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import os
import cv2
import numpy as np
from moviepy.editor import ImageSequenceClip
def get_video_frames(video_path):
"""Reads a video frame by frame
Args:
video_path: A video path
Returns:
A list of numpy arrays representing video frames
"""
vid_cap = cv2.VideoCapture(video_path)
frames = []
while True:
ret, frame = vid_cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(frame)
else:
break
return frames
class Plot:
"""Class for plotting keypoints on video frames and creating a video
"""
KEYPOINT_EDGE_INDS_TO_COLOR = {
(0, 1): "m",
(0, 2): "c",
(1, 3): "m",
(2, 4): "c",
(0, 5): "m",
(0, 6): "c",
(5, 7): "m",
(7, 9): "m",
(6, 8): "c",
(8, 10): "c",
(5, 6): "y",
(5, 11): "m",
(6, 12): "c",
(11, 12): "y",
(11, 13): "m",
(13, 15): "m",
(12, 14): "c",
(14, 16): "c",
}
@staticmethod
def add_keypoints_to_image(
images_array, keypoints_list
):
"""
Adds keypoints to the image
Args:
images_array: list of images to represent the keypoints
keypoints_list : list of keypoints
model: name of the model used to detect the keypoints
Returns:
None
"""
output_overlay_array = images_array.astype(np.int32)
output_overlay_list = Plot.draw_prediction_on_image(
output_overlay_array, keypoints_list
)
return np.array(output_overlay_list)
@staticmethod
def draw_prediction_on_image(
image_list, keypoints_list
):
"""Draws the keypoint predictions on image.
Args:
image_list: A numpy array with shape [n, height, width, channel]
representing the
pixel values of the input image where n is number of images.
keypoints_list: A numpy array with shape [n, 17, 2] representing the
coordinates of 17 keypoints where n is number of images.
Returns:
A numpy array with shape [n, out_height, out_width, channel]
representing
the list of
images overlaid with keypoint predictions.
"""
height, width, channel = image_list[0].shape
keypoint_locs, keypoint_edges = Plot._keypoints_and_edges_for_display(
keypoints_list,
height, width
)
for img_i in range(keypoint_locs.shape[0]):
for edge in keypoint_edges[img_i]:
image = cv2.line(
image_list[img_i], (int(edge[0]), int(edge[1])),
(int(edge[2]), int(edge[3])), color=(0, 0, 255), thickness=3
)
for center_x, center_y in keypoint_locs[img_i]:
image = cv2.circle(
image_list[img_i], (int(center_x), int(center_y)), radius=5,
color=(255, 0, 0), thickness=-1
)
image_list[img_i] = image
return image_list
@staticmethod
def _keypoints_and_edges_for_display(
keypoints_list, height, width,
):
"""Returns high confidence keypoints and edges for visualization.
Args:
keypoints_list: A numpy array with shape [1, 1, 17, 3] representing
the keypoint coordinates and scores returned from the MoveNet model.
height: height of the image in pixels.
width: width of the image in pixels.
keypoint_threshold: minimum confidence score for keypoint to be
visualized.
