}
img_paths = os.listdir(params['test_img_directory'])
for img_path in img_paths:
img = cv2.imread(os.path.join(params['test_img_directory'], img_path))
bbox = bounding_box[img_path[0:12]]
size = max((bbox[2] - bbox[0]), (bbox[3] - bbox[1]))
img_crop = np.copy(img)[bbox[1]:bbox[1] + size, bbox[0]:bbox[0] + size]
img_resize = cv2.resize(img_crop, (256, 256))
#test_img = cv2.cvtColor(test_img, cv2.COLOR_BGR2RGB)
if predictJoints:
predictions = model.predictJoints(img_resize, mode='gpu')
print(' Predict on {}'.format(img_path))
print(
np.add(
np.asarray(predictions) * (bbox[1] - bbox[0]) / 256,
np.array([bbox[0], bbox[2]])))
show_prections(img_resize, predictions,
img_path[0:-4] + '_pred' + img_path[-4:])
else:
# output heatmap = 1*64 x 64 x outputDim
out_heatmap = np.squeeze(model.predictHM(img_resize))
for i in range(out_heatmap.shape[2]):
joint_hm = np.asarray(cv2.resize(255 * out_heatmap[:, :, i],
(256, 256)),
dtype=np.uint8)
joint_hm = cv2.applyColorMap(joint_hm, cv2.COLORMAP_JET)
cv2.imwrite(
os.path.join(
params['test_result_directory'], img_path[0:-4] +
'_pred_joint' + str(i) + img_path[-4:]),
0.5 * img_resize + 0.5 * joint_hm)
#['25000','25001','25002','25003','25004','25005','25006','25007','25008','25009',
#'25010', '25011', '25012', '25013', '25014', '25015', '25016', '25017', '25018', '25019']
#['00010'] #['00358','00826','03783','04646','06097','06351','06402','06685','08684','08836',
# '09147','09184','09301','09410','09462','09530','09557','09795','09887','09985',
# '10004','10005','10006','10010','10037','10041','10046','10048','10071','10084',
# '20000','20001','20002','20003','20004','20005','20006','20007','20008','20009','20010','20011','20012']
joint_list = [
'e', 't', 'a', 'o', 'i', 'n', 's', 'r', 'h', 'l', 'd', 'c', 'u', 'm', 'f',
'p', 'g', 'w', 'y', 'b', 'v', 'k', 'x', 'j', 'q', 'z'
]
for ex in range(len(examples)):
img = cv2.imread("dataMarsden26FREQ-CROPPEDSELECTION/" + examples[ex] +
".jpg")
img = cv2.resize(img, (256, 256))
hms = infer.predictHM(img)
#print(hms.shape)
for i in range(np.shape(hms)[3]):
max = np.amax(hms[0, :, :, i])
temp = copy.deepcopy(hms[0, :, :, i] * (150 / max))
#cv2.imwrite('testing/FORPAPERUNIF301_output' +examples[ex]+ '_' + str(joint_list[i]) + '.jpg', temp)
temp_big = cv2.resize(temp, (256, 256))
img_grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imwrite(
'heatmaps-good/Marsden26FREQCROPPED/e2043_26FREQ_CROPPED_256_8_' +
examples[ex] + '_' + str(joint_list[i]) + '.jpg',
(img_grey + temp_big)) #np.maximum(img_grey,temp_big))
print(examples[ex] + ".jpg done")
#index=np.argmax(hms[0,:,:,i])
#print(str(joint_list[i]), index, max, np.argmin(hms[0,:,:,i]), np.amin(hms[0,:,:,i]))
