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Vehicle_Classification.py

+import cv2
+import numpy as np
+import imutils
+from imutils.video import WebcamVideoStream
+from imutils.video import FPS
+
+
+class ObjectDetection:
+
+    def __init__(self):
+        self.model = "MobileNetSSD_deploy.caffemodel"
+        self.prototxt = "MobileNetSSD_deploy.prototxt.txt"
+        self.net = cv2.dnn.readNetFromCaffe(self.prototxt, self.model)
+
+    def define_classes(self):
+        """Define classes and associates colors randomly"""
+
+        self.classes = ["background", "aeroplane", "bicycle", "bird", "boat",
+                        "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
+                        "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
+                        "sofa", "train", "tvmonitor"]
+        self.colors = np.random.uniform(0, 255, size=(len(self.classes), 3))
+
+    def run_detection(self):
+
+        print("Start object detection ...")
+
+        self.define_classes()
+        vs = WebcamVideoStream(0).start()
+        fps = FPS().start()
+
+        while True:
+
+            frame = vs.read()
+            (h, w) = frame.shape[:-1]
+            blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
+                                         scalefactor=0.007843, size=(300, 300), mean=127.5)
+            self.net.setInput(blob)
+            detections = self.net.forward()
+
+            # Loop over detected objects
+            for i in np.arange(0, detections.shape[2]):
+
+                # Get proba for each object
+                probability = detections[0, 0, i, 2]
+
+                # Set up threshold for filtering detection
+                if probability > 0.5:
+                    # Get prediction index
+                    index = int(detections[0, 0, i, 1])
+
+                    # Get bounding box coordinates
+                    box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+                    (startX, startY, endX, endY) = box.astype("int")
+
+                    label = "{}: {:.2f}%".format(self.classes[index], probability * 100)
+
+                    y = startY - 10 if startY - 10 > 10 else startY + 10
+
+                    cv2.rectangle(frame, (startX, startY), (endX, endY),
+                                  self.colors[index], 2)
+                    cv2.putText(frame, label, (startX, y),
+                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, self.colors[index], 2)
+
+            cv2.imshow('frame', frame)
+            fps.update()
+
+            key = cv2.waitKey(1) & 0xFF
+            if key == ord("q"):
+                cv2.destroyAllWindows()
+                vs.stop()
+                fps.stop()
+                break
+
+        print("Fps: {:.2f}".format(fps.fps()))
+        fps.update()
+
+
+if __name__ == '__main__' :
+    detector = ObjectDetection()
+    detector.run_detection()