Returns:
A (kpts_absolute_xy, edges_xy) containing:
* array with shape [n, 17, 2] representing the coordinates of all
keypoints of all detected entities in n images;
* array with shape [n, 18, 4] representing the coordinates of all
skeleton edges of all detected entities in n images;
"""
kpts_x = width * keypoints_list[:, :, 0]
kpts_y = height * keypoints_list[:, :, 1]
edge_pair = np.array(list(Plot.KEYPOINT_EDGE_INDS_TO_COLOR.keys()))
kpts_absolute_xy = np.stack(
[kpts_x, kpts_y], axis=-1
)
x_start = kpts_x[:, edge_pair[:, 0]]
y_start = kpts_y[:, edge_pair[:, 0]]
x_end = kpts_x[:, edge_pair[:, 1]]
y_end = kpts_y[:, edge_pair[:, 1]]
edges = np.stack([x_start, y_start, x_end, y_end], axis=2)
return kpts_absolute_xy, edges
@staticmethod
def resize_and_concat(ref_image_list, test_image_list):
"""Resizes either of reference frames list or test frames list to
make both list of equal shape and merges both frames side by side
Args:
ref_image_list: A list of numpy array representing reference video
frames
test_image_list: A list of numpy array representing test video frames
Returns:
concat_img_list: A list of numpy array representing merged video
frames
"""
def pad_image(image_list, pad_axis, pad_len, odd_len_diff):
"""pads given number of pixels to image_list on given axis
Args:
image_list: A list of numpy array representing video frames
pad_axis: A list of numpy array representing test video frames
pad_len: number of pixels to pad on either side of frame
odd_len_diff: 1 if difference between reference and test is odd
else 0
Returns:
padded video frame
"""
if pad_axis == 0:
return np.pad(
image_list, (
(0, 0), (pad_len, pad_len + odd_len_diff), (0, 0),
(0, 0)), 'constant',
constant_values=(0)
)
elif pad_axis == 1:
return np.pad(
image_list, (
(0, 0), (0, 0), (pad_len, pad_len + odd_len_diff),
(0, 0)), 'constant',
constant_values=(0)
)
ref_height, ref_width, _ = ref_image_list[0].shape
test_height, test_width, _ = test_image_list[0].shape
pad_height = abs(test_height - ref_height) // 2
odd_height_diff = (test_height - ref_height) % 2
if ref_height < test_height:
ref_image_list = pad_image(
ref_image_list, 0, pad_height, odd_height_diff
)
elif ref_height > test_height:
test_image_list = pad_image(
test_image_list, 0, pad_height, odd_height_diff
)
pad_width = abs(test_width - ref_width) // 2
odd_width_diff = (test_width - ref_width) % 2
if ref_width < test_width:
ref_image_list = pad_image(
ref_image_list, 1, pad_width, odd_width_diff
)
elif ref_width > test_width:
test_image_list = pad_image(
test_image_list, 1, pad_width, odd_width_diff
)
concat_img_list = np.concatenate(
(ref_image_list, test_image_list), axis=2
)
return concat_img_list
@staticmethod
def overlay_score_on_images(image_list, scores):
"""writes score on given image list
Args:
image_list: A list of numpy array representing video frames
scores: A list of score between reference and test keypoints
Returns:
A list of numpy array with score overlayed on it
"""
for i in range(len(image_list)):
image = image_list[i, :, :, :]
txt = f"Score : {scores[i]}"
image = cv2.putText(
image, txt, (5, 10), cv2.FONT_HERSHEY_SIMPLEX,
0.4, (255, 0, 0), 1, cv2.LINE_AA
)
image_list[i, :, :, :] = image
return image_list
def plot_matching(
ref_frames, test_frames, ref_keypoints, test_keypoints, ref_frames_idx,
test_frames_idx, costs, output_path
):
"""creates a video of reference and test video frames with keypoints
overlayed on them
Args:
ref_frames: A list of numpy array representing reference video frames
test_frames: A list of numpy array representing test video frames
ref_keypoints: A list of numpy array representing reference video
keypoints
test_keypoints: A list of numpy array representing reference video
keypoints
ref_frames_idx: A list of reference frame indices
test_frames_idx: A list of test frame indices
costs: A list of score between reference and test keypoints
output_path: path at which output video to be stored
"""
if (ref_frames_idx is not None) and (
test_frames_idx is not None) and len(ref_frames_idx) == len(
test_frames_idx
):
ref_frames = ref_frames[ref_frames_idx]
test_frames = test_frames[test_frames_idx]
ref_keypoints = ref_keypoints[ref_frames_idx]
test_keypoints = test_keypoints[test_frames_idx]
if costs is None:
costs = ['N/A'] * len(ref_frames)
display_line = [
f"Cost: {costs[i]} Ref frame: {ref_frames_idx[i]} Test " \
f"frame: {test_frames_idx[i]}"
for i in range(len(ref_frames_idx))]
ref_image_list = Plot.add_keypoints_to_image(
ref_frames, ref_keypoints
)
test_image_list = Plot.add_keypoints_to_image(
test_frames, test_keypoints
)
comparison_img_list = Plot.resize_and_concat(
ref_image_list, test_image_list
)
comparison_img_list = Plot.overlay_score_on_images(
comparison_img_list, display_line
)
video = ImageSequenceClip(list(comparison_img_list), fps=5)
video.write_videofile(output_path, fps=5)
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