mirror of
https://github.com/opencv/opencv.git
synced 2024-11-27 12:40:05 +08:00
144 lines
5.1 KiB
Python
144 lines
5.1 KiB
Python
import cv2 as cv
|
|
import argparse
|
|
import numpy as np
|
|
|
|
parser = argparse.ArgumentParser(description=
|
|
'Use this script to run Mask-RCNN object detection and semantic '
|
|
'segmentation network from TensorFlow Object Detection API.')
|
|
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
|
|
parser.add_argument('--model', required=True, help='Path to a .pb file with weights.')
|
|
parser.add_argument('--config', required=True, help='Path to a .pxtxt file contains network configuration.')
|
|
parser.add_argument('--classes', help='Optional path to a text file with names of classes.')
|
|
parser.add_argument('--colors', help='Optional path to a text file with colors for an every class. '
|
|
'An every color is represented with three values from 0 to 255 in BGR channels order.')
|
|
parser.add_argument('--width', type=int, default=800,
|
|
help='Preprocess input image by resizing to a specific width.')
|
|
parser.add_argument('--height', type=int, default=800,
|
|
help='Preprocess input image by resizing to a specific height.')
|
|
parser.add_argument('--thr', type=float, default=0.5, help='Confidence threshold')
|
|
args = parser.parse_args()
|
|
|
|
np.random.seed(324)
|
|
|
|
# Load names of classes
|
|
classes = None
|
|
if args.classes:
|
|
with open(args.classes, 'rt') as f:
|
|
classes = f.read().rstrip('\n').split('\n')
|
|
|
|
# Load colors
|
|
colors = None
|
|
if args.colors:
|
|
with open(args.colors, 'rt') as f:
|
|
colors = [np.array(color.split(' '), np.uint8) for color in f.read().rstrip('\n').split('\n')]
|
|
|
|
legend = None
|
|
def showLegend(classes):
|
|
global legend
|
|
if not classes is None and legend is None:
|
|
blockHeight = 30
|
|
assert(len(classes) == len(colors))
|
|
|
|
legend = np.zeros((blockHeight * len(colors), 200, 3), np.uint8)
|
|
for i in range(len(classes)):
|
|
block = legend[i * blockHeight:(i + 1) * blockHeight]
|
|
block[:,:] = colors[i]
|
|
cv.putText(block, classes[i], (0, blockHeight/2), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
|
|
|
|
cv.namedWindow('Legend', cv.WINDOW_NORMAL)
|
|
cv.imshow('Legend', legend)
|
|
classes = None
|
|
|
|
|
|
def drawBox(frame, classId, conf, left, top, right, bottom):
|
|
# Draw a bounding box.
|
|
cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))
|
|
|
|
label = '%.2f' % conf
|
|
|
|
# Print a label of class.
|
|
if classes:
|
|
assert(classId < len(classes))
|
|
label = '%s: %s' % (classes[classId], label)
|
|
|
|
labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
|
|
top = max(top, labelSize[1])
|
|
cv.rectangle(frame, (left, top - labelSize[1]), (left + labelSize[0], top + baseLine), (255, 255, 255), cv.FILLED)
|
|
cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
|
|
|
|
|
|
# Load a network
|
|
net = cv.dnn.readNet(args.model, args.config)
|
|
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
|
|
|
|
winName = 'Mask-RCNN in OpenCV'
|
|
cv.namedWindow(winName, cv.WINDOW_NORMAL)
|
|
|
|
cap = cv.VideoCapture(args.input if args.input else 0)
|
|
legend = None
|
|
while cv.waitKey(1) < 0:
|
|
hasFrame, frame = cap.read()
|
|
if not hasFrame:
|
|
cv.waitKey()
|
|
break
|
|
|
|
frameH = frame.shape[0]
|
|
frameW = frame.shape[1]
|
|
|
|
# Create a 4D blob from a frame.
|
|
blob = cv.dnn.blobFromImage(frame, size=(args.width, args.height), swapRB=True, crop=False)
|
|
|
|
# Run a model
|
|
net.setInput(blob)
|
|
|
|
boxes, masks = net.forward(['detection_out_final', 'detection_masks'])
|
|
|
|
numClasses = masks.shape[1]
|
|
numDetections = boxes.shape[2]
|
|
|
|
# Draw segmentation
|
|
if not colors:
|
|
# Generate colors
|
|
colors = [np.array([0, 0, 0], np.uint8)]
|
|
for i in range(1, numClasses + 1):
|
|
colors.append((colors[i - 1] + np.random.randint(0, 256, [3], np.uint8)) / 2)
|
|
del colors[0]
|
|
|
|
boxesToDraw = []
|
|
for i in range(numDetections):
|
|
box = boxes[0, 0, i]
|
|
mask = masks[i]
|
|
score = box[2]
|
|
if score > args.thr:
|
|
classId = int(box[1])
|
|
left = int(frameW * box[3])
|
|
top = int(frameH * box[4])
|
|
right = int(frameW * box[5])
|
|
bottom = int(frameH * box[6])
|
|
|
|
left = max(0, min(left, frameW - 1))
|
|
top = max(0, min(top, frameH - 1))
|
|
right = max(0, min(right, frameW - 1))
|
|
bottom = max(0, min(bottom, frameH - 1))
|
|
|
|
boxesToDraw.append([frame, classId, score, left, top, right, bottom])
|
|
|
|
classMask = mask[classId]
|
|
classMask = cv.resize(classMask, (right - left + 1, bottom - top + 1))
|
|
mask = (classMask > 0.5)
|
|
|
|
roi = frame[top:bottom+1, left:right+1][mask]
|
|
frame[top:bottom+1, left:right+1][mask] = (0.7 * colors[classId] + 0.3 * roi).astype(np.uint8)
|
|
|
|
for box in boxesToDraw