Fix video, rename image, add images to gitignore

.gitignore

 *.pyc
 camera/venv
+camera/images
+camera/videos

camera/.vscode/launch.json

+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Current File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        },
+
+        {
+            "name": "Python: Current File with args",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "args": ["-v", "~/Videos/video.h264"]
+        }
+    ]
+}

camera/.vscode/settings.json

+{
+    "python.pythonPath": "venv/bin/python"
+}

camera/person-detection.py → camera/image_presence.py

+#!/usr/bin/env python
+
 # import the necessary packages
 from __future__ import print_function
 from imutils.object_detection import non_max_suppression

camera/video_presence.py

+#!/usr/bin/env python
+
 from imutils.video import VideoStream
 from imutils.video import FPS
 import argparse
 
         # dilate the thresholded image to fill in holes, then find contours on thresholded image
         thresh = cv2.dilate(thresh, None, iterations=2)
-        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
-        cnts = cnts[0] if imutils.is_cv2() else cnts[1]
+        thresh = np.uint8(thresh)
+        cnts, im2  = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
+        #cnts = cnts if imutils.is_cv2() else im2
+        #print(len(cnts))
+        #if len(cnts) > 1:
+        #cnts = cnts[0] if imutils.is_cv2() else cnts[1]
 
         # loop over the contours identified
         contourcount = 0
         for c in cnts:
             contourcount =  contourcount + 1
 
-           # if the contour is too small, ignore it
+            # if the contour is too small, ignore it
             if cv2.contourArea(c) < args["min_area"]:
                 continue
 
             (x, y, w, h) = cv2.boundingRect(c)
             initBB2 =(x,y,w,h)
 
-            prott1 = r'ML-Models\MobileNetSSD_deploy.prototxt'
-            prott2 = r'ML-Models\MobileNetSSD_deploy.caffemodel'
+            prott1 = r'ML-Models/MobileNetSSD_deploy.prototxt'
+            prott2 = r'ML-Models/MobileNetSSD_deploy.caffemodel'
             net = cv2.dnn.readNetFromCaffe(prott1, prott2)
 
             CLASSES = ["person"]
                     box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
                     (startX, startY, endX, endY) = box.astype("int")
                     # draw the prediction on the frame
-                    label = "{}: {:.2f}%".format(CLASSES[idx],
-                                                 confidence * 100)
-                    cv2.rectangle(frame, (startX, startY), (endX, endY),
-                                  COLORS[idx], 2)
+                    
+                    #label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
+                    label = "{}: {:.2f}%".format(CLASSES[0], confidence * 100)
+                    
+                    #cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2)
+                    cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[0], 2)
+                    
                     y = startY - 15 if startY - 15 > 15 else startY + 15
-                    cv2.putText(frame, label, (startX, y),
-                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
+
+                    #cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
+                    cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[0], 2)
 
             cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 0), 2)
             # Start tracker
         # draw the text and timestamp on the frame
         now2 = datetime.now()
         time_passed_seconds = str((now2-now).seconds)
-        cv2.putText(frame, 'Detecting persons',(10, 20),
-                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+        cv2.putText(frame, 'Detecting persons',(10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
 
     # show the frame and record if the user presses a key
     cv2.imshow("Video stream", frame)