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import cv2
import matplotlib.pyplot as plt
import numpy as np
import torchvision
import ffmpeg
__all__ = ["joints_dict", "draw_points_and_skeleton"]
def joints_dict():
joints = {
"coco_25": {
"keypoints": {
0: "nose",
1: "left_eye",
2: "right_eye",
3: "left_ear",
4: "right_ear",
5: "neck",
6: "left_shoulder",
7: "right_shoulder",
8: "left_elbow",
9: "right_elbow",
10: "left_wrist",
11: "right_wrist",
12: "left_hip",
13: "right_hip",
14: "hip",
15: "left_knee",
16: "right_knee",
17: "left_ankle",
18: "right_ankle",
19: "left_big toe",
20: "left_small_toe",
21: "left_heel",
22: "right_big_toe",
23: "right_small_toe",
24: "right_heel",
},
"skeleton": [
[17, 15], [15, 12], [18, 16], [16, 13], [12, 14], [13, 14], [5, 14],
[6, 5], [7, 5], [6, 8], [7, 9], [8, 10], [9, 11], [1, 2], [0, 1], [0, 2],
[1, 3], [2, 4], [17, 21], [18, 24], [19, 20], [22, 23], [19, 21],
[22, 24], [5, 0]
]
},
"coco": {
"keypoints": {
0: "nose",
1: "left_eye",
2: "right_eye",
3: "left_ear",
4: "right_ear",
5: "left_shoulder",
6: "right_shoulder",
7: "left_elbow",
8: "right_elbow",
9: "left_wrist",
10: "right_wrist",
11: "left_hip",
12: "right_hip",
13: "left_knee",
14: "right_knee",
15: "left_ankle",
16: "right_ankle"
},
"skeleton": [
[15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12],
[5, 6], [5, 7], [6, 8], [7, 9], [8, 10], [1, 2], [0, 1],
[0, 2], [1, 3], [2, 4], [0, 5], [0, 6]
]
},
"mpii": {
"keypoints": {
0: "right_ankle",
1: "right_knee",
2: "right_hip",
3: "left_hip",
4: "left_knee",
5: "left_ankle",
6: "pelvis",
7: "thorax",
8: "upper_neck",
9: "head top",
10: "right_wrist",
11: "right_elbow",
12: "right_shoulder",
13: "left_shoulder",
14: "left_elbow",
15: "left_wrist"
},
"skeleton": [
[5, 4], [4, 3], [0, 1], [1, 2], [3, 2], [3, 6], [2, 6], [6, 7],
[7, 8], [8, 9], [13, 7], [12, 7], [13, 14], [12, 11], [14, 15],
[11, 10],
]
},
'ap10k': {
'keypoints': {
0: 'L_Eye',
1: 'R_Eye',
2: 'Nose',
3: 'Neck',
4: 'Root of tail',
5: 'L_Shoulder',
6: 'L_Elbow',
7: 'L_F_Paw',
8: 'R_Shoulder',
9: 'R_Elbow',
10: 'R_F_Paw',
11: 'L_Hip',
12: 'L_Knee',
13: 'L_B_Paw',
14: 'R_Hip',
15: 'R_Knee',
16: 'R_B_Paw'
},
'skeleton': [
[0, 1], [0, 2], [1, 2], [2, 3], [3, 4], [3, 5], [5, 6], [6, 7],
[3, 8], [8, 9], [9, 10], [4, 11], [11, 12], [12, 13], [4, 14],
[14, 15], [15, 16]
]
},
'apt36k': {
'keypoints': {
0: 'L_Eye',
1: 'R_Eye',
2: 'Nose',
3: 'Neck',
4: 'Root of tail',
5: 'L_Shoulder',
6: 'L_Elbow',
7: 'L_F_Paw',
8: 'R_Shoulder',
9: 'R_Elbow',
10: 'R_F_Paw',
11: 'L_Hip',
12: 'L_Knee',
13: 'L_B_Paw',
14: 'R_Hip',
15: 'R_Knee',
16: 'R_B_Paw'
},
'skeleton': [
[0, 1], [0, 2], [1, 2], [2, 3], [3, 4], [3, 5], [5, 6], [6, 7],
[3, 8], [8, 9], [9, 10], [4, 11], [11, 12], [12, 13], [4, 14],
[14, 15], [15, 16]
]
},
'aic': {
'keypoints': {
0: 'right_shoulder',
1: 'right_elbow',
2: 'right_wrist',
3: 'left_shoulder',
4: 'left_elbow',
5: 'left_wrist',
6: 'right_hip',
7: 'right_knee',
8: 'right_ankle',
9: 'left_hip',
10: 'left_knee',
11: 'left_ankle',
12: 'head_top',
13: 'neck'
},
'skeleton': [
[2, 1], [1, 0], [0, 13], [13, 3], [3, 4], [4, 5], [8, 7],
[7, 6], [6, 9], [9, 10], [10, 11], [12, 13], [0, 6], [3, 9]
]
},
'wholebody': {
'keypoints': {
0: 'nose',
1: 'left_eye',
2: 'right_eye',
3: 'left_ear',
4: 'right_ear',
