socialDistFull.py

#socialDistancing.py
 
 
import cv2
import datetime
import imutils
import numpy as np
from centroidtracker import CentroidTracker
from itertools import combinations
import math
 
 
 
protopath = "MobileNetSSD_deploy.prototxt"
modelpath = "MobileNetSSD_deploy.caffemodel"
detector = cv2.dnn.readNetFromCaffe(prototxt=protopath, caffeModel=modelpath)
 
 
 
 
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
          "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
          "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
          "sofa", "train", "tvmonitor"]
 
tracker = CentroidTracker(maxDisappeared=40, maxDistance=50)
 
 
def non_max_suppression_fast(boxes, overlapThresh):
   try:
       if len(boxes) == 0:
           return []
 
       if boxes.dtype.kind == "i":
           boxes = boxes.astype("float")
 
       pick = []
 
       x1 = boxes[:, 0]
       y1 = boxes[:, 1]
       x2 = boxes[:, 2]
       y2 = boxes[:, 3]
 
       area = (x2 - x1 + 1) * (y2 - y1 + 1)
       idxs = np.argsort(y2)
 
       while len(idxs) > 0:
           last = len(idxs) - 1
           i = idxs[last]
           pick.append(i)
 
           xx1 = np.maximum(x1[i], x1[idxs[:last]])
           yy1 = np.maximum(y1[i], y1[idxs[:last]])
           xx2 = np.minimum(x2[i], x2[idxs[:last]])
           yy2 = np.minimum(y2[i], y2[idxs[:last]])
 
           w = np.maximum(0, xx2 - xx1 + 1)
           h = np.maximum(0, yy2 - yy1 + 1)
 
           overlap = (w * h) / area[idxs[:last]]
 
           idxs = np.delete(idxs, np.concatenate(([last],
                                                  np.where(overlap > overlapThresh)[0])))
 
       return boxes[pick].astype("int")
   except Exception as e:
       print("Exception occurred in non_max_suppression : {}".format(e))
 
 
def main():
   cap = cv2.VideoCapture('/home/python/OpenCV/soacialDistanceDetect/video .mp4')
 
   fps_start_time = datetime.datetime.now()
   fps = 0
   total_frames = 0
 
   while True:
       ret, frame = cap.read()
       frame = imutils.resize(frame, width=600)
       total_frames = total_frames + 1
 
       (H, W) = frame.shape[:2]
 
       blob = cv2.dnn.blobFromImage(frame, 0.007843, (W, H), 127.5)
 
       detector.setInput(blob)
       person_detections = detector.forward()
       rects = []
       for i in np.arange(0, person_detections.shape[2]):
           confidence = person_detections[0, 0, i, 2]
           if confidence > 0.5:
               idx = int(person_detections[0, 0, i, 1])
 
               if CLASSES[idx] != "person":
                   continue
 
               person_box = person_detections[0, 0, i, 3:7] * np.array([W, H, W, H])
               (startX, startY, endX, endY) = person_box.astype("int")
               rects.append(person_box)
 
       boundingboxes = np.array(rects)
       boundingboxes = boundingboxes.astype(int)
       rects = non_max_suppression_fast(boundingboxes, 0.3)
       centroid_dict = dict()
       objects = tracker.update(rects)
       for (objectId, bbox) in objects.items():
           x1, y1, x2, y2 = bbox
           x1 = int(x1)
           y1 = int(y1)
           x2 = int(x2)
           y2 = int(y2)
           cX = int((x1 + x2) / 2.0)
           cY = int((y1 + y2) / 2.0)
 
 
           centroid_dict[objectId] = (cX, cY, x1, y1, x2, y2)
 
 
       red_zone_list = []
       for (id1, p1), (id2, p2) in combinations(centroid_dict.items(), 2):
           dx, dy = p1[0] - p2[0], p1[1] - p2[1]
           distance = math.sqrt(dx * dx + dy * dy)
           if distance < 75.0:
               if id1 not in red_zone_list:
                   red_zone_list.append(id1)
               if id2 not in red_zone_list:
                   red_zone_list.append(id2)
 
       for id, box in centroid_dict.items():
           if id in red_zone_list:
               cv2.rectangle(frame, (box[2], box[3]), (box[4], box[5]), (0, 0, 255), 2)
           else:
               cv2.rectangle(frame, (box[2], box[3]), (box[4], box[5]), (0, 255, 0), 2)
 
 
       fps_end_time = datetime.datetime.now()
       time_diff = fps_end_time - fps_start_time
       if time_diff.seconds == 0:
           fps = 0.0
       else:
           fps = (total_frames / time_diff.seconds)
 
       fps_text = "FPS: {:.2f}".format(fps)
 
       cv2.putText(frame, fps_text, (5, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
 
       cv2.imshow("SocialDistancing", frame)
       key = cv2.waitKey(1)
       if key == ord('q'):
           break
 
   cv2.destroyAllWindows()
 
main()