script for computing IOU for tensorflow object detection

Author: Geoyi

examples/utils/tf_iou.py

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+"""
+Python script to obtain IOU (aslo know as Jaccard index) for Tensorflow object detection.
+For defination of IOU, please see: https://en.wikipedia.org/wiki/Jaccard_index.
+Note:
+The bbox from Label Maker is organized as [xmin, ymin, xmax, ymax],
+but the bbox predection from TensorFlow Object Detection is [ymin, xmin, ymax, xmax], and it was
+normalized by the image size. For more, see:
+https://www.tensorflow.org/versions/r0.12/api_docs/python/image/working_with_bounding_boxes
+
+Usage:
+           python tf_iou.py   --model_name=building_od_ssd  \
+                                      --path_to_label=data/building_od.pbtxt\
+                                      --test_image_path=test_test
+"""
+
+import os
+from os import makedirs, path as op
+import sys
+import glob
+import six.moves.urllib as urllib
+import tensorflow as tf
+import tarfile
+import pandas as pd
+
+import numpy as np
+from collections import defaultdict
+from PIL import Image
+
+sys.path.append("..")
+
+from utils import label_map_util
+
+flags = tf.app.flags
+flags.DEFINE_string('model_name', '', 'Path to frozen detection graph')
+flags.DEFINE_string('path_to_label', '', 'Path to label file')
+flags.DEFINE_string('test_image_path', '', 'Path to test imgs and output diractory')
+FLAGS = flags.FLAGS
+
+def load_image_into_numpy_array(image):
+    (im_width, im_height) = image.size
+    return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)
+
+def bb_IOU(boxA, boxB):
+    xA = max(boxA[0], boxB[0])
+    yA = max(boxA[1], boxB[1])
+    xB = min(boxA[2], boxB[2])
+    yB = min(boxA[3], boxB[3])
+    # compute the area of intersection rectangle
+    interArea = max(0,(xB - xA + 1)) * max(0,(yB - yA + 1))
+    # compute the area of both the prediction and ground-truthrectangles
+    boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
+    boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
+    iou = interArea / float(boxAArea + boxBArea - interArea)
+    return iou
+
+def pred_bbox():
+    pred_bboxes = list()
+    with detection_graph.as_default():
+        with tf.Session(graph=detection_graph) as sess:
+            # Definite input and output Tensors for detection_graph
+            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
+            # Each box represents a part of the image where a particular object was detected.
+            detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
+            # Each score represent how level of confidence for each of the objects.
+            # Score is shown on the result image, together with the class label.
+            detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
+            detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
+            num_detections = detection_graph.get_tensor_by_name('num_detections:0')
+    for image_path in test_imgs:
+        image = Image.open(image_path)
+        image_np = load_image_into_numpy_array(image)
+        # the array based representation of the image will be used later in order to prepare the
+        # result image with boxes and labels on it.
+        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
+        image_np_expanded = np.expand_dims(image_np, axis=0)
+        # Actual detection.
+        (boxes, scores, classes, num) = sess.run(
+          [detection_boxes, detection_scores, detection_classes, num_detections],
+          feed_dict={image_tensor: image_np_expanded})
+          ### 256 here is the image size from Label Maker, ajust it according to your input image size.
+        bboxe = (boxes*256).astype(np.int)
+        bboxe = np.squeeze(bboxe)
+        score = np.squeeze(((scores*100).transpose()).astype(np.int))
+        ### only keep the bbox that prediction score is higher than 50.
+        bboxes = bboxe[score > 50]
+        if bboxes.any():
+            bboxes_ls = bboxes.tolist()
+            for bbox in bboxes_ls:
+                # pred_bboxes.append([image_path[-18:],bbox])
+                pred_bboxes.append(bbox)
+    return pred_bboxes
+
+def gr_bbox():
+    gr_data = np.load("labels.npz")
+    tile_names = [tile for tile in gr_data.files]
+    tiles = np.array(tile_names)
+
+    gr_bboxes = list()
+    for tile in tiles:
+        bboxes = gr_data[tile].tolist()
+        bbox_info = list()
+        if bboxes:
+            for bbox in bboxes:
+                bbox = [max(0, min(255, x)) for x in bbox[:4]]
+                # switching bbox from [xmin, ymin, xmax, ymax] to [ymin, xmin, ymax, xmax]
+                bbox = [bbox[1], bbox[0], bbox[3], bbox[2]]
+            gr_bboxes.append(bbox)
+    return gr_bboxes
+
+def get_iou():
+    pred_bboxes = pred_bbox()
+    gr_bboxes = gr_bbox()
+    iou_out = list()
+    for pred_box in pred_bboxes:
+        for gr_box in gr_bboxes:
+            try:
+                iou = bb_IOU(pred_box, gr_box)
+                ### If iou is large than 0.5, we assume this prediction is acceptable.
+                if iou >=0.5:
+                    iou_out.append(iou)
+            except:
+                pass
+    return iou_out
+
+if __name__ =='__main__':
+    model_name = op.join(os.getcwd(), FLAGS.model_name)
+    # Path to frozen detection graph.
+    path_to_ckpt = op.join(model_name,  'frozen_inference_graph.pb')
+    # Path to the label file
+    path_to_label = op.join(os.getcwd(), FLAGS.path_to_label)
+    #only train on buildings
+    num_classes = 1
+    #Directory to test images path
+    test_image_path = op.join(os.getcwd(), FLAGS.test_image_path)
+    test_imgs = glob.glob(test_image_path + "/*.jpg")
+
+    detection_graph = tf.Graph()
+    with detection_graph.as_default():
+        od_graph_def = tf.GraphDef()
+        with tf.gfile.GFile(path_to_ckpt, 'rb') as fid:
+            serialized_graph = fid.read()
+            od_graph_def.ParseFromString(serialized_graph)
+            tf.import_graph_def(od_graph_def, name='')
+
+    label_map = label_map_util.load_labelmap(path_to_label)
+    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=num_classes, use_display_name=True)
+    category_index = label_map_util.create_category_index(categories)
+    iou_out = get_iou()
+    pred_bboxes = pred_bbox()
+    print("The IOU for your object detection is: {}".format(float(len(iou_out)/len(pred_bboxes))))