imaginAIry/imaginairy/img_processors/openpose.py
2023-12-15 14:32:01 -08:00

837 lines
29 KiB
Python

"""Functions for human pose estimation"""
import math
from collections import OrderedDict
from functools import lru_cache
import cv2
import matplotlib as mpl
import numpy as np
import torch
from scipy.ndimage.filters import gaussian_filter
from torch import nn
from imaginairy.utils import get_device
from imaginairy.utils.img_utils import torch_image_to_openvcv_img
from imaginairy.utils.model_manager import get_cached_url_path
def pad_right_down_corner(img, stride, padValue):
h = img.shape[0]
w = img.shape[1]
pad = 4 * [None]
pad[0] = 0 # up
pad[1] = 0 # left
pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right
img_padded = img
pad_up = np.tile(img_padded[0:1, :, :] * 0 + padValue, (pad[0], 1, 1))
img_padded = np.concatenate((pad_up, img_padded), axis=0)
pad_left = np.tile(img_padded[:, 0:1, :] * 0 + padValue, (1, pad[1], 1))
img_padded = np.concatenate((pad_left, img_padded), axis=1)
pad_down = np.tile(img_padded[-2:-1, :, :] * 0 + padValue, (pad[2], 1, 1))
img_padded = np.concatenate((img_padded, pad_down), axis=0)
pad_right = np.tile(img_padded[:, -2:-1, :] * 0 + padValue, (1, pad[3], 1))
img_padded = np.concatenate((img_padded, pad_right), axis=1)
return img_padded, pad
def transfer(model, model_weights):
# transfer caffe model to pytorch which will match the layer name
transfered_model_weights = {}
for weights_name in model.state_dict():
transfered_model_weights[weights_name] = model_weights[
".".join(weights_name.split(".")[1:])
]
return transfered_model_weights
# draw the body keypoint and lims
def draw_bodypose(canvas, candidate, subset):
stickwidth = 4
limbSeq = [
[2, 3],
[2, 6],
[3, 4],
[4, 5],
[6, 7],
[7, 8],
[2, 9],
[9, 10],
[10, 11],
[2, 12],
[12, 13],
[13, 14],
[2, 1],
[1, 15],
[15, 17],
[1, 16],
[16, 18],
[3, 17],
[6, 18],
]
colors = [
[255, 0, 0],
[255, 85, 0],
[255, 170, 0],
[255, 255, 0],
[170, 255, 0],
[85, 255, 0],
[0, 255, 0],
[0, 255, 85],
[0, 255, 170],
[0, 255, 255],
[0, 170, 255],
[0, 85, 255],
[0, 0, 255],
[85, 0, 255],
[170, 0, 255],
[255, 0, 255],
[255, 0, 170],
[255, 0, 85],
]
for i in range(18):
for n in range(len(subset)):
index = int(subset[n][i])
if index == -1:
continue
x, y = candidate[index][0:2]
cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
for i in range(17):
for n in range(len(subset)):
index = subset[n][np.array(limbSeq[i]) - 1]
if -1 in index:
continue
cur_canvas = canvas.copy()
Y = candidate[index.astype(int), 0]
X = candidate[index.astype(int), 1]
mX = np.mean(X)
mY = np.mean(Y)
length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
polygon = cv2.ellipse2Poly(
(int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1
)
cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
# plt.imsave("preview.jpg", canvas[:, :, [2, 1, 0]])
# plt.imshow(canvas[:, :, [2, 1, 0]])
return canvas
# image drawed by opencv is not good.
def draw_handpose(canvas, all_hand_peaks, show_number=False):
edges = [
[0, 1],
[1, 2],
[2, 3],
[3, 4],
[0, 5],
[5, 6],
[6, 7],
[7, 8],
[0, 9],
[9, 10],
[10, 11],
[11, 12],
[0, 13],
[13, 14],
[14, 15],
[15, 16],
[0, 17],
[17, 18],
[18, 19],
[19, 20],
]
for peaks in all_hand_peaks:
for ie, e in enumerate(edges):
if np.sum(np.all(peaks[e], axis=1) == 0) == 0:
x1, y1 = peaks[e[0]]
x2, y2 = peaks[e[1]]
cv2.line(
canvas,
(x1, y1),
(x2, y2),
mpl.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255,
thickness=2,
)
for i, keyponit in enumerate(peaks):
x, y = keyponit
cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
if show_number:
cv2.putText(
canvas,
str(i),
(x, y),
cv2.FONT_HERSHEY_SIMPLEX,
0.3,
(0, 0, 0),
lineType=cv2.LINE_AA,
)
return canvas
# detect hand according to body pose keypoints
# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
def handDetect(candidate, subset, oriImg):
# right hand: wrist 4, elbow 3, shoulder 2
# left hand: wrist 7, elbow 6, shoulder 5
ratioWristElbow = 0.33
detect_result = []
image_height, image_width = oriImg.shape[0:2]
for person in subset.astype(int):
# if any of three not detected
has_left = np.sum(person[[5, 6, 7]] == -1) == 0
has_right = np.sum(person[[2, 3, 4]] == -1) == 0
if not (has_left or has_right):
continue
hands = []
# left hand
if has_left:
left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]]
x1, y1 = candidate[left_shoulder_index][:2]
x2, y2 = candidate[left_elbow_index][:2]
x3, y3 = candidate[left_wrist_index][:2]
hands.append([x1, y1, x2, y2, x3, y3, True])
# right hand
if has_right:
right_shoulder_index, right_elbow_index, right_wrist_index = person[
[2, 3, 4]
]
x1, y1 = candidate[right_shoulder_index][:2]
x2, y2 = candidate[right_elbow_index][:2]
x3, y3 = candidate[right_wrist_index][:2]
hands.append([x1, y1, x2, y2, x3, y3, False])
for x1, y1, x2, y2, x3, y3, is_left in hands:
# pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
# handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
# handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
# const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
# const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
# handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
x = x3 + ratioWristElbow * (x3 - x2)
y = y3 + ratioWristElbow * (y3 - y2)
distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
# x-y refers to the center --> offset to topLeft point
# handRectangle.x -= handRectangle.width / 2.f;
# handRectangle.y -= handRectangle.height / 2.f;
x -= width / 2
y -= width / 2 # width = height
# overflow the image
x = max(x, 0)
y = max(y, 0)
width1 = width
width2 = width
if x + width > image_width:
width1 = image_width - x
if y + width > image_height:
width2 = image_height - y
width = min(width1, width2)
# the max hand box value is 20 pixels
if width >= 20:
detect_result.append([int(x), int(y), int(width), is_left])
# return value: [[x, y, w, True if left hand else False]].
# width=height since the network require squared input.
# x, y is the coordinate of top left
return detect_result
# get max index of 2d array
def npmax(array):
arrayindex = array.argmax(1)
arrayvalue = array.max(1)
i = arrayvalue.argmax()
j = arrayindex[i]
return i, j
def make_layers(block, no_relu_layers):
layers = []
for layer_name, v in block.items():
if "pool" in layer_name:
layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], padding=v[2])
layers.append((layer_name, layer))
else:
conv2d = nn.Conv2d(
in_channels=v[0],
out_channels=v[1],
kernel_size=v[2],
stride=v[3],
padding=v[4],
)
layers.append((layer_name, conv2d))
if layer_name not in no_relu_layers:
layers.append(("relu_" + layer_name, nn.ReLU(inplace=True)))
return nn.Sequential(OrderedDict(layers))
class bodypose_model(nn.Module):
def __init__(self):
super().__init__()
# these layers have no relu layer
no_relu_layers = [
"conv5_5_CPM_L1",
"conv5_5_CPM_L2",
"Mconv7_stage2_L1",
"Mconv7_stage2_L2",
"Mconv7_stage3_L1",
"Mconv7_stage3_L2",
"Mconv7_stage4_L1",
"Mconv7_stage4_L2",
"Mconv7_stage5_L1",
"Mconv7_stage5_L2",
"Mconv7_stage6_L1",
"Mconv7_stage6_L1",
]
blocks = {}
block0 = OrderedDict(
[
("conv1_1", [3, 64, 3, 1, 1]),
("conv1_2", [64, 64, 3, 1, 1]),
("pool1_stage1", [2, 2, 0]),
("conv2_1", [64, 128, 3, 1, 1]),
("conv2_2", [128, 128, 3, 1, 1]),
("pool2_stage1", [2, 2, 0]),
("conv3_1", [128, 256, 3, 1, 1]),
("conv3_2", [256, 256, 3, 1, 1]),
("conv3_3", [256, 256, 3, 1, 1]),
("conv3_4", [256, 256, 3, 1, 1]),
("pool3_stage1", [2, 2, 0]),
("conv4_1", [256, 512, 3, 1, 1]),
("conv4_2", [512, 512, 3, 1, 1]),
("conv4_3_CPM", [512, 256, 3, 1, 1]),
("conv4_4_CPM", [256, 128, 3, 1, 1]),
]
)
# Stage 1
block1_1 = OrderedDict(
[
("conv5_1_CPM_L1", [128, 128, 3, 1, 1]),
("conv5_2_CPM_L1", [128, 128, 3, 1, 1]),
("conv5_3_CPM_L1", [128, 128, 3, 1, 1]),
("conv5_4_CPM_L1", [128, 512, 1, 1, 0]),
("conv5_5_CPM_L1", [512, 38, 1, 1, 0]),
]
)
block1_2 = OrderedDict(
[
("conv5_1_CPM_L2", [128, 128, 3, 1, 1]),
("conv5_2_CPM_L2", [128, 128, 3, 1, 1]),
("conv5_3_CPM_L2", [128, 128, 3, 1, 1]),
("conv5_4_CPM_L2", [128, 512, 1, 1, 0]),
("conv5_5_CPM_L2", [512, 19, 1, 1, 0]),
]
)
blocks["block1_1"] = block1_1
blocks["block1_2"] = block1_2
self.model0 = make_layers(block0, no_relu_layers)
# Stages 2 - 6
for i in range(2, 7):
blocks[f"block{i}_1"] = OrderedDict(
[
(f"Mconv1_stage{i}_L1", [185, 128, 7, 1, 3]),
(f"Mconv2_stage{i}_L1", [128, 128, 7, 1, 3]),
(f"Mconv3_stage{i}_L1", [128, 128, 7, 1, 3]),
(f"Mconv4_stage{i}_L1", [128, 128, 7, 1, 3]),
(f"Mconv5_stage{i}_L1", [128, 128, 7, 1, 3]),
(f"Mconv6_stage{i}_L1", [128, 128, 1, 1, 0]),
(f"Mconv7_stage{i}_L1", [128, 38, 1, 1, 0]),
]
)
blocks[f"block{i}_2"] = OrderedDict(
[
(f"Mconv1_stage{i}_L2", [185, 128, 7, 1, 3]),
(f"Mconv2_stage{i}_L2", [128, 128, 7, 1, 3]),
(f"Mconv3_stage{i}_L2", [128, 128, 7, 1, 3]),
(f"Mconv4_stage{i}_L2", [128, 128, 7, 1, 3]),
(f"Mconv5_stage{i}_L2", [128, 128, 7, 1, 3]),
(f"Mconv6_stage{i}_L2", [128, 128, 1, 1, 0]),
(f"Mconv7_stage{i}_L2", [128, 19, 1, 1, 0]),
]
)
for k in blocks:
blocks[k] = make_layers(blocks[k], no_relu_layers)
self.model1_1 = blocks["block1_1"]
self.model2_1 = blocks["block2_1"]
self.model3_1 = blocks["block3_1"]
self.model4_1 = blocks["block4_1"]
self.model5_1 = blocks["block5_1"]
self.model6_1 = blocks["block6_1"]
self.model1_2 = blocks["block1_2"]
self.model2_2 = blocks["block2_2"]
self.model3_2 = blocks["block3_2"]
self.model4_2 = blocks["block4_2"]
self.model5_2 = blocks["block5_2"]
self.model6_2 = blocks["block6_2"]
def forward(self, x):
out1 = self.model0(x)
out1_1 = self.model1_1(out1)
out1_2 = self.model1_2(out1)
out2 = torch.cat([out1_1, out1_2, out1], 1)
out2_1 = self.model2_1(out2)
out2_2 = self.model2_2(out2)
out3 = torch.cat([out2_1, out2_2, out1], 1)
out3_1 = self.model3_1(out3)
out3_2 = self.model3_2(out3)
out4 = torch.cat([out3_1, out3_2, out1], 1)
out4_1 = self.model4_1(out4)
