Upload files to 'camera'

camera/image_presence.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python
+
+# import the necessary packages
+from __future__ import print_function
+from imutils.object_detection import non_max_suppression
+from imutils import paths
+import numpy as np
+import argparse
+import imutils
+import cv2
+
+# construct the argument parse and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--images", required=True, help="path to images directory")
+args = vars(ap.parse_args())
+
+# initialize the HOG descriptor/person detector
+hog = cv2.HOGDescriptor()
+hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+
+# loop over the image paths
+for imagePath in paths.list_images(args["images"]):
+    # load the image and resize it to (1) reduce detection time
+    # and (2) improve detection accuracy
+    image = cv2.imread(imagePath)
+    image = imutils.resize(image, width=min(400, image.shape[1]))
+    orig = image.copy()
+
+    # detect people in the image
+    (rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
+    padding=(8, 8), scale=1.05)
+
+    # draw the original bounding boxes
+    for (x, y, w, h) in rects:
+        cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)
+
+    # apply non-maxima suppression to the bounding boxes using a
+    # fairly large overlap threshold to try to maintain overlapping
+    # boxes that are still people
+    rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
+    pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)
+
+    # draw the final bounding boxes
+    for (xA, yA, xB, yB) in pick:
+        cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
+
+    # show some information on the number of bounding boxes
+    filename = imagePath[imagePath.rfind("/") + 1:]
+    print("[INFO] {}: {} original boxes, {} after suppression".format(filename, len(rects), len(pick)))
+
+    # show the output images
+    #cv2.imshow("Before NMS", orig)
+    #cv2.imshow("After NMS", image)
+    #cv2.waitKey(0)

camera/video_presence.py

@@ -1,3 +1,5 @@
+#!/usr/bin/env python
+
 from imutils.video import VideoStream
 from imutils.video import FPS
 import argparse
@@ -58,8 +60,10 @@ differ = None
 now = ''
 framecounter = 0
 trackeron = 0
+people_count_total = 0
 
 while True:
+    people_count_per_frame = 0
     frame = vs.read()
     frame = frame if args.get("video", None) is None else frame[1]
     # if the frame can not be grabbed, then we have reached the end of the video
@@ -87,15 +91,19 @@ while True:
 
         # 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
 
@@ -103,8 +111,8 @@ while True:
             (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"]
@@ -120,22 +128,28 @@ while True:
             for i in np.arange(0, detections.shape[2]):
                 confidence = detections[0, 0, i, 2]
 
-                confidence_level = 0.7 
+                confidence_level = 0.8
 
                 if confidence > confidence_level:
+                    people_count_per_frame+=1
+                    people_count_total+=1
                     # extract the index of the class label from the `detections`, then compute the (x, y)-coordinates of
                     # the bounding box for the object
                     idx = int(detections[0, 0, i, 1])
                     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
@@ -146,7 +160,7 @@ while True:
 
 
     # check to see if we are currently tracking an object, if so, ignore other boxes
-    # this code is relevant if we want to identify particular persons (section 2 of this tutorial)
+    # this code is relevant if we want to identify particular persons 
     if initBB2 is not None:
 
         # grab the new bounding box coordinates of the object
@@ -173,6 +187,8 @@ while True:
         info = [
             ("Success", "Yes" if success else "No"),
             ("FPS", "{:.2f}".format(fps.fps())),
+            ("People Frame", "{}".format(people_count_per_frame)),
+            ("People Total", "{}".format(people_count_total))
         ]
 
         # loop over the info tuples and draw them on our frame
@@ -183,8 +199,7 @@ while True:
         # 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)