5: 'left_shoulder',
6: 'right_shoulder',
7: 'left_elbow',
8: 'right_elbow',
9: 'left_wrist',
10: 'right_wrist',
11: 'left_hip',
12: 'right_hip',
13: 'left_knee',
14: 'right_knee',
15: 'left_ankle',
16: 'right_ankle',
17: 'left_big_toe',
18: 'left_small_toe',
19: 'left_heel',
20: 'right_big_toe',
21: 'right_small_toe',
22: 'right_heel',
23: 'face-0',
24: 'face-1',
25: 'face-2',
26: 'face-3',
27: 'face-4',
28: 'face-5',
29: 'face-6',
30: 'face-7',
31: 'face-8',
32: 'face-9',
33: 'face-10',
34: 'face-11',
35: 'face-12',
36: 'face-13',
37: 'face-14',
38: 'face-15',
39: 'face-16',
40: 'face-17',
41: 'face-18',
42: 'face-19',
43: 'face-20',
44: 'face-21',
45: 'face-22',
46: 'face-23',
47: 'face-24',
48: 'face-25',
49: 'face-26',
50: 'face-27',
51: 'face-28',
52: 'face-29',
53: 'face-30',
54: 'face-31',
55: 'face-32',
56: 'face-33',
57: 'face-34',
58: 'face-35',
59: 'face-36',
60: 'face-37',
61: 'face-38',
62: 'face-39',
63: 'face-40',
64: 'face-41',
65: 'face-42',
66: 'face-43',
67: 'face-44',
68: 'face-45',
69: 'face-46',
70: 'face-47',
71: 'face-48',
72: 'face-49',
73: 'face-50',
74: 'face-51',
75: 'face-52',
76: 'face-53',
77: 'face-54',
78: 'face-55',
79: 'face-56',
80: 'face-57',
81: 'face-58',
82: 'face-59',
83: 'face-60',
84: 'face-61',
85: 'face-62',
86: 'face-63',
87: 'face-64',
88: 'face-65',
89: 'face-66',
90: 'face-67',
91: 'left_hand_root',
92: 'left_thumb1',
93: 'left_thumb2',
94: 'left_thumb3',
95: 'left_thumb4',
96: 'left_forefinger1',
97: 'left_forefinger2',
98: 'left_forefinger3',
99: 'left_forefinger4',
100: 'left_middle_finger1',
101: 'left_middle_finger2',
102: 'left_middle_finger3',
103: 'left_middle_finger4',
104: 'left_ring_finger1',
105: 'left_ring_finger2',
106: 'left_ring_finger3',
107: 'left_ring_finger4',
108: 'left_pinky_finger1',
109: 'left_pinky_finger2',
110: 'left_pinky_finger3',
111: 'left_pinky_finger4',
112: 'right_hand_root',
113: 'right_thumb1',
114: 'right_thumb2',
115: 'right_thumb3',
116: 'right_thumb4',
117: 'right_forefinger1',
118: 'right_forefinger2',
119: 'right_forefinger3',
120: 'right_forefinger4',
121: 'right_middle_finger1',
122: 'right_middle_finger2',
123: 'right_middle_finger3',
124: 'right_middle_finger4',
125: 'right_ring_finger1',
126: 'right_ring_finger2',
127: 'right_ring_finger3',
128: 'right_ring_finger4',
129: 'right_pinky_finger1',
130: 'right_pinky_finger2',
131: 'right_pinky_finger3',
132: 'right_pinky_finger4'
},
'skeleton': [
[15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12],
[5, 6], [5, 7], [6, 8], [7, 9], [8, 10], [1, 2], [0, 1], [0, 2],
[1, 3], [2, 4], [3, 5], [4, 6], [15, 17], [15, 18], [15, 19],
[16, 20], [16, 21], [16, 22], [91, 92], [92, 93], [93, 94],
[94, 95], [91, 96], [96, 97], [97, 98], [98, 99], [91, 100],
[100, 101], [101, 102], [102, 103], [91, 104], [104, 105],
[105, 106], [106, 107], [91, 108], [108, 109], [109, 110],
[110, 111], [112, 113], [113, 114], [114, 115], [115, 116],
[112, 117], [117, 118], [118, 119], [119, 120], [112, 121],
[121, 122], [122, 123], [123, 124], [112, 125], [125, 126],
[126, 127], [127, 128], [112, 129], [129, 130], [130, 131],
[131, 132]
]
}
}
return joints
def draw_points(image, points, color_palette='tab20', palette_samples=16, confidence_threshold=0.5):
"""
Draws `points` on `image`.