out4_2 = self.model4_2(out4)
out5 = torch.cat([out4_1, out4_2, out1], 1)
out5_1 = self.model5_1(out5)
out5_2 = self.model5_2(out5)
out6 = torch.cat([out5_1, out5_2, out1], 1)
out6_1 = self.model6_1(out6)
out6_2 = self.model6_2(out6)
return out6_1, out6_2
class handpose_model(nn.Module):
def __init__(self):
super().__init__()
# these layers have no relu layer
no_relu_layers = [
"conv6_2_CPM",
"Mconv7_stage2",
"Mconv7_stage3",
"Mconv7_stage4",
"Mconv7_stage5",
"Mconv7_stage6",
]
# stage 1
block1_0 = OrderedDict(
[
("conv1_1", [3, 64, 3, 1, 1]),
("conv1_2", [64, 64, 3, 1, 1]),
("pool1_stage1", [2, 2, 0]),
("conv2_1", [64, 128, 3, 1, 1]),
("conv2_2", [128, 128, 3, 1, 1]),
("pool2_stage1", [2, 2, 0]),
("conv3_1", [128, 256, 3, 1, 1]),
("conv3_2", [256, 256, 3, 1, 1]),
("conv3_3", [256, 256, 3, 1, 1]),
("conv3_4", [256, 256, 3, 1, 1]),
("pool3_stage1", [2, 2, 0]),
("conv4_1", [256, 512, 3, 1, 1]),
("conv4_2", [512, 512, 3, 1, 1]),
("conv4_3", [512, 512, 3, 1, 1]),
("conv4_4", [512, 512, 3, 1, 1]),
("conv5_1", [512, 512, 3, 1, 1]),
("conv5_2", [512, 512, 3, 1, 1]),
("conv5_3_CPM", [512, 128, 3, 1, 1]),
]
)
block1_1 = OrderedDict(
[("conv6_1_CPM", [128, 512, 1, 1, 0]), ("conv6_2_CPM", [512, 22, 1, 1, 0])]
)
blocks = {}
blocks["block1_0"] = block1_0
blocks["block1_1"] = block1_1
# stage 2-6
for i in range(2, 7):
blocks[f"block{i}"] = OrderedDict(
[
(f"Mconv1_stage{i}", [150, 128, 7, 1, 3]),
(f"Mconv2_stage{i}", [128, 128, 7, 1, 3]),
(f"Mconv3_stage{i}", [128, 128, 7, 1, 3]),
(f"Mconv4_stage{i}", [128, 128, 7, 1, 3]),
(f"Mconv5_stage{i}", [128, 128, 7, 1, 3]),
(f"Mconv6_stage{i}", [128, 128, 1, 1, 0]),
(f"Mconv7_stage{i}", [128, 22, 1, 1, 0]),
]
)
for k in blocks:
blocks[k] = make_layers(blocks[k], no_relu_layers)
self.model1_0 = blocks["block1_0"]
self.model1_1 = blocks["block1_1"]
self.model2 = blocks["block2"]
self.model3 = blocks["block3"]
self.model4 = blocks["block4"]
self.model5 = blocks["block5"]
self.model6 = blocks["block6"]
def forward(self, x):
out1_0 = self.model1_0(x)
out1_1 = self.model1_1(out1_0)
concat_stage2 = torch.cat([out1_1, out1_0], 1)
out_stage2 = self.model2(concat_stage2)
concat_stage3 = torch.cat([out_stage2, out1_0], 1)
out_stage3 = self.model3(concat_stage3)
concat_stage4 = torch.cat([out_stage3, out1_0], 1)
out_stage4 = self.model4(concat_stage4)
concat_stage5 = torch.cat([out_stage4, out1_0], 1)
out_stage5 = self.model5(concat_stage5)
concat_stage6 = torch.cat([out_stage5, out1_0], 1)
out_stage6 = self.model6(concat_stage6)
return out_stage6
@lru_cache(maxsize=1)
def openpose_model():
model = bodypose_model()
weights_url = "https://huggingface.co/lllyasviel/ControlNet/resolve/38a62cbf79862c1bac73405ec8dc46133aee3e36/annotator/ckpts/body_pose_model.pth"
model_path = get_cached_url_path(weights_url)
model_dict = transfer(model, torch.load(model_path))
model.load_state_dict(model_dict)
model.eval()
return model
def create_body_pose_img(original_img_t):
candidate, subset = create_body_pose(original_img_t)
canvas = np.zeros((original_img_t.shape[2], original_img_t.shape[3], 3))
canvas = draw_bodypose(canvas, candidate, subset)
canvas = torch.from_numpy(canvas).to(dtype=torch.float32)
# canvas = canvas.unsqueeze(0)
canvas = canvas.permute(2, 0, 1).unsqueeze(0)
return canvas
def create_body_pose(original_img_t):
original_img = torch_image_to_openvcv_img(original_img_t)
model = openpose_model()
# scale_search = [0.5, 1.0, 1.5, 2.0]
scale_search = [0.5]
boxsize = 368
stride = 8
padValue = 128
thre1 = 0.1
thre2 = 0.05
multiplier = [x * boxsize / original_img.shape[0] for x in scale_search]
