Merge pull request #21 from edgeimpulse/add-visual-ad

Add visual anomaly grid to image classification results

Author: AIWintermuteAI

examples/image/classify-full-frame.py

@@ -119,6 +119,11 @@ def print_classification(res, tag):
                         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']))
 
+                    if "visual_anomaly_grid" in res["result"].keys():
+                        print('Found %d visual anomalies (%d ms.)' % (len(res["result"]["visual_anomaly_grid"]), res['timing']['dsp'] + res['timing']['classification']))
+                        for grid_cell in res["result"]["visual_anomaly_grid"]:
+                            print('\t%s (%.2f): x=%d y=%d w=%d h=%d' % (grid_cell['label'], grid_cell['value'], grid_cell['x'], grid_cell['y'], grid_cell['width'], grid_cell['height']))
+
                 print_classification(res_l, 'LEFT')
                 print_classification(res_r, 'RIGHT')
 

examples/image/classify-image.py

@@ -68,6 +68,12 @@ def main(argv):
                     print('\t%s (%.2f): x=%d y=%d w=%d h=%d' % (bb['label'], bb['value'], bb['x'], bb['y'], bb['width'], bb['height']))
                     cropped = cv2.rectangle(cropped, (bb['x'], bb['y']), (bb['x'] + bb['width'], bb['y'] + bb['height']), (255, 0, 0), 1)
 
+            if "visual_anomaly_grid" in res["result"].keys():
+                print('Found %d visual anomalies (%d ms.)' % (len(res["result"]["visual_anomaly_grid"]), res['timing']['dsp'] + res['timing']['classification']))
+                for grid_cell in res["result"]["visual_anomaly_grid"]:
+                    print('\t%s (%.2f): x=%d y=%d w=%d h=%d' % (grid_cell['label'], grid_cell['value'], grid_cell['x'], grid_cell['y'], grid_cell['width'], grid_cell['height']))
+                    cropped = cv2.rectangle(cropped, (grid_cell['x'], grid_cell['y']), (grid_cell['x'] + grid_cell['width'], grid_cell['y'] + grid_cell['height']), (255, 125, 0), 1)
+
             # 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))

examples/image/classify-video.py

@@ -91,6 +91,12 @@ def getFrame(sec):
                         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 "visual_anomaly_grid" in res["result"].keys():
+                    print('Found %d visual anomalies (%d ms.)' % (len(res["result"]["visual_anomaly_grid"]), res['timing']['dsp'] + res['timing']['classification']))
+                    for grid_cell in res["result"]["visual_anomaly_grid"]:
+                        print('\t%s (%.2f): x=%d y=%d w=%d h=%d' % (grid_cell['label'], grid_cell['value'], grid_cell['x'], grid_cell['y'], grid_cell['width'], grid_cell['height']))
+                        img = cv2.rectangle(cropped, (grid_cell['x'], grid_cell['y']), (grid_cell['x'] + grid_cell['width'], grid_cell['y'] + grid_cell['height']), (255, 125, 0), 1)
+
                 if (show_camera):
                     cv2.imshow('edgeimpulse', cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR))
                     if cv2.waitKey(1) == ord('q'):