
gs1 = gridspec.GridSpec(13, 8)
gs1.update(wspace=0.0, hspace=0.0)
filenames = [
k for k in keypoints.keys() if keypoints[k]['is_it_test_sample']
]
filenames.sort()
filenames = filenames[:13 * 8]
dataset = Database(dataset_name, number_of_clusters, test=True)
for i in range(len(filenames)):
ax = plt.subplot(gs1[i])
plt.axis('off')
pointstoshow = keypoints[filenames[i]]['prediction']
image = dataset.getimage_FAN(dataset, filenames[i])
ax.imshow(image)
colors = [
Utils.colorlist[int(i)] for i in np.arange(len(pointstoshow))
]
ax.scatter(pointstoshow[:, 0],
pointstoshow[:, 1],
s=400,
c=colors,
marker='P',
edgecolors='black',
linewidths=0.3)
fig.show()
filename = GetLogsPath(experiment_name, log_path) / 'Step2.jpg'
fig.savefig(filename)
LogText(f"Step2 results created in {filename}", experiment_name, log_path)
def ShowClusters(keypoints, log_path, experiment_name, number_of_clusters,
dataset_name):
dataset = Database(dataset_name, number_of_clusters)
image_names = list(keypoints.keys())
random.shuffle(image_names)
for cluster_number in range(number_of_clusters):
counter_figureimages = 0
counter_datasetimages = 0
fig, subplots = plt.subplots(8, 8, figsize=(15, 15))
subplots = subplots.reshape(-1)
fig.subplots_adjust(wspace=0, hspace=0)
for s in subplots:
s.set_axis_off()
while counter_figureimages < 64:
#for the case where cluster has less than 64 instances
if (counter_datasetimages > len(keypoints) - 1):
filename = GetLogsPath(
experiment_name, log_path) / f'Cluster{cluster_number}.jpg'
fig.savefig(filename)
break
imagename = image_names[counter_datasetimages]
imagepoints = keypoints[imagename]
#if cluster exists in image
if (sum(imagepoints[:, 2] == cluster_number) > 0):
image = dataset.getimage_FAN(dataset, imagename)
ax = subplots[counter_figureimages]
ax.imshow(image)
ax.scatter(
4 * imagepoints[imagepoints[:, 2] == cluster_number, 0],
4 * imagepoints[imagepoints[:, 2] == cluster_number, 1])
counter_figureimages += 1
counter_datasetimages += 1
filename = GetLogsPath(experiment_name,
log_path) / f'Cluster{cluster_number}.jpg'
fig.savefig(filename)
LogText(f"Cluster images created in {filename}", experiment_name,
log_path)