animal tracking added

animalDetection/animal_tracking.py

+import cv2
+import datetime
+import imutils
+import numpy as np
+from centroidtracker import CentroidTracker
+
+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=80, maxDistance=90)
+
+
+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('sample_dog.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)
+        animal_detections = detector.forward()
+        rects = []
+        for i in np.arange(0, animal_detections.shape[2]):
+            confidence = animal_detections[0, 0, i, 2]
+            if confidence > 0.5:
+                idx = int(animal_detections[0, 0, i, 1])
+
+                if CLASSES[idx] != "dog":
+                    continue
+
+                person_box = animal_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)
+
+        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)
+
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
+            text = "ID: {}".format(objectId)
+            cv2.putText(frame, text, (x1, y1-5), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
+
+        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)
+
+        if int(objectId):
+            print("Warning")
+
+        cv2.imshow("Application", frame)
+        key = cv2.waitKey(1)
+        if key == ord('q'):
+            break
+
+    cv2.destroyAllWindows()
+
+
+main()