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import cv2
import numpy as np
import math
import time
from scipy.ndimage.filters import gaussian_filter
import matplotlib.pyplot as plt
import matplotlib
import torch
from torchvision import transforms
from typing import NamedTuple, List, Union
from . import util
from .model import bodypose_model
from .types import Keypoint, BodyResult
class Body(object):
def __init__(self, model_path):
self.model = bodypose_model()
# if torch.cuda.is_available():
# self.model = self.model.cuda()
# print('cuda')
model_dict = util.transfer(self.model, torch.load(model_path))
self.model.load_state_dict(model_dict)
self.model.eval()
def __call__(self, oriImg):
# 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 / oriImg.shape[0] for x in scale_search]
heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
for m in range(len(multiplier)):
scale = multiplier[m]
imageToTest = util.smart_resize_k(oriImg, fx=scale, fy=scale)
imageToTest_padded, pad = util.padRightDownCorner(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()
if torch.cuda.is_available():
data = data.cuda()
# data = data.permute([2, 0, 1]).unsqueeze(0).float()
with torch.no_grad():
data = data.to(self.cn_device)
Mconv7_stage6_L1, Mconv7_stage6_L2 = self.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 = util.smart_resize_k(heatmap, fx=stride, fy=stride)
heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
heatmap = util.smart_resize(heatmap, (oriImg.shape[0], oriImg.shape[1]))
# 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 = util.smart_resize_k(paf, fx=stride, fy=stride)
paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
paf = util.smart_resize(paf, (oriImg.shape[0], oriImg.shape[1]))
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))])
vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
for I in range(len(startend))])
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 * oriImg.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 in range(len(subset)): # 1:size(subset,1):
if subset[j][indexA] == partAs[i] or subset[j][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 in range(len(subset)):
if subset[i][-1] < 4 or subset[i][-2] / subset[i][-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
@staticmethod
def format_body_result(candidate: np.ndarray, subset: np.ndarray) -> List[BodyResult]:
"""
Format the body results from the candidate and subset arrays into a list of BodyResult objects.
Args:
candidate (np.ndarray): An array of candidates containing the x, y coordinates, score, and id
for each body part.
subset (np.ndarray): An array of subsets containing indices to the candidate array for each
person detected. The last two columns of each row hold the total score and total parts
of the person.
Returns:
List[BodyResult]: A list of BodyResult objects, where each object represents a person with
detected keypoints, total score, and total parts.
"""
return [
BodyResult(
keypoints=[
Keypoint(
x=candidate[candidate_index][0],
y=candidate[candidate_index][1],
score=candidate[candidate_index][2],
id=candidate[candidate_index][3]
) if candidate_index != -1 else None
for candidate_index in person[:18].astype(int)
],
total_score=person[18],
total_parts=person[19]
)
for person in subset
]
if __name__ == "__main__":
body_estimation = Body('../model/body_pose_model.pth')
test_image = '../images/ski.jpg'
oriImg = cv2.imread(test_image) # B,G,R order
candidate, subset = body_estimation(oriImg)
bodies = body_estimation.format_body_result(candidate, subset)
canvas = oriImg
for body in bodies:
canvas = util.draw_bodypose(canvas, body)
plt.imshow(canvas[:, :, [2, 1, 0]])
plt.show() |