Hello world

.gitignore

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+# Tensorflow checkpoints
+*.ckpt
+snapshot-*
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/

Evaluation-Tools/AnalyzeVideos.py

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+"""
+DeepLabCut Toolbox
+https://github.com/AlexEMG/DeepLabCut
+
+A Mathis, [email protected]
+M Mathis, [email protected]
+
+This script analyzes videos based on a trained network.
+You need tensorflow for evaluation. Run by:
+CUDA_VISIBLE_DEVICES=0 python3 AnalyzeVideos.py
+
+"""
+
+####################################################
+# Dependencies
+####################################################
+
+import os.path
+import sys
+
+subfolder = os.getcwd().split('Evaluation-Tools')[0]
+sys.path.append(subfolder)
+# add parent directory: (where nnet & config are!)
+sys.path.append(subfolder + "pose-tensorflow/")
+sys.path.append(subfolder + "Generating_a_Training_Set")
+
+from myconfig_analysis import videofolder, cropping, Task, date, \
+    trainingsFraction, resnet, snapshotindex, shuffle,x1, x2, y1, y2
+
+# Deep-cut dependencies
+from config import load_config
+from nnet import predict
+from dataset.pose_dataset import data_to_input
+
+# Dependencies for video:
+import pickle
+# import matplotlib.pyplot as plt
+import imageio
+imageio.plugins.ffmpeg.download()
+from skimage.util import img_as_ubyte
+from moviepy.editor import VideoFileClip
+import skimage
+import skimage.color
+import time
+import pandas as pd
+import numpy as np
+import os
+from tqdm import tqdm
+
+
+def getpose(image, cfg, outputs, outall=False):
+    ''' Adapted from DeeperCut, see pose-tensorflow folder'''
+    image_batch = data_to_input(skimage.color.gray2rgb(image))
+    outputs_np = sess.run(outputs, feed_dict={inputs: image_batch})
+    scmap, locref = predict.extract_cnn_output(outputs_np, cfg)
+    pose = predict.argmax_pose_predict(scmap, locref, cfg.stride)
+    if outall:
+        return scmap, locref, pose
+    else:
+        return pose
+
+
+####################################################
+# Loading data, and defining model folder
+####################################################
+
+basefolder = '../pose-tensorflow/models/'  # for cfg file & ckpt!
+modelfolder = (basefolder + Task + str(date) + '-trainset' +
+               str(int(trainingsFraction * 100)) + 'shuffle' + str(shuffle))
+cfg = load_config(modelfolder + '/test/' + "pose_cfg.yaml")
+
+##################################################
+# Load and setup CNN part detector
+##################################################
+
+# Check which snap shots are available and sort them by # iterations
+Snapshots = np.array([
+    fn.split('.')[0]
+    for fn in os.listdir(modelfolder + '/train/')
+    if "index" in fn
+])
+increasing_indices = np.argsort([int(m.split('-')[1]) for m in Snapshots])
+Snapshots = Snapshots[increasing_indices]
+
+print(modelfolder)
+print(Snapshots)
+
+##################################################
+# Compute predictions over images
+##################################################
+
+# Check if data already was generated:
+cfg['init_weights'] = modelfolder + '/train/' + Snapshots[snapshotindex]
+
+# Name for scorer:
+trainingsiterations = (cfg['init_weights'].split('/')[-1]).split('-')[-1]
+
+# Name for scorer:
+scorer = 'DeepCut' + "_resnet" + str(resnet) + "_" + Task + str(
+    date) + 'shuffle' + str(shuffle) + '_' + str(trainingsiterations)
+cfg['init_weights'] = modelfolder + '/train/' + Snapshots[snapshotindex]
+sess, inputs, outputs = predict.setup_pose_prediction(cfg)
+pdindex = pd.MultiIndex.from_product(
+    [[scorer], cfg['all_joints_names'], ['x', 'y', 'likelihood']],
+    names=['scorer', 'bodyparts', 'coords'])
+
+##################################################
+# Datafolder
+##################################################
+
+# videofolder='../videos/' #where your folder with videos is.
+
+os.chdir(videofolder)
+
+videos = np.sort([fn for fn in os.listdir(os.curdir) if (".avi" in fn)])
+print("Starting ", videofolder, videos)
+for video in videos:
+    dataname = video.split('.')[0] + scorer + '.h5'
+    try:
+        # Attempt to load data...
+        pd.read_hdf(dataname)
+        print("Video already analyzed!", dataname)
+    except:
+        print("Loading ", video)
+        clip = VideoFileClip(video)
+        ny, nx = clip.size  # dimensions of frame (height, width)
+        fps = clip.fps
+        nframes = np.sum(1 for j in clip.iter_frames())
+
+        if cropping:
+            clip = clip.crop(
+                y1=y1, y2=y2, x1=x1, x2=x2)  # one might want to adjust
+
+        print("Duration of video [s]: ", clip.duration, ", recorded with ", fps,
+              "fps!")
+        print("Overall # of frames: ", nframes,
+              "with cropped frame dimensions: ", clip.size)
+
+        start = time.time()
+        PredicteData = np.zeros((nframes, 3 * len(cfg['all_joints_names'])))
+
+        print("Starting to extract posture")
+        for index in tqdm(range(nframes)):
+            image = img_as_ubyte(clip.get_frame(index * 1. / fps))
+            pose = getpose(image, cfg, outputs)
+            PredicteData[index, :] = pose.flatten(
+            )  # NOTE: thereby cfg['all_joints_names'] should be same order as bodyparts!
+
+        stop = time.time()
+
+        dictionary = {
+            "start": start,
+            "stop": stop,
+            "run_duration": stop - start,
+            "Scorer": scorer,
+            "config file": cfg,
+            "fps": fps,
+            "frame_dimensions": (ny, nx),
+            "nframes": nframes
+        }
+        metadata = {'data': dictionary}
+
+        print("Saving results...")
+        DataMachine = pd.DataFrame(
+            PredicteData, columns=pdindex, index=range(nframes))
+        DataMachine.to_hdf(
+            dataname, 'df_with_missing', format='table', mode='w')
+        with open(dataname.split('.')[0] + 'includingmetadata.pickle',
+                  'wb') as f:
+            pickle.dump(metadata, f, pickle.HIGHEST_PROTOCOL)