dyguay/object-detection-api

1import cv2 as cv2import numpy as np3 4 5classNames = ["background", "person", "bicycle", "car", "motorcycle",6            "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant",7            "unknown", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse",8            "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "unknown", "backpack",9            "umbrella", "unknown", "unknown", "handbag", "tie", "suitcase", "frisbee", "skis",10            "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard",11            "surfboard", "tennis racket", "bottle", "unknown", "wine glass", "cup", "fork", "knife",12            "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog",13            "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "unknown", "dining table",14            "unknown", "unknown", "toilet", "unknown", "tv", "laptop", "mouse", "remote", "keyboard",15            "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "unknown",16            "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush" ] 17 18 19class Detector:20    def __init__(self):21        global cvNet, colors22        cvNet = cv.dnn.readNetFromTensorflow('frozen_inference_graph.pb',23                                             'ssd_mobilenet_v1_coco_2017_11_17.pbtxt')24        np.random.seed(543210)25        colors = np.random.uniform(0,255,size=(len(classNames),3))26 27    def detectObject(self, img):28        cvNet.setInput(cv.dnn.blobFromImage(cv.resize(img, (300, 300)), 0.007843, (300, 300), 130))29        detections = cvNet.forward()30        cols = img.shape[1]31        rows = img.shape[0]32 33        for i in range(detections.shape[2]):34            confidence = detections[0, 0, i, 2]35            if confidence > 0.3:36                class_id = int(detections[0, 0, i, 1])37 38                xLeftBottom = int(detections[0, 0, i, 3] * cols)39                yLeftBottom = int(detections[0, 0, i, 4] * rows)40                xRightTop = int(detections[0, 0, i, 5] * cols)41                yRightTop = int(detections[0, 0, i, 6] * rows)42 43                cv.rectangle(img, (xLeftBottom, yLeftBottom), (xRightTop, yRightTop),colors[class_id], 7)44                if class_id in range(len(classNames)):45                    label = classNames[class_id] + ": " + str(confidence)46                    labelSize, _ = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX,1.5, 4)47                    yLeftBottom = max(yLeftBottom, labelSize[1])48                    cv.putText(img, label, (xLeftBottom, yLeftBottom-30 if yLeftBottom > 60 else yLeftBottom+30),\49                                cv.FONT_HERSHEY_SIMPLEX, 1.5, colors[class_id], 5)50 51        return img