Add benchmark app and update README

Author: rwightman

README.md

@@ -15,19 +15,23 @@ A conda environment with these packages should suffice: `conda install tensorflo
 
 ### Usage
 
-There are two demo apps to try the PoseNet model. They are very basic and could be made more performant.
+There are three demo apps in the root that utilize the PoseNet model. They are very basic and could definitely be improved.
 
-The first time these apps are run (or the library is used) model weights will be downloaded for the TFJS version and converted on the fly.
+The first time these apps are run (or the library is used) model weights will be downloaded from the TensorFlow.js version and converted on the fly.
 
-For both demos, the model can be specified by using its ordinal id (0-3) or integer depth multiplier (50, 75, 100, 101). The default is the 101 model.
+For all demos, the model can be specified with the '--model` argument by using its ordinal id (0-3) or integer depth multiplier (50, 75, 100, 101). The default is the 101 model.
 
 #### image_demo.py 
 
 Image demo runs inference on an input folder of images and outputs those images with the keypoints and skeleton overlayed.
 
 `python image_demo.py --model 101 --image_dir ./images --output_dir ./output`
 
-A folder of suitable test images can be downloaded by first running the get_test_images.py script.
+A folder of suitable test images can be downloaded by first running the `get_test_images.py` script.
+
+#### benchmark.py
+
+A minimal performance benchmark based on image_demo. Images in `--image_dir` are pre-loaded and inference is run `--num_images` times with no drawing and no text output.
 
 #### webcam_demo.py
 

benchmark.py

@@ -0,0 +1,52 @@
+import tensorflow as tf
+import time
+import argparse
+import os
+
+import posenet
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model', type=int, default=101)
+parser.add_argument('--image_dir', type=str, default='./images')
+parser.add_argument('--num_images', type=int, default=1000)
+args = parser.parse_args()
+
+
+def main():
+
+    with tf.Session() as sess:
+        model_cfg, model_outputs = posenet.load_model(args.model, sess)
+        height = model_cfg['height']
+        width = model_cfg['width']
+        output_stride = model_cfg['output_stride']
+        num_images = args.num_images
+
+        filenames = [
+            f.path for f in os.scandir(args.image_dir) if f.is_file() and f.path.endswith(('.png', '.jpg'))]
+        if len(filenames) > num_images:
+            filenames = filenames[:num_images]
+
+        images = {f: posenet.read_imgfile(f, width, height)[0] for f in filenames}
+
+        start = time.time()
+        for i in range(num_images):
+            heatmaps_result, offsets_result, displacement_fwd_result, displacement_bwd_result = sess.run(
+                model_outputs,
+                feed_dict={'image:0': images[filenames[i % len(filenames)]]}
+            )
+
+            output = posenet.decode_multiple_poses(
+                heatmaps_result.squeeze(axis=0),
+                offsets_result.squeeze(axis=0),
+                displacement_fwd_result.squeeze(axis=0),
+                displacement_bwd_result.squeeze(axis=0),
+                output_stride=output_stride,
+                max_pose_detections=10,
+                min_pose_score=0.25)
+
+        print('Average FPS:', num_images / (time.time() - start))
+
+
+if __name__ == "__main__":
+    main()

get_test_images.py

@@ -42,6 +42,7 @@ def main():
 
     for f in TEST_IMAGES:
         url = os.path.join(GOOGLE_CLOUD_IMAGE_BUCKET, f)
+        print('Downloading %s' % f)
         urllib.request.urlretrieve(url, os.path.join(args.image_dir, f))