Added headless option for webcam/raspicam

README.md

@@ -1,3 +1,7 @@
+### CHANGES of this repo
+This repo contains a modification for the detection_webcam file.
+In essence it let's you run the python script on a headless raspberry, to avoid issues with the desktop libs and dependencies
+
 # TensorFlow Lite Object Detection on Android and Raspberry Pi
 Train your own TensorFlow Lite object detection models and run them on the Raspberry Pi, Android phones, and other edge devices!
 

TFLite_detection_webcam.py

@@ -15,6 +15,7 @@
 
 # Import packages
 import os
+import uuid
 import argparse
 import cv2
 import numpy as np
@@ -79,10 +80,16 @@ def stop(self):
                     default='1280x720')
 parser.add_argument('--edgetpu', help='Use Coral Edge TPU Accelerator to speed up detection',
                     action='store_true')
+parser.add_argument('--noshow_results', help='Don\'t show result images (only use this if --save_results is enabled)',
+                    action='store_false') 
+parser.add_argument('--save_results', help='Save labeled images and annotation data to a results folder',
+                    action='store_true')                   
 
 args = parser.parse_args()
 
 MODEL_NAME = args.modeldir
+show_results = args.noshow_results # Defaults to True
+save_results = args.save_results # Defaults to False
 GRAPH_NAME = args.graph
 LABELMAP_NAME = args.labels
 min_conf_threshold = float(args.threshold)
@@ -196,6 +203,7 @@ def stop(self):
     scores = interpreter.get_tensor(output_details[scores_idx]['index'])[0] # Confidence of detected objects
 
     # Loop over all detections and draw detection box if confidence is above minimum threshold
+    detections = []
     for i in range(len(scores)):
         if ((scores[i] > min_conf_threshold) and (scores[i] <= 1.0)):
 
@@ -215,22 +223,46 @@ def stop(self):
             label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window
             cv2.rectangle(frame, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in
             cv2.putText(frame, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) # Draw label text
+            detections.append([object_name, scores[i], xmin, ymin, xmax, ymax])
 
     # Draw framerate in corner of frame
     cv2.putText(frame,'FPS: {0:.2f}'.format(frame_rate_calc),(30,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,0),2,cv2.LINE_AA)
 
     # All the results have been drawn on the frame, so it's time to display it.
-    cv2.imshow('Object detector', frame)
+    if show_results:
+        cv2.imshow('Object detector', frame)
+        # Press 'q' to quit
+        if cv2.waitKey(1) == ord('q'):
+            break
+
+    # Save the labeled image to results folder if desired
+    if save_results:
+        if len(detections) > 0:
+            for detection in detections:
+                if detection[0] == 'bird':
+                    # Get filenames and paths
+                    fake_file_name = f'{uuid.uuid4()}'
+                    image_fn = os.path.basename("./results/"+fake_file_name+".jpg")
+                    image_savepath = os.path.join(CWD_PATH,"results",image_fn)
+
+                    base_fn, ext = os.path.splitext(image_fn)
+                    txt_result_fn = base_fn +'.txt'
+                    txt_savepath = os.path.join(CWD_PATH,"results",txt_result_fn)
+
+                    # Save image
+                    cv2.imwrite(image_savepath, frame)
+
+                    # Write results to text file
+                    # (Using format defined by https://github.com/Cartucho/mAP, which will make it easy to calculate mAP)
+                    with open(txt_savepath,'w') as f:
+                        for detection in detections:
+                            f.write('%s %.4f %d %d %d %d\n' % (detection[0], detection[1], detection[2], detection[3], detection[4], detection[5]))
 
     # Calculate framerate
     t2 = cv2.getTickCount()
     time1 = (t2-t1)/freq
     frame_rate_calc= 1/time1
 
-    # Press 'q' to quit
-    if cv2.waitKey(1) == ord('q'):
-        break
-
 # Clean up
 cv2.destroyAllWindows()
 videostream.stop()

some.txt

@@ -0,0 +1 @@
+dfdfd