Merge pull request #2 from sssruhan1/master

python version of pck and visualization

evaluation/eval_PCKh.py

@@ -0,0 +1,90 @@
+from scipy.io import loadmat
+from numpy import transpose
+import skimage.io as sio
+from utils import visualize
+import numpy as np
+import os
+
+
+detection = loadmat('evaluation/data/detections.mat')
+det_idxs = detection['RELEASE_img_index']
+debug = 0
+threshold = 0.5
+SC_BIAS = 0.6
+
+pa = [2, 3, 7, 7, 4, 5, 8, 9, 10, 0, 12, 13, 8, 8, 14, 15]
+
+dict = loadmat('evaluation/data/detections_our_format.mat')
+dataset_joints = dict['dataset_joints']
+jnt_missing = dict['jnt_missing']
+pos_pred_src = dict['pos_pred_src']
+pos_gt_src = dict['pos_gt_src']
+headboxes_src = dict['headboxes_src']
+
+
+
+#predictions
+model_name = 'hg4'
+predfile = 'checkpoint/mpii/' + model_name + '/preds_valid.mat'
+preds = loadmat(predfile)['preds']
+pos_pred_src = transpose(preds, [1, 2, 0])
+
+
+if debug:
+
+    for i in range(len(det_idxs[0])):
+        anno = mat['RELEASE']['annolist'][0, 0][0][det_idxs[0][i] - 1]
+        fn = anno['image']['name'][0, 0][0]
+        imagePath = 'data/mpii/images/' + fn
+        oriImg = sio.imread(imagePath)
+        pred = pos_pred_src[:, :, i]
+        visualize(oriImg, pred, pa)
+
+
+head = np.where(dataset_joints == 'head')[1][0]
+lsho = np.where(dataset_joints == 'lsho')[1][0]
+lelb = np.where(dataset_joints == 'lelb')[1][0]
+lwri = np.where(dataset_joints == 'lwri')[1][0]
+lhip = np.where(dataset_joints == 'lhip')[1][0]
+lkne = np.where(dataset_joints == 'lkne')[1][0]
+lank = np.where(dataset_joints == 'lank')[1][0]
+
+rsho = np.where(dataset_joints == 'rsho')[1][0]
+relb = np.where(dataset_joints == 'relb')[1][0]
+rwri = np.where(dataset_joints == 'rwri')[1][0]
+rkne = np.where(dataset_joints == 'rkne')[1][0]
+rank = np.where(dataset_joints == 'rank')[1][0]
+rhip = np.where(dataset_joints == 'rhip')[1][0]
+
+jnt_visible = 1 - jnt_missing
+uv_error = pos_pred_src - pos_gt_src
+uv_err = np.linalg.norm(uv_error, axis=1)
+headsizes = headboxes_src[1, :, :] - headboxes_src[0, :, :]
+headsizes = np.linalg.norm(headsizes, axis=0)
+headsizes *= SC_BIAS
+scale = np.multiply(headsizes, np.ones((len(uv_err), 1)))
+scaled_uv_err = np.divide(uv_err, scale)
+scaled_uv_err = np.multiply(scaled_uv_err, jnt_visible)
+jnt_count = np.sum(jnt_visible, axis=1)
+less_than_threshold = np.multiply((scaled_uv_err < threshold), jnt_visible)
+PCKh = np.divide(100. * np.sum(less_than_threshold, axis=1), jnt_count)
+
+
+# save
+rng = np.arange(0, 0.5, 0.01)
+pckAll = np.zeros((len(rng), 16))
+
+for r in range(len(rng)):
+    threshold = rng[r]
+    less_than_threshold = np.multiply(scaled_uv_err < threshold, jnt_visible)
+    pckAll[r, :] = np.divide(100.*np.sum(less_than_threshold, axis=1), jnt_count)
+
+name = predfile.split(os.sep)[-1]
+PCKh = np.ma.array(PCKh, mask=False)
+PCKh.mask[6:7] = True
+print("Model,  Head,   Shoulder, Elbow,  Wrist,   Hip ,     Knee  , Ankle ,  Mean")
+print('{:s}   {:.2f}  {:.2f}     {:.2f}  {:.2f}   {:.2f}   {:.2f}   {:.2f}   {:.2f}'.format(model_name, PCKh[head], 0.5 * (PCKh[lsho] + PCKh[rsho])\
+        , 0.5 * (PCKh[lelb] + PCKh[relb]),0.5 * (PCKh[lwri] + PCKh[rwri]), 0.5 * (PCKh[lhip] + PCKh[rhip]), 0.5 * (PCKh[lkne] + PCKh[rkne]) \
+        , 0.5 * (PCKh[lank] + PCKh[rank]), np.mean(PCKh)))
+
+

evaluation/utils.py

@@ -0,0 +1,44 @@
+
+def visualize(oriImg, points, pa):
+    import matplotlib
+    import cv2 as cv
+    import matplotlib.pyplot as plt
+    import math
+
+    fig = matplotlib.pyplot.gcf()
+    # fig.set_size_inches(12, 12)
+
+    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]]
+    canvas = oriImg
+    stickwidth = 4
+    x = points[:, 0]
+    y = points[:, 1]
+
+    for n in range(len(x)):
+        for child in range(len(pa)):
+            if pa[child] is 0:
+                continue
+
+            x1 = x[pa[child] - 1]
+            y1 = y[pa[child] - 1]
+            x2 = x[child]
+            y2 = y[child]
+
+            cv.line(canvas, (x1, y1), (x2, y2), colors[child], 8)
+
+
+    plt.imshow(canvas[:, :, [2, 1, 0]])
+    fig = matplotlib.pyplot.gcf()
+    fig.set_size_inches(12, 12)
+
+    from time import gmtime, strftime
+    import os
+    directory = 'data/mpii/result/test_images'
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
+    fn = os.path.join(directory, strftime("%Y-%m-%d-%H_%M_%S", gmtime()) + '.jpg')
+
+    plt.savefig(fn)