def evaluate_afm(im_list, gt_list, afm):
print("Compute AFM mAP")
all_junc = np.zeros((0, 3))
all_junc_ids = np.zeros(0, dtype=np.int32)
all_jc_gt = []
afm = glob.glob(AFM)
afm.sort()
for i, (im_fn, gt_fn, afm_fn) in enumerate(zip(im_list, gt_list, afm)):
im = cv2.imread(im_fn)
im = cv2.resize(im, (128, 128))
with np.load(gt_fn) as npz:
junc_gt = npz["junc"][:, :2]
with np.load(afm_fn) as fafm:
afm_line = fafm["lines"].reshape(-1, 2, 2)[:, :, ::-1]
afm_score = -fafm["scores"]
h = fafm["h"]
w = fafm["w"]
afm_line[:, :, 0] *= 128 / h
afm_line[:, :, 1] *= 128 / w
jun_c = []
for line, score in zip(afm_line, afm_score):
jun_c.append(list(line[0]) + [score])
jun_c.append(list(line[1]) + [score])
jun_c = np.array(jun_c)
all_junc = np.vstack((all_junc, jun_c))
all_jc_gt.append(junc_gt)
all_junc_ids = np.hstack((all_junc_ids, np.array([i] * len(jun_c))))
all_junc_ids = all_junc_ids.astype(np.int64)
ap_jc = mAPJ(all_junc, all_jc_gt, DIST, all_junc_ids)
print(f" {ap_jc:.1f}")
def evaluate_lcnn(im_list, gt_list, lcnn_list):
# define result array to aggregate (n x 3) where 3 is (x, y, score)
all_junc = np.zeros((0, 3))
all_offset_junc = np.zeros((0, 3))
# for each detected junction, which image they correspond to
all_junc_ids = np.zeros(0, dtype=np.int32)
# gt is a list since the variable gt number per image
all_jc_gt = []
for i, (lcnn_fn, gt_fn) in enumerate(zip(lcnn_list, gt_list)):
with np.load(lcnn_fn) as npz:
result = {name: arr for name, arr in npz.items()}
jmap = result["jmap"]
joff = result["joff"]
with np.load(gt_fn) as npz:
junc_gt = npz["junc"][:, :2]
# for j in junc_gt:
# plt.scatter(round(j[1]), round(j[0]), c="red")
# for j in juncs_wf:
# plt.scatter(round(j[1]), round(j[0]), c="blue")
# plt.show()
jun_c = post_jheatmap(jmap[0])
all_junc = np.vstack((all_junc, jun_c))
jun_o_c = post_jheatmap(jmap[0], offset=joff[0])
all_offset_junc = np.vstack((all_offset_junc, jun_o_c))
all_jc_gt.append(junc_gt)
all_junc_ids = np.hstack((all_junc_ids, np.array([i] * len(jun_c))))
# sometimes filter all and concat empty list will change dtype
all_junc_ids = all_junc_ids.astype(np.int64)
ap_jc = mAPJ(all_junc, all_jc_gt, DIST, all_junc_ids)
ap_joc = mAPJ(all_offset_junc, all_jc_gt, DIST, all_junc_ids)
print(f" {ap_jc:.1f} | {ap_joc:.1f}")
def evaluate_wireframe(im_list, gt_list, juncs_wf):
print("Compute WF mAP")
juncs_wf = load_wf()
all_junc = np.zeros((0, 3))
all_junc_ids = np.zeros(0, dtype=np.int32)
all_jc_gt = []
for i, (im_fn, gt_fn, junc_wf) in enumerate(zip(im_list, gt_list, juncs_wf)):
im = cv2.imread(im_fn)
im = cv2.resize(im, (128, 128))
with np.load(gt_fn) as npz:
junc_gt = npz["junc"][:, :2]
jun_c = sorted(junc_wf, key=lambda x: -x[2])[:1000]
all_junc = np.vstack((all_junc, jun_c))
all_jc_gt.append(junc_gt)
all_junc_ids = np.hstack((all_junc_ids, np.array([i] * len(jun_c))))
all_junc_ids = all_junc_ids.astype(np.int64)
ap_jc = mAPJ(all_junc, all_jc_gt, DIST, all_junc_ids)
print(f" {ap_jc:.1f}")