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204 lines
9.2 KiB
Python
204 lines
9.2 KiB
Python
import cv2 as cv
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import argparse
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import sys
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import numpy as np
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backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
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targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD)
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parser = argparse.ArgumentParser(description='Use this script to run object detection deep learning networks using OpenCV.')
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parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
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parser.add_argument('--model', required=True,
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help='Path to a binary file of model contains trained weights. '
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'It could be a file with extensions .caffemodel (Caffe), '
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'.pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet)')
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parser.add_argument('--config',
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help='Path to a text file of model contains network configuration. '
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'It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet)')
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parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'torch', 'darknet'],
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help='Optional name of an origin framework of the model. '
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'Detect it automatically if it does not set.')
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parser.add_argument('--classes', help='Optional path to a text file with names of classes to label detected objects.')
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parser.add_argument('--mean', nargs='+', type=float, default=[0, 0, 0],
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help='Preprocess input image by subtracting mean values. '
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'Mean values should be in BGR order.')
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parser.add_argument('--scale', type=float, default=1.0,
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help='Preprocess input image by multiplying on a scale factor.')
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parser.add_argument('--width', type=int,
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help='Preprocess input image by resizing to a specific width.')
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parser.add_argument('--height', type=int,
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help='Preprocess input image by resizing to a specific height.')
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parser.add_argument('--rgb', action='store_true',
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help='Indicate that model works with RGB input images instead BGR ones.')
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parser.add_argument('--thr', type=float, default=0.5, help='Confidence threshold')
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parser.add_argument('--nms', type=float, default=0.4, help='Non-maximum suppression threshold')
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parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
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help="Choose one of computation backends: "
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"%d: automatically (by default), "
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"%d: Halide language (http://halide-lang.org/), "
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"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"%d: OpenCV implementation" % backends)
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parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
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help='Choose one of target computation devices: '
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'%d: CPU target (by default), '
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'%d: OpenCL, '
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'%d: OpenCL fp16 (half-float precision), '
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'%d: VPU' % targets)
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args = parser.parse_args()
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# Load names of classes
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classes = None
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if args.classes:
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with open(args.classes, 'rt') as f:
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classes = f.read().rstrip('\n').split('\n')
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# Load a network
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net = cv.dnn.readNet(args.model, args.config, args.framework)
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net.setPreferableBackend(args.backend)
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net.setPreferableTarget(args.target)
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confThreshold = args.thr
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nmsThreshold = args.nms
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def getOutputsNames(net):
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layersNames = net.getLayerNames()
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return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
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def postprocess(frame, outs):
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frameHeight = frame.shape[0]
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frameWidth = frame.shape[1]
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def drawPred(classId, conf, left, top, right, bottom):
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# Draw a bounding box.
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cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))
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label = '%.2f' % conf
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# Print a label of class.
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if classes:
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assert(classId < len(classes))
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label = '%s: %s' % (classes[classId], label)
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labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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top = max(top, labelSize[1])
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cv.rectangle(frame, (left, top - labelSize[1]), (left + labelSize[0], top + baseLine), (255, 255, 255), cv.FILLED)
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cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
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layerNames = net.getLayerNames()
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lastLayerId = net.getLayerId(layerNames[-1])
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lastLayer = net.getLayer(lastLayerId)
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classIds = []
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confidences = []
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boxes = []
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if net.getLayer(0).outputNameToIndex('im_info') != -1: # Faster-RCNN or R-FCN
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# Network produces output blob with a shape 1x1xNx7 where N is a number of
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# detections and an every detection is a vector of values
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# [batchId, classId, confidence, left, top, right, bottom]
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for out in outs:
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for detection in out[0, 0]:
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confidence = detection[2]
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if confidence > confThreshold:
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left = int(detection[3])
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top = int(detection[4])
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right = int(detection[5])
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bottom = int(detection[6])
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width = right - left + 1
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height = bottom - top + 1
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classIds.append(int(detection[1]) - 1) # Skip background label
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confidences.append(float(confidence))
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boxes.append([left, top, width, height])
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elif lastLayer.type == 'DetectionOutput':
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# Network produces output blob with a shape 1x1xNx7 where N is a number of
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# detections and an every detection is a vector of values
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# [batchId, classId, confidence, left, top, right, bottom]
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for out in outs:
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for detection in out[0, 0]:
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confidence = detection[2]
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if confidence > confThreshold:
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left = int(detection[3] * frameWidth)
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top = int(detection[4] * frameHeight)
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right = int(detection[5] * frameWidth)
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bottom = int(detection[6] * frameHeight)
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width = right - left + 1
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height = bottom - top + 1
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classIds.append(int(detection[1]) - 1) # Skip background label
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confidences.append(float(confidence))
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boxes.append([left, top, width, height])
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elif lastLayer.type == 'Region':
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# Network produces output blob with a shape NxC where N is a number of
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# detected objects and C is a number of classes + 4 where the first 4
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# numbers are [center_x, center_y, width, height]
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classIds = []
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confidences = []
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boxes = []
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for out in outs:
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for detection in out:
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scores = detection[5:]
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classId = np.argmax(scores)
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confidence = scores[classId]
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if confidence > confThreshold:
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center_x = int(detection[0] * frameWidth)
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center_y = int(detection[1] * frameHeight)
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width = int(detection[2] * frameWidth)
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height = int(detection[3] * frameHeight)
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left = center_x - width / 2
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top = center_y - height / 2
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classIds.append(classId)
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confidences.append(float(confidence))
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boxes.append([left, top, width, height])
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else:
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print('Unknown output layer type: ' + lastLayer.type)
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exit()
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indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
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for i in indices:
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i = i[0]
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box = boxes[i]
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left = box[0]
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top = box[1]
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width = box[2]
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height = box[3]
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drawPred(classIds[i], confidences[i], left, top, left + width, top + height)
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# Process inputs
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winName = 'Deep learning object detection in OpenCV'
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cv.namedWindow(winName, cv.WINDOW_NORMAL)
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def callback(pos):
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global confThreshold
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confThreshold = pos / 100.0
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cv.createTrackbar('Confidence threshold, %', winName, int(confThreshold * 100), 99, callback)
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cap = cv.VideoCapture(args.input if args.input else 0)
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while cv.waitKey(1) < 0:
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hasFrame, frame = cap.read()
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if not hasFrame:
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cv.waitKey()
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break
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frameHeight = frame.shape[0]
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frameWidth = frame.shape[1]
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# Create a 4D blob from a frame.
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inpWidth = args.width if args.width else frameWidth
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inpHeight = args.height if args.height else frameHeight
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blob = cv.dnn.blobFromImage(frame, args.scale, (inpWidth, inpHeight), args.mean, args.rgb, crop=False)
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# Run a model
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net.setInput(blob)
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if net.getLayer(0).outputNameToIndex('im_info') != -1: # Faster-RCNN or R-FCN
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frame = cv.resize(frame, (inpWidth, inpHeight))
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net.setInput(np.array([[inpHeight, inpWidth, 1.6]], dtype=np.float32), 'im_info')
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outs = net.forward(getOutputsNames(net))
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postprocess(frame, outs)
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# Put efficiency information.
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t, _ = net.getPerfProfile()
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label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
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cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
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cv.imshow(winName, frame)
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