Args:
image: image in opencv format
points: list of points to be drawn.
Shape: (nof_points, 3)
Format: each point should contain (y, x, confidence)
color_palette: name of a matplotlib color palette
Default: 'tab20'
palette_samples: number of different colors sampled from the `color_palette`
Default: 16
confidence_threshold: only points with a confidence higher than this threshold will be drawn. Range: [0, 1]
Default: 0.5
Returns:
A new image with overlaid points
"""
try:
colors = np.round(
np.array(plt.get_cmap(color_palette).colors) * 255
).astype(np.uint8)[:, ::-1].tolist()
except AttributeError: # if palette has not pre-defined colors
colors = np.round(
np.array(plt.get_cmap(color_palette)(np.linspace(0, 1, palette_samples))) * 255
).astype(np.uint8)[:, -2::-1].tolist()
circle_size = max(1, min(image.shape[:2]) // 150) # ToDo Shape it taking into account the size of the detection
# circle_size = max(2, int(np.sqrt(np.max(np.max(points, axis=0) - np.min(points, axis=0)) // 16)))
for i, pt in enumerate(points):
if pt[2] > confidence_threshold:
image = cv2.circle(image, (int(pt[1]), int(pt[0])), circle_size, tuple(colors[i % len(colors)]), -1)
return image
def draw_skeleton(image, points, skeleton, color_palette='Set2', palette_samples=8, person_index=0,
confidence_threshold=0.5):
"""
Draws a `skeleton` on `image`.
Args:
image: image in opencv format
points: list of points to be drawn.
Shape: (nof_points, 3)
Format: each point should contain (y, x, confidence)
skeleton: list of joints to be drawn
Shape: (nof_joints, 2)
Format: each joint should contain (point_a, point_b) where `point_a` and `point_b` are an index in `points`
color_palette: name of a matplotlib color palette
Default: 'Set2'
palette_samples: number of different colors sampled from the `color_palette`
Default: 8
person_index: index of the person in `image`
Default: 0
confidence_threshold: only points with a confidence higher than this threshold will be drawn. Range: [0, 1]
Default: 0.5
Returns:
A new image with overlaid joints
"""
try:
colors = np.round(
np.array(plt.get_cmap(color_palette).colors) * 255
).astype(np.uint8)[:, ::-1].tolist()
except AttributeError: # if palette has not pre-defined colors
colors = np.round(
np.array(plt.get_cmap(color_palette)(np.linspace(0, 1, palette_samples))) * 255
).astype(np.uint8)[:, -2::-1].tolist()
for i, joint in enumerate(skeleton):
pt1, pt2 = points[joint]
if pt1[2] > confidence_threshold and pt2[2] > confidence_threshold:
image = cv2.line(
image, (int(pt1[1]), int(pt1[0])), (int(pt2[1]), int(pt2[0])),
tuple(colors[person_index % len(colors)]), 2
)
return image
def draw_points_and_skeleton(image, points, skeleton, points_color_palette='tab20', points_palette_samples=16,
skeleton_color_palette='Set2', skeleton_palette_samples=8, person_index=0,
confidence_threshold=0.5):
"""
Draws `points` and `skeleton` on `image`.
Args:
image: image in opencv format
points: list of points to be drawn.
Shape: (nof_points, 3)
Format: each point should contain (y, x, confidence)
skeleton: list of joints to be drawn
Shape: (nof_joints, 2)
Format: each joint should contain (point_a, point_b) where `point_a` and `point_b` are an index in `points`
points_color_palette: name of a matplotlib color palette
Default: 'tab20'
points_palette_samples: number of different colors sampled from the `color_palette`
Default: 16
skeleton_color_palette: name of a matplotlib color palette
Default: 'Set2'
skeleton_palette_samples: number of different colors sampled from the `color_palette`
Default: 8
person_index: index of the person in `image`
Default: 0
confidence_threshold: only points with a confidence higher than this threshold will be drawn. Range: [0, 1]
Default: 0.5
Returns:
A new image with overlaid joints
"""
image = draw_skeleton(image, points, skeleton, color_palette=skeleton_color_palette,
palette_samples=skeleton_palette_samples, person_index=person_index,
confidence_threshold=confidence_threshold)
image = draw_points(image, points, color_palette=points_color_palette, palette_samples=points_palette_samples,
confidence_threshold=confidence_threshold)
return image
def save_images(images, target, joint_target, output, joint_output, joint_visibility, summary_writer=None, step=0,
prefix=''):
"""
Creates a grid of images with gt joints and a grid with predicted joints.
