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