Added classify-video example (#10)

Author: luisomoreau

README.md

@@ -56,8 +56,9 @@ Then you can start classifying realtime sensor data. We have examples for:
 
 * [Audio](https://github.com/edgeimpulse/linux-sdk-python/blob/master/examples/audio/classify.py) - grabs data from the microphone and classifies it in realtime.
 * [Camera](https://github.com/edgeimpulse/linux-sdk-python/blob/master/examples/image/classify.py) - grabs data from a webcam and classifies it in realtime.
-* [Camera (full frame)](https://github.com/edgeimpulse/linux-sdk-python/blob/master/examples/image/classify.py) - grabs data from a webcam and classifies it twice (once cut from the left, once cut from the right). This is useful if you have a wide-angle lense and don't want to miss any events.
+* [Camera (full frame)](https://github.com/edgeimpulse/linux-sdk-python/blob/master/examples/image/classify-full-frame.py) - grabs data from a webcam and classifies it twice (once cut from the left, once cut from the right). This is useful if you have a wide-angle lense and don't want to miss any events.
 * [Still image](https://github.com/edgeimpulse/linux-sdk-python/blob/master/examples/image/classify-image.py) - classifies a still image from your hard drive.
+* [Video](https://github.com/edgeimpulse/linux-sdk-python/blob/master/examples/image/classify-video.py) - grabs frames from a video source from your hard drive and classifies it.
 * [Custom data](https://github.com/edgeimpulse/linux-sdk-python/blob/master/examples/custom/classify.py) - classifies custom sensor data.
 
 ## Troubleshooting

examples/image/classify-video.py

@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+
+import device_patches       # Device specific patches for Jetson Nano (needs to be before importing cv2)
+
+import cv2
+import os
+import time
+import sys, getopt
+import numpy as np
+from edge_impulse_linux.image import ImageImpulseRunner
+
+runner = None
+# if you don't want to see a video preview, set this to False
+show_camera = True
+if (sys.platform == 'linux' and not os.environ.get('DISPLAY')):
+    show_camera = False
+
+
+def help():
+    print('python classify-video.py <path_to_model.eim> <path_to_video.mp4>')
+
+def main(argv):
+    try:
+        opts, args = getopt.getopt(argv, "h", ["--help"])
+    except getopt.GetoptError:
+        help()
+        sys.exit(2)
+
+    for opt, arg in opts:
+        if opt in ('-h', '--help'):
+            help()
+            sys.exit()
+
+    if len(args) != 2:
+        help()
+        sys.exit(2)
+
+    model = args[0]
+
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    modelfile = os.path.join(dir_path, model)
+
+    print('MODEL: ' + modelfile)
+
+    with ImageImpulseRunner(modelfile) as runner:
+        try:
+            model_info = runner.init()
+            print('Loaded runner for "' + model_info['project']['owner'] + ' / ' + model_info['project']['name'] + '"')
+            labels = model_info['model_parameters']['labels']
+
+            vidcap = cv2.VideoCapture(args[1])
+            sec = 0
+            start_time = time.time()
+
+            def getFrame(sec):
+                vidcap.set(cv2.CAP_PROP_POS_MSEC,sec*1000)
+                hasFrames,image = vidcap.read()
+                if hasFrames:
+                    return image
+                else: 
+                    print('Failed to load frame', args[1])
+                    exit(1)
+
+
+            img = getFrame(sec)
+
+            while img.size != 0:
+            
+                # imread returns images in BGR format, so we need to convert to RGB
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+                # get_features_from_image also takes a crop direction arguments in case you don't have square images
+                features, cropped = runner.get_features_from_image(img)
+
+                # the image will be resized and cropped, save a copy of the picture here
+                # so you can see what's being passed into the classifier
+                cv2.imwrite('debug.jpg', cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR))
+
+                res = runner.classify(features)
+
+                if "classification" in res["result"].keys():
+                    print('Result (%d ms.) ' % (res['timing']['dsp'] + res['timing']['classification']), end='')
+                    for label in labels:
+                        score = res['result']['classification'][label]
+                        print('%s: %.2f\t' % (label, score), end='')
+                    print('', flush=True)
+
+                elif "bounding_boxes" in res["result"].keys():
+                    print('Found %d bounding boxes (%d ms.)' % (len(res["result"]["bounding_boxes"]), res['timing']['dsp'] + res['timing']['classification']))
+                    for bb in res["result"]["bounding_boxes"]:
+                        print('\t%s (%.2f): x=%d y=%d w=%d h=%d' % (bb['label'], bb['value'], bb['x'], bb['y'], bb['width'], bb['height']))
+                        img = cv2.rectangle(cropped, (bb['x'], bb['y']), (bb['x'] + bb['width'], bb['y'] + bb['height']), (255, 0, 0), 1)
+
+                if (show_camera):
+                    cv2.imshow('edgeimpulse', cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR))
+                    if cv2.waitKey(1) == ord('q'):
+                        break
+
+                sec = time.time() - start_time
+                sec = round(sec, 2)
+                print("Getting frame at: %.2f sec" % sec)
+                img = getFrame(sec)
+        finally:
+            if (runner):
+                runner.stop()
+
+if __name__ == "__main__":
+   main(sys.argv[1:])