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compute_coordinates.py
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compute_coordinates.py
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import pose_utils
import os
import numpy as np
from keras.models import load_model
import skimage.transform as st
import pandas as pd
from tqdm import tqdm
from skimage.io import imread
from skimage.transform import resize
from scipy.ndimage import gaussian_filter
from cmd import args
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]]
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]]
threshold = 0.1
boxsize = 368
scale_search = [0.5, 1, 1.5, 2]
def compute_cordinates(heatmap_avg, paf_avg, oriImg, th1=0.1, th2=0.05):
all_peaks = []
peak_counter = 0
for part in range(18):
map_ori = heatmap_avg[:,:,part]
map = gaussian_filter(map_ori, sigma=3)
map_left = np.zeros(map.shape)
map_left[1:,:] = map[:-1,:]
map_right = np.zeros(map.shape)
map_right[:-1,:] = map[1:,:]
map_up = np.zeros(map.shape)
map_up[:,1:] = map[:,:-1]
map_down = np.zeros(map.shape)
map_down[:,:-1] = map[:,1:]
peaks_binary = np.logical_and.reduce((map>=map_left, map>=map_right, map>=map_up, map>=map_down, map > th1))
peaks = 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]
id = range(peak_counter, peak_counter + len(peaks))
peaks_with_score_and_id = [peaks_with_score[i] + (id[i],) for i in range(len(id))]
all_peaks.append(peaks_with_score_and_id)
peak_counter += len(peaks)
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 = np.sqrt(vec[0]*vec[0] + vec[1]*vec[1])
vec = np.divide(vec, norm)
startend = 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 > th2)[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
print "found = 2"
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)
if len(subset) == 0:
return np.array([[-1, -1]] * 18).astype(int)
cordinates = []
result_image_index = np.argmax(subset[:, -2])
for part in subset[result_image_index, :18]:
if part == -1:
cordinates.append([-1, -1])
else:
Y = candidate[part.astype(int), 0]
X = candidate[part.astype(int), 1]
cordinates.append([X, Y])
return np.array(cordinates).astype(int)
def cordinates_from_image_file(image_name, model):
oriImg = imread(image_name)[:, :, ::-1] # B,G,R order
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]
new_size = (np.array(oriImg.shape[:2]) * scale).astype(np.int32)
imageToTest = resize(oriImg, new_size, order=3, preserve_range=True)
imageToTest_padded = imageToTest[np.newaxis, :, :, :]/255 - 0.5
output1, output2 = model.predict(imageToTest_padded)
heatmap = st.resize(output2[0], oriImg.shape[:2], preserve_range=True, order=1)
paf = st.resize(output1[0], oriImg.shape[:2], preserve_range=True, order=1)
heatmap_avg += heatmap
paf_avg += paf
heatmap_avg /= len(multiplier)
pose_cords = compute_cordinates(heatmap_avg, paf_avg, oriImg=oriImg)
return pose_cords
if __name__ == "__main__":
args = args()
model = load_model(args.pose_estimator)
for dataset in ['train', 'test']:
input_folder = vars(args)['images_dir_' + dataset]
output_path = vars(args)['annotations_file_' + dataset]
if os.path.exists(output_path):
processed_names = set(pd.read_csv(output_path, sep=':')['name'])
result_file = open(output_path, 'a')
else:
result_file = open(output_path, 'w')
processed_names = set()
print >> result_file, 'name:keypoints_y:keypoints_x'
for image_name in tqdm(os.listdir(input_folder)):
if image_name in processed_names:
continue
pose_cords = cordinates_from_image_file(os.path.join(input_folder, image_name), model=model)
print >> result_file, "%s: %s: %s" % (image_name, str(list(pose_cords[:, 0])), str(list(pose_cords[:, 1])))
result_file.flush()