# This script is used to demonstrate MobileNet-SSD network using OpenCV deep learning module. # # It works with model taken from https://github.com/chuanqi305/MobileNet-SSD/ that # was trained in Caffe-SSD framework, https://github.com/weiliu89/caffe/tree/ssd. # Model detects objects from 20 classes. # # Also TensorFlow model from TensorFlow object detection model zoo may be used to # detect objects from 90 classes: # http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz # Text graph definition must be taken from opencv_extra: # https://github.com/opencv/opencv_extra/tree/master/testdata/dnn/ssd_mobilenet_v1_coco.pbtxt import numpy as np import argparse try: import cv2 as cv except ImportError: raise ImportError('Can\'t find OpenCV Python module. If you\'ve built it from sources without installation, ' 'configure environment variable PYTHONPATH to "opencv_build_dir/lib" directory (with "python3" subdirectory if required)') inWidth = 300 inHeight = 300 WHRatio = inWidth / float(inHeight) inScaleFactor = 0.007843 meanVal = 127.5 if __name__ == "__main__": parser = argparse.ArgumentParser( description='Script to run MobileNet-SSD object detection network ' 'trained either in Caffe or TensorFlow frameworks.') parser.add_argument("--video", help="path to video file. If empty, camera's stream will be used") parser.add_argument("--prototxt", default="MobileNetSSD_deploy.prototxt", help='Path to text network file: ' 'MobileNetSSD_deploy.prototxt for Caffe model or ' 'ssd_mobilenet_v1_coco.pbtxt from opencv_extra for TensorFlow model') parser.add_argument("--weights", default="MobileNetSSD_deploy.caffemodel", help='Path to weights: ' 'MobileNetSSD_deploy.caffemodel for Caffe model or ' 'frozen_inference_graph.pb from TensorFlow.') parser.add_argument("--num_classes", default=20, type=int, help="Number of classes. It's 20 for Caffe model from " "https://github.com/chuanqi305/MobileNet-SSD/ and 90 for " "TensorFlow model from http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz") parser.add_argument("--thr", default=0.2, type=float, help="confidence threshold to filter out weak detections") args = parser.parse_args() if args.num_classes == 20: net = cv.dnn.readNetFromCaffe(args.prototxt, args.weights) swapRB = False classNames = { 0: 'background', 1: 'aeroplane', 2: 'bicycle', 3: 'bird', 4: 'boat', 5: 'bottle', 6: 'bus', 7: 'car', 8: 'cat', 9: 'chair', 10: 'cow', 11: 'diningtable', 12: 'dog', 13: 'horse', 14: 'motorbike', 15: 'person', 16: 'pottedplant', 17: 'sheep', 18: 'sofa', 19: 'train', 20: 'tvmonitor' } else: assert(args.num_classes == 90) net = cv.dnn.readNetFromTensorflow(args.weights, args.prototxt) swapRB = True classNames = { 0: 'background', 1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle', 5: 'airplane', 6: 'bus', 7: 'train', 8: 'truck', 9: 'boat', 10: 'traffic light', 11: 'fire hydrant', 13: 'stop sign', 14: 'parking meter', 15: 'bench', 16: 'bird', 17: 'cat', 18: 'dog', 19: 'horse', 20: 'sheep', 21: 'cow', 22: 'elephant', 23: 'bear', 24: 'zebra', 25: 'giraffe', 27: 'backpack', 28: 'umbrella', 31: 'handbag', 32: 'tie', 33: 'suitcase', 34: 'frisbee', 35: 'skis', 36: 'snowboard', 37: 'sports ball', 38: 'kite', 39: 'baseball bat', 40: 'baseball glove', 41: 'skateboard', 42: 'surfboard', 43: 'tennis racket', 44: 'bottle', 46: 'wine glass', 47: 'cup', 48: 'fork', 49: 'knife', 50: 'spoon', 51: 'bowl', 52: 'banana', 53: 'apple', 54: 'sandwich', 55: 'orange', 56: 'broccoli', 57: 'carrot', 58: 'hot dog', 59: 'pizza', 60: 'donut', 61: 'cake', 62: 'chair', 63: 'couch', 64: 'potted plant', 65: 'bed', 67: 'dining table', 70: 'toilet', 72: 'tv', 73: 'laptop', 74: 'mouse', 75: 'remote', 76: 'keyboard', 77: 'cell phone', 78: 'microwave', 79: 'oven', 80: 'toaster', 81: 'sink', 82: 'refrigerator', 84: 'book', 85: 'clock', 86: 'vase', 87: 'scissors', 88: 'teddy bear', 89: 'hair drier', 90: 'toothbrush' } if args.video: cap = cv.VideoCapture(args.video) else: cap = cv.VideoCapture(0) while True: # Capture frame-by-frame ret, frame = cap.read() blob = cv.dnn.blobFromImage(frame, inScaleFactor, (inWidth, inHeight), (meanVal, meanVal, meanVal), swapRB) net.setInput(blob) detections = net.forward() cols = frame.shape[1] rows = frame.shape[0] if cols / float(rows) > WHRatio: cropSize = (int(rows * WHRatio), rows) else: cropSize = (cols, int(cols / WHRatio)) y1 = int((rows - cropSize[1]) / 2) y2 = y1 + cropSize[1] x1 = int((cols - cropSize[0]) / 2) x2 = x1 + cropSize[0] frame = frame[y1:y2, x1:x2] cols = frame.shape[1] rows = frame.shape[0] for i in range(detections.shape[2]): confidence = detections[0, 0, i, 2] if confidence > args.thr: class_id = int(detections[0, 0, i, 1]) xLeftBottom = int(detections[0, 0, i, 3] * cols) yLeftBottom = int(detections[0, 0, i, 4] * rows) xRightTop = int(detections[0, 0, i, 5] * cols) yRightTop = int(detections[0, 0, i, 6] * rows) cv.rectangle(frame, (xLeftBottom, yLeftBottom), (xRightTop, yRightTop), (0, 255, 0)) if class_id in classNames: label = classNames[class_id] + ": " + str(confidence) labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1) yLeftBottom = max(yLeftBottom, labelSize[1]) cv.rectangle(frame, (xLeftBottom, yLeftBottom - labelSize[1]), (xLeftBottom + labelSize[0], yLeftBottom + baseLine), (255, 255, 255), cv.FILLED) cv.putText(frame, label, (xLeftBottom, yLeftBottom), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0)) cv.imshow("detections", frame) if cv.waitKey(1) >= 0: break