This is a basic function for debugging purposes only.
If summary_writer is not None, the grid will be written in that SummaryWriter with name "{prefix}_images" and
"{prefix}_predictions".
Args:
images (torch.Tensor): a tensor of images with shape (batch x channels x height x width).
target (torch.Tensor): a tensor of gt heatmaps with shape (batch x channels x height x width).
joint_target (torch.Tensor): a tensor of gt joints with shape (batch x joints x 2).
output (torch.Tensor): a tensor of predicted heatmaps with shape (batch x channels x height x width).
joint_output (torch.Tensor): a tensor of predicted joints with shape (batch x joints x 2).
joint_visibility (torch.Tensor): a tensor of joint visibility with shape (batch x joints).
summary_writer (tb.SummaryWriter): a SummaryWriter where write the grids.
Default: None
step (int): summary_writer step.
Default: 0
prefix (str): summary_writer name prefix.
Default: ""
Returns:
A pair of images which are built from torchvision.utils.make_grid
"""
# Input images with gt
images_ok = images.detach().clone()
images_ok[:, 0].mul_(0.229).add_(0.485)
images_ok[:, 1].mul_(0.224).add_(0.456)
images_ok[:, 2].mul_(0.225).add_(0.406)
for i in range(images.shape[0]):
joints = joint_target[i] * 4.
joints_vis = joint_visibility[i]
for joint, joint_vis in zip(joints, joints_vis):
if joint_vis[0]:
a = int(joint[1].item())
b = int(joint[0].item())
# images_ok[i][:, a-1:a+1, b-1:b+1] = torch.tensor([1, 0, 0])
images_ok[i][0, a - 1:a + 1, b - 1:b + 1] = 1
images_ok[i][1:, a - 1:a + 1, b - 1:b + 1] = 0
grid_gt = torchvision.utils.make_grid(images_ok, nrow=int(images_ok.shape[0] ** 0.5), padding=2, normalize=False)
if summary_writer is not None:
summary_writer.add_image(prefix + 'images', grid_gt, global_step=step)
# Input images with prediction
images_ok = images.detach().clone()
images_ok[:, 0].mul_(0.229).add_(0.485)
images_ok[:, 1].mul_(0.224).add_(0.456)
images_ok[:, 2].mul_(0.225).add_(0.406)
for i in range(images.shape[0]):
joints = joint_output[i] * 4.
joints_vis = joint_visibility[i]
for joint, joint_vis in zip(joints, joints_vis):
if joint_vis[0]:
a = int(joint[1].item())
b = int(joint[0].item())
# images_ok[i][:, a-1:a+1, b-1:b+1] = torch.tensor([1, 0, 0])
images_ok[i][0, a - 1:a + 1, b - 1:b + 1] = 1
images_ok[i][1:, a - 1:a + 1, b - 1:b + 1] = 0
grid_pred = torchvision.utils.make_grid(images_ok, nrow=int(images_ok.shape[0] ** 0.5), padding=2, normalize=False)
if summary_writer is not None:
summary_writer.add_image(prefix + 'predictions', grid_pred, global_step=step)
# Heatmaps
# ToDo
# for h in range(0,17):
# heatmap = torchvision.utils.make_grid(output[h].detach(), nrow=int(np.sqrt(output.shape[0])),
# padding=2, normalize=True, range=(0, 1))
# summary_writer.add_image('train_heatmap_%d' % h, heatmap, global_step=step + epoch*len_dl_train)
return grid_gt, grid_pred
def check_video_rotation(filename):
# thanks to
# https://stackoverflow.com/questions/53097092/frame-from-video-is-upside-down-after-extracting/55747773#55747773
# this returns meta-data of the video file in form of a dictionary
meta_dict = ffmpeg.probe(filename)
# from the dictionary, meta_dict['streams'][0]['tags']['rotate'] is the key
# we are looking for
rotation_code = None
try:
if int(meta_dict['streams'][0]['tags']['rotate']) == 90:
rotation_code = cv2.ROTATE_90_CLOCKWISE
elif int(meta_dict['streams'][0]['tags']['rotate']) == 180:
rotation_code = cv2.ROTATE_180
elif int(meta_dict['streams'][0]['tags']['rotate']) == 270:
rotation_code = cv2.ROTATE_90_COUNTERCLOCKWISE
else:
raise ValueError
except KeyError:
pass
return rotation_code
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