heatmap_avg = np.zeros((original_img.shape[0], original_img.shape[1], 19))
paf_avg = np.zeros((original_img.shape[0], original_img.shape[1], 38))
for m, scale in enumerate(multiplier):
imageToTest = cv2.resize(
original_img, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC
)
imageToTest_padded, pad = pad_right_down_corner(imageToTest, stride, padValue)
im = (
np.transpose(
np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)
)
/ 256
- 0.5
)
im = np.ascontiguousarray(im)
data = torch.from_numpy(im).float()
data.to(get_device())
# data = data.permute([2, 0, 1]).unsqueeze(0).float()
with torch.no_grad():
Mconv7_stage6_L1, Mconv7_stage6_L2 = model(data)
Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()
# extract outputs, resize, and remove padding
# heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0)) # output 1 is heatmaps
heatmap = np.transpose(
np.squeeze(Mconv7_stage6_L2), (1, 2, 0)
) # output 1 is heatmaps
heatmap = cv2.resize(
heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC
)
heatmap = heatmap[
: imageToTest_padded.shape[0] - pad[2],
: imageToTest_padded.shape[1] - pad[3],
:,
]
heatmap = cv2.resize(
heatmap,
(original_img.shape[1], original_img.shape[0]),
interpolation=cv2.INTER_CUBIC,
)
# paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0)) # output 0 is PAFs
paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0)) # output 0 is PAFs
paf = cv2.resize(
paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC
)
paf = paf[
: imageToTest_padded.shape[0] - pad[2],
: imageToTest_padded.shape[1] - pad[3],
:,
]
paf = cv2.resize(
paf,
(original_img.shape[1], original_img.shape[0]),
interpolation=cv2.INTER_CUBIC,
)
heatmap_avg += heatmap_avg + heatmap / len(multiplier)
paf_avg += +paf / len(multiplier)
all_peaks = []
peak_counter = 0
for part in range(18):
map_ori = heatmap_avg[:, :, part]
one_heatmap = gaussian_filter(map_ori, sigma=3)
map_left = np.zeros(one_heatmap.shape)
map_left[1:, :] = one_heatmap[:-1, :]
map_right = np.zeros(one_heatmap.shape)
map_right[:-1, :] = one_heatmap[1:, :]
map_up = np.zeros(one_heatmap.shape)
map_up[:, 1:] = one_heatmap[:, :-1]
map_down = np.zeros(one_heatmap.shape)
map_down[:, :-1] = one_heatmap[:, 1:]
peaks_binary = np.logical_and.reduce(
(
one_heatmap >= map_left,
one_heatmap >= map_right,
one_heatmap >= map_up,
one_heatmap >= map_down,
one_heatmap > thre1,
)
)
peaks = list(
zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])
) # note reverse
peaks_with_score = [(*x, map_ori[x[1], x[0]]) for x in peaks]
peak_id = range(peak_counter, peak_counter + len(peaks))
peaks_with_score_and_id = [
peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))
]
all_peaks.append(peaks_with_score_and_id)
peak_counter += len(peaks)
# find connection in the specified sequence, center 29 is in the position 15
limbSeq = [
[2, 3],
[2, 6],
[3, 4],
[4, 5],
[6, 7],
[7, 8],
[2, 9],
[9, 10],
[10, 11],
[2, 12],
[12, 13],
[13, 14],
[2, 1],
[1, 15],
[15, 17],
[1, 16],
[16, 18],
[3, 17],
[6, 18],
]
# the middle joints heatmap correpondence
mapIdx = [
[31, 32],
[39, 40],
[33, 34],
[35, 36],
[41, 42],
[43, 44],
[19, 20],
[21, 22],
[23, 24],
[25, 26],
[27, 28],
[29, 30],
[47, 48],
[49, 50],
[53, 54],
[51, 52],
[55, 56],
[37, 38],
[45, 46],
]
connection_all = []
special_k = []
mid_num = 10
for k in range(len(mapIdx)):
score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
candA = all_peaks[limbSeq[k][0] - 1]
candB = all_peaks[limbSeq[k][1] - 1]
nA = len(candA)
nB = len(candB)
indexA, indexB = limbSeq[k]
