add differing methods for opencv ver 3/4

.gitignore

@@ -6,4 +6,5 @@ camera/images
 camera/videos
 *.jpg
 *.h264
-*.mp4
+*.mp4
+*.png

camera/counter_people.py

@@ -0,0 +1,160 @@
+from imutils.object_detection import non_max_suppression
+import numpy as np
+import imutils
+import cv2
+import requests
+import time
+import argparse
+import time
+import base64
+
+'''
+Usage:
+
+python peopleCounter.py -i PATH_TO_IMAGE  # Reads and detect people in a single local stored image
+python peopleCounter.py -c  # Attempts to detect people using webcam
+'''
+
+HOGCV = cv2.HOGDescriptor()
+HOGCV.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+
+def detector(image):
+    '''
+    @image is a numpy array
+    '''
+
+    clone = image.copy()
+
+    (rects, weights) = HOGCV.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(clone, (x, y), (x + w, y + h), (0, 0, 255), 2)
+
+    # Applies non-max supression from imutils package to kick-off overlapped
+    # boxes
+    rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
+    result = non_max_suppression(rects, probs=None, overlapThresh=0.65)
+
+    return result
+
+
+def buildPayload(variable, value, context):
+    return {variable: {"value": value, "context": context}}
+
+
+def argsParser():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("-i", "--image", default=None,
+                    help="path to image test file directory")
+    ap.add_argument("-c", "--camera", default=False,
+                    help="Set as true if you wish to use the camera")
+    args = vars(ap.parse_args())
+
+    return args
+
+
+def localDetect(image_path):
+    result = []
+    image = cv2.imread(image_path)
+    image = imutils.resize(image, width=min(400, image.shape[1]))
+    clone = image.copy()
+    if len(image) <= 0:
+        print("[ERROR] could not read local image")
+        return result
+    print("[INFO] Detecting people")
+    result = detector(image)
+
+    """# shows the result
+    for (xA, yA, xB, yB) in result:
+        cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
+
+    cv2.imshow("result", image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+    cv2.imwrite("result.png", np.hstack((clone, image)))"""
+    return result#(result, image)
+
+
+def cameraDetect(token, device, variable, sample_time=5):
+
+    cap = cv2.VideoCapture(0)
+    init = time.time()
+
+    # Allowed sample time for Ubidots is 1 dot/second
+    if sample_time < 1:
+        sample_time = 1
+
+    while(True):
+        # Capture frame-by-frame
+        ret, frame = cap.read()
+        frame = imutils.resize(frame, width=min(400, frame.shape[1]))
+        result = detector(frame.copy())
+
+        # shows the result
+        #for (xA, yA, xB, yB) in result:
+        #    cv2.rectangle(frame, (xA, yA), (xB, yB), (0, 255, 0), 2)
+        #cv2.imshow('frame', frame)
+
+        # Sends results
+        if time.time() - init >= sample_time:
+            #print("[INFO] Sending actual frame results")
+            # Converts the image to base 64 and adds it to the context
+            #b64 = convert_to_base64(frame)
+            #context = {"image": b64}
+            if len(result):
+                print("{} people detected.".format(len(result)))
+            init = time.time()
+        
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    # When everything done, release the capture
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+def convert_to_base64(image):
+    image = imutils.resize(image, width=400)
+    img_str = cv2.imencode('.png', image)[1].tostring()
+    b64 = base64.b64encode(img_str)
+
+    return b64.decode('utf-8')
+
+
+def detectPeople(args):
+    image_path = args["image"]
+    camera = True if str(args["camera"]) == 'true' else False
+
+    # Routine to read local image
+    if image_path != None and not camera:
+        print("[INFO] Image path provided, attempting to read image")
+        (result, image) = localDetect(image_path)
+        print("[INFO] sending results")
+        # Converts the image to base 64 and adds it to the context
+        b64 = convert_to_base64(image)
+        context = {"image": b64}
+        print(len(result))
+        # Sends the result
+        """req = sendToUbidots(TOKEN, DEVICE, VARIABLE,
+                            len(result), context=context)
+        if req.status_code >= 400:
+            print("[ERROR] Could not send data to Ubidots")
+            return req"""
+
+    # Routine to read images from webcam
+    if camera:
+        print("[INFO] reading camera images")
+        cameraDetect(TOKEN, DEVICE, VARIABLE)
+
+
+def main():
+    args = argsParser()
+    detectPeople(args)
+
+
+if __name__ == '__main__':
+    main()

camera/video_presence.py

@@ -20,6 +20,12 @@ args = vars(ap.parse_args())
 """ Determine opencv version and select tracker """
 # extract the OpenCV version info
 (major, minor) = cv2.__version__.split(".")[:2]
+# different methods of opencv require differing ways to unpack find countours
+if int(major) > 3:
+    OPENCV4=True
+else:
+    OPENCV4=False
+
 # if we are using OpenCV 3.2 or an earlier version, we can use a special factory
 # function to create the entity that tracks objects 
 if int(major) == 3 and int(minor) < 3:
@@ -62,8 +68,13 @@ now = ''
 framecounter = 0
 trackeron = 0
 people_count_total = 0
+frame_counter= 0
 
 while True:
+    """frame_counter+=1
+    if framecounter%5 != 0:
+        continue"""
+
     people_count_per_frame = 0
     frame = vs.read()
     frame = frame if args.get("video", None) is None else frame[1]
@@ -93,7 +104,10 @@ while True:
         # dilate the thresholded image to fill in holes, then find contours on thresholded image
         thresh = cv2.dilate(thresh, None, iterations=2)
         thresh = np.uint8(thresh)
-        _, cnts, im2  = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
+        if OPENCV4:
+            cnts, im2  = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
+        else:
+            _, 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: