Add vidual-debugging to video_pres and video capabilities to couter

camera/counter_people.py

@@ -2,7 +2,6 @@ from imutils.object_detection import non_max_suppression
 import numpy as np
 import imutils
 import cv2
-import requests
 import time
 import argparse
 import time
@@ -25,8 +24,7 @@ def detector(image):
 
     clone = image.copy()
 
-    (rects, weights) = HOGCV.detectMultiScale(image, winStride=(4, 4),
-                                              padding=(8, 8), scale=1.05)
+    (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:
@@ -46,10 +44,9 @@ def buildPayload(variable, value, 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")
+    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")
+    ap.add_argument("-v", "--video", default=None, help="path to the video file")
     args = vars(ap.parse_args())
 
     return args
@@ -78,35 +75,35 @@ def localDetect(image_path):
     return result#(result, image)
 
 
-def cameraDetect(token, device, variable, sample_time=5):
+def cameraDetect(video_path="", 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
+    if video_path:
+        cap = cv2.VideoCapture(video_path)
+    else:
+        cap = cv2.VideoCapture(0)
+    
+    #init = time.time()
 
     while(True):
         # Capture frame-by-frame
-        ret, frame = cap.read()
+        _, frame = cap.read()
+
+        if frame is None:
+            break
+
         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()
+        for (xA, yA, xB, yB) in result:
+            cv2.rectangle(frame, (xA, yA), (xB, yB), (0, 255, 0), 2)
+        cv2.imshow('frame', frame)
+        cv2.waitKey(0)
+
+        #if time.time() - init >= sample_time:
+        if len(result):
+            print("{} people detected.".format(len(result)))
+        #init = time.time()
         
 
         if cv2.waitKey(1) & 0xFF == ord('q'):
@@ -127,28 +124,23 @@ def convert_to_base64(image):
 
 def detectPeople(args):
     image_path = args["image"]
+    video_path = args["video"]
     camera = True if str(args["camera"]) == 'true' else False
 
     # Routine to read local image
-    if image_path != None and not camera:
+    if image_path != None and not camera and video_path == None:
         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"""
+        print(str(len(result)) + " People detected.")
+
+    if video_path != None and not camera:
+        print("[INFO] reading video")
+        cameraDetect(video_path)
 
     # Routine to read images from webcam
     if camera:
         print("[INFO] reading camera images")
-        cameraDetect(TOKEN, DEVICE, VARIABLE)
+        cameraDetect()
 
 
 def main():

camera/video_presence.py

@@ -10,6 +10,8 @@ from datetime import datetime, time
 import numpy as np
 import time as time2
 
+VISUAL_DEBUG=True
+
 """ Arguments """
 ap = argparse.ArgumentParser()
 ap.add_argument("-v", "--video", help="path to the video file")
@@ -20,12 +22,6 @@ 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:
@@ -56,7 +52,6 @@ if args.get("video", None) is None:
 # otherwise, we are reading from a video file
 else:
     vs = cv2.VideoCapture(args["video"])
-    #vs.set(cv2.CAP_PROP_FPS, 2)
 
 """" Analyzing video frames """
 # loop over the frames of the video, and store corresponding information from each frame
@@ -68,13 +63,14 @@ now = ''
 framecounter = 0
 trackeron = 0
 people_count_total = 0
-frame_counter= 0
 
-while True:
-    """frame_counter+=1
-    if framecounter%5 != 0:
-        continue"""
+cv2.namedWindow('Video stream', cv2.WINDOW_NORMAL)
+if VISUAL_DEBUG: 
+    cv2.namedWindow('debug image', cv2.WINDOW_NORMAL)
 
+while True:
+    if VISUAL_DEBUG: 
+        print("Frame {}".format(framecounter))
     people_count_per_frame = 0
     frame = vs.read()
     frame = frame if args.get("video", None) is None else frame[1]
@@ -100,18 +96,26 @@ while True:
         # compute the absolute difference between the current frame and first frame
         frameDelta = cv2.absdiff(firstFrame, gray)
         thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
-
-        # dilate the thresholded image to fill in holes, then find contours on thresholded image
+        
+        #debug
+        if VISUAL_DEBUG: 
+            cv2.imshow("debug image", thresh)
+            cv2.waitKey(0)
+            #cv2.destroyWindow("threshhold image")
+        
+        # dilate the thresholded image to fill in holes
         thresh = cv2.dilate(thresh, None, iterations=2)
+
+        # find contours on thresholded image
         thresh = np.uint8(thresh)
-        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:
-        #cnts = cnts[0] if imutils.is_cv2() else cnts[1]
+        cnts, im2  = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
+        
+        if VISUAL_DEBUG: 
+            """img = cv2.drawContours(thresh.copy(), cnts, -1, (0,255,0), 3)
+            cv2.imshow("debug image", img)
+            cv2.waitKey(0)"""
+        
+            print(len(cnts))
 
         # loop over the contours identified
         contourcount = 0
@@ -136,6 +140,13 @@ while True:
             trackbox = frame[y:y+h, x:x+w]
             trackbox = cv2.resize(trackbox, (224, 224))
             #cv2.imshow('image',trackbox)
+
+            """if VISUAL_DEBUG:
+                trackbox2 = thresh[y:y+h, x:x+w]
+                trackbox2 = cv2.resize(trackbox2, (224, 224))
+                cv2.imshow('debug image',trackbox2)
+                cv2.waitKey(0)"""
+            
             blob = cv2.dnn.blobFromImage(cv2.resize(trackbox, (300, 300)),0.007843, (300, 300), 127.5)
             net.setInput(blob)
             detections = net.forward()
@@ -143,7 +154,7 @@ while True:
             for i in np.arange(0, detections.shape[2]):
                 confidence = detections[0, 0, i, 2]
 
-                confidence_level = 0.8
+                confidence_level = 0.95
 
                 if confidence > confidence_level:
                     people_count_per_frame+=1
@@ -166,6 +177,11 @@ while True:
                     #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)
 
+                    if VISUAL_DEBUG:
+                        print("person found")
+                        cv2.imshow("debug image", frame)
+                        key = cv2.waitKey(0)
+
             cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 0), 2)
             # Start tracker
             now = datetime.now()
@@ -176,7 +192,7 @@ 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 
-    if initBB2 is not None:
+    """if initBB2 is not None:
 
         # grab the new bounding box coordinates of the object
         (success, box) = tracker.update(frame)
@@ -214,7 +230,7 @@ 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)