Add full frame example

Author: janjongboom

README.md

@@ -56,6 +56,7 @@ 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.
 * [Still image](https://github.com/edgeimpulse/linux-sdk-python/blob/master/examples/image/classify-image.py) - classifies a still image from your hard drive.
 * [Custom data](https://github.com/edgeimpulse/linux-sdk-python/blob/master/examples/custom/classify.py) - classifies custom sensor data.
 

edge_impulse_linux/image.py

@@ -44,6 +44,13 @@ def __exit__(self, type, value, traceback):
     def classify(self, data):
         return super(ImageImpulseRunner, self).classify(data)
 
+    def get_frames(self, videoDeviceId = 0):
+        self.videoCapture = cv2.VideoCapture(videoDeviceId)
+        while not self.closed:
+            success, img = self.videoCapture.read()
+            if success:
+                yield img
+
     def classifier(self, videoDeviceId = 0):
         self.videoCapture = cv2.VideoCapture(videoDeviceId)
         while not self.closed:
@@ -54,7 +61,7 @@ def classifier(self, videoDeviceId = 0):
                 res = self.classify(features)
                 yield res, cropped
 
-    def get_features_from_image(self, img):
+    def get_features_from_image(self, img, crop_direction_x='center', crop_direction_y='center'):
         features = []
 
         EI_CLASSIFIER_INPUT_WIDTH = self.dim[0]
@@ -74,8 +81,23 @@ def get_features_from_image(self, img):
 
         resized = cv2.resize(img, resize_size, interpolation = cv2.INTER_AREA)
 
-        crop_x = int((resize_size_w - resize_size_h) / 2) if resize_size_w > resize_size_h else 0
-        crop_y = int((resize_size_h - resize_size_w) / 2) if resize_size_h > resize_size_w else 0
+        if (crop_direction_x == 'center'):
+            crop_x = int((resize_size_w - resize_size_h) / 2) if resize_size_w > resize_size_h else 0
+        elif (crop_direction_x == 'left'):
+            crop_x = 0
+        elif (crop_direction_x == 'right'):
+            crop_x = resize_size_w - EI_CLASSIFIER_INPUT_WIDTH
+        else:
+            raise Exception('Invalid value for crop_direction_x, should be center, left or right')
+
+        if (crop_direction_y == 'center'):
+            crop_y = int((resize_size_h - resize_size_w) / 2) if resize_size_h > resize_size_w else 0
+        elif (crop_direction_y == 'top'):
+            crop_y = 0
+        elif (crop_direction_y == 'bottom'):
+            crop_y = resize_size_h - EI_CLASSIFIER_INPUT_HEIGHT
+        else:
+            raise Exception('Invalid value for crop_direction_y, should be center, top or bottom')
 
         crop_region = (crop_x, crop_y, EI_CLASSIFIER_INPUT_WIDTH, EI_CLASSIFIER_INPUT_HEIGHT)
 

examples/image/classify-full-frame.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python
+
+import cv2
+import os
+import sys, getopt
+import signal
+import time
+from edge_impulse_linux.image import ImageImpulseRunner
+
+runner = None
+show_camera = False
+
+def now():
+    return round(time.time() * 1000)
+
+def get_webcams():
+    port_ids = []
+    for port in range(5):
+        print("Looking for a camera in port %s:" %port)
+        camera = cv2.VideoCapture(port)
+        if camera.isOpened():
+            ret = camera.read()[0]
+            if ret:
+                backendName =camera.getBackendName()
+                w = camera.get(3)
+                h = camera.get(4)
+                print("Camera %s (%s x %s) found in port %s " %(backendName,h,w, port))
+                port_ids.append(port)
+            camera.release()
+    return port_ids
+
+def sigint_handler(sig, frame):
+    print('Interrupted')
+    if (runner):
+        runner.stop()
+    sys.exit(0)
+
+signal.signal(signal.SIGINT, sigint_handler)
+
+def help():
+    print('python classify.py <path_to_model.eim> <Camera port ID, only required when more than 1 camera is present>')
+
+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) == 0:
+        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']
+            if len(args)>= 2:
+                videoCaptureDeviceId = int(args[1])
+            else:
+                port_ids = get_webcams()
+                if len(port_ids) == 0:
+                    raise Exception('Cannot find any webcams')
+                if len(args)<= 1 and len(port_ids)> 1:
+                    raise Exception("Multiple cameras found. Add the camera port ID as a second argument to use to this script")
+                videoCaptureDeviceId = int(port_ids[0])
+
+            camera = cv2.VideoCapture(videoCaptureDeviceId)
+            ret = camera.read()[0]
+            if ret:
+                backendName = camera.getBackendName()
+                w = camera.get(3)
+                h = camera.get(4)
+                print("Camera %s (%s x %s) in port %s selected." %(backendName,h,w, videoCaptureDeviceId))
+                camera.release()
+            else:
+                raise Exception("Couldn't initialize selected camera.")
+
+            next_frame = 0 # limit to ~10 fps here
+
+            for img in runner.get_frames(videoCaptureDeviceId):
+                if (next_frame > now()):
+                    time.sleep((next_frame - now()) / 1000)
+
+                # make two cuts from the image, one on the left and one on the right
+                features_l, cropped_l = runner.get_features_from_image(img, 'left')
+                features_r, cropped_r = runner.get_features_from_image(img, 'right')
+
+                # classify both
+                res_l = runner.classify(features_l)
+                res_r = runner.classify(features_r)
+
+                cv2.imwrite('debug_l.jpg', cropped_l)
+                cv2.imwrite('debug_r.jpg', cropped_r)
+
+                def print_classification(res, tag):
+                    if "classification" in res["result"].keys():
+                        print('%s: Result (%d ms.) ' % (tag, 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('%s: Found %d bounding boxes (%d ms.)' % (tag, 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']))
+
+                print_classification(res_l, 'LEFT')
+                print_classification(res_r, 'RIGHT')
+
+                next_frame = now() + 100
+
+        finally:
+            if (runner):
+                runner.stop()
+
+if __name__ == "__main__":
+   main(sys.argv[1:])

examples/image/classify-image.py

@@ -42,6 +42,7 @@ def main(argv):
 
             img = cv2.imread(args[1])
 
+            # 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

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = edge_impulse_linux
-version = 1.0.3
+version = 1.0.4
 author = EdgeImpulse Inc.
 author_email = hello@edgeimpulse.com
 description = Python runner for real-time ML classification