Datasets:
HenryAI
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KerasCodeExamples.txt

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[0, 0, 0],
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In this example, the keys we are interested in are:
img_path
joints
There are a total of 24 entries present inside joints. Each entry has 3 values:
x-coordinate
y-coordinate
visibility flag of the keypoints (1 indicates visibility and 0 indicates non-visibility)
As we can see joints contain multiple [0, 0, 0] entries which denote that those keypoints were not labeled. In this example, we will consider both non-visible as well as unlabeled keypoints in order to allow mini-batch learning.
# Load the metdata definition file and preview it.
keypoint_def = pd.read_csv(KEYPOINT_DEF)
keypoint_def.head()
# Extract the colours and labels.
colours = keypoint_def[\"Hex colour\"].values.tolist()
colours = [\"#\" + colour for colour in colours]
labels = keypoint_def[\"Name\"].values.tolist()
# Utility for reading an image and for getting its annotations.
def get_dog(name):
data = json_dict[name]
img_data = plt.imread(os.path.join(IMG_DIR, data[\"img_path\"]))
# If the image is RGBA convert it to RGB.
if img_data.shape[-1] == 4:
img_data = img_data.astype(np.uint8)
img_data = Image.fromarray(img_data)
img_data = np.array(img_data.convert(\"RGB\"))
data[\"img_data\"] = img_data
return data
Visualize data
Now, we write a utility function to visualize the images and their keypoints.
# Parts of this code come from here:
# https://github.com/benjiebob/StanfordExtra/blob/master/demo.ipynb
def visualize_keypoints(images, keypoints):
fig, axes = plt.subplots(nrows=len(images), ncols=2, figsize=(16, 12))
[ax.axis(\"off\") for ax in np.ravel(axes)]
for (ax_orig, ax_all), image, current_keypoint in zip(axes, images, keypoints):
ax_orig.imshow(image)
ax_all.imshow(image)
# If the keypoints were formed by `imgaug` then the coordinates need
# to be iterated differently.
if isinstance(current_keypoint, KeypointsOnImage):
for idx, kp in enumerate(current_keypoint.keypoints):
ax_all.scatter(
[kp.x], [kp.y], c=colours[idx], marker=\"x\", s=50, linewidths=5
)
else:
current_keypoint = np.array(current_keypoint)
# Since the last entry is the visibility flag, we discard it.
current_keypoint = current_keypoint[:, :2]
for idx, (x, y) in enumerate(current_keypoint):
ax_all.scatter([x], [y], c=colours[idx], marker=\"x\", s=50, linewidths=5)
plt.tight_layout(pad=2.0)
plt.show()
# Select four samples randomly for visualization.
samples = list(json_dict.keys())
num_samples = 4
selected_samples = np.random.choice(samples, num_samples, replace=False)
images, keypoints = [], []
for sample in selected_samples:
data = get_dog(sample)
image = data[\"img_data\"]
keypoint = data[\"joints\"]
images.append(image)
keypoints.append(keypoint)
visualize_keypoints(images, keypoints)
png
The plots show that we have images of non-uniform sizes, which is expected in most real-world scenarios. However, if we resize these images to have a uniform shape (for instance (224 x 224)) their ground-truth annotations will also be affected. The same applies if we apply any geometric transformation (horizontal flip,...
Prepare data generator
class KeyPointsDataset(keras.utils.Sequence):
def __init__(self, image_keys, aug, batch_size=BATCH_SIZE, train=True):
self.image_keys = image_keys
self.aug = aug
self.batch_size = batch_size
self.train = train
self.on_epoch_end()
def __len__(self):
return len(self.image_keys) // self.batch_size