if nA != 0 and nB != 0:
connection_candidate = []
for i in range(nA):
for j in range(nB):
vec = np.subtract(candB[j][:2], candA[i][:2])
norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
norm = max(0.001, norm)
vec = np.divide(vec, norm)
startend = list(
zip(
np.linspace(candA[i][0], candB[j][0], num=mid_num),
np.linspace(candA[i][1], candB[j][1], num=mid_num),
)
)
vec_x = np.array(
[
score_mid[
int(round(startend[I][1])),
int(round(startend[I][0])),
0,
]
for I in range(len(startend)) # noqa
]
)
vec_y = np.array(
[
score_mid[
int(round(startend[I][1])),
int(round(startend[I][0])),
1,
]
for I in range(len(startend)) # noqa
]
)
score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(
vec_y, vec[1]
)
score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
0.5 * original_img.shape[0] / norm - 1, 0
)
criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(
score_midpts
)
criterion2 = score_with_dist_prior > 0
if criterion1 and criterion2:
connection_candidate.append(
[
i,
j,
score_with_dist_prior,
score_with_dist_prior + candA[i][2] + candB[j][2],
]
)
connection_candidate = sorted(
connection_candidate, key=lambda x: x[2], reverse=True
)
connection = np.zeros((0, 5))
for c in range(len(connection_candidate)):
i, j, s = connection_candidate[c][0:3]
if i not in connection[:, 3] and j not in connection[:, 4]:
connection = np.vstack(
[connection, [candA[i][3], candB[j][3], s, i, j]]
)
if len(connection) >= min(nA, nB):
break
connection_all.append(connection)
else:
special_k.append(k)
connection_all.append([])
# last number in each row is the total parts number of that person
# the second last number in each row is the score of the overall configuration
subset = -1 * np.ones((0, 20))
candidate = np.array([item for sublist in all_peaks for item in sublist])
for k in range(len(mapIdx)):
if k not in special_k:
partAs = connection_all[k][:, 0]
partBs = connection_all[k][:, 1]
indexA, indexB = np.array(limbSeq[k]) - 1
for i in range(len(connection_all[k])): # = 1:size(temp,1)
found = 0
subset_idx = [-1, -1]
for j, row in enumerate(subset): # 1:size(subset,1):
if row[indexA] == partAs[i] or row[indexB] == partBs[i]:
subset_idx[found] = j
found += 1
if found == 1:
j = subset_idx[0]
if subset[j][indexB] != partBs[i]:
subset[j][indexB] = partBs[i]
subset[j][-1] += 1
subset[j][-2] += (
candidate[partBs[i].astype(int), 2]
+ connection_all[k][i][2]
)
elif found == 2: # if found 2 and disjoint, merge them
j1, j2 = subset_idx
membership = (
(subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int)
)[:-2]
if len(np.nonzero(membership == 2)[0]) == 0: # merge
subset[j1][:-2] += subset[j2][:-2] + 1
subset[j1][-2:] += subset[j2][-2:]
subset[j1][-2] += connection_all[k][i][2]
subset = np.delete(subset, j2, 0)
else: # as like found == 1
subset[j1][indexB] = partBs[i]
subset[j1][-1] += 1
subset[j1][-2] += (
candidate[partBs[i].astype(int), 2]
+ connection_all[k][i][2]
)
# if find no partA in the subset, create a new subset
elif not found and k < 17:
row = -1 * np.ones(20)
row[indexA] = partAs[i]
row[indexB] = partBs[i]
row[-1] = 2
row[-2] = (
sum(candidate[connection_all[k][i, :2].astype(int), 2])
+ connection_all[k][i][2]
)
subset = np.vstack([subset, row])
# delete some rows of subset which has few parts occur
deleteIdx = []
for i, s in enumerate(subset):
if s[-1] < 4 or s[-2] / s[-1] < 0.4:
deleteIdx.append(i)
subset = np.delete(subset, deleteIdx, axis=0)
# subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
# candidate: x, y, score, id
return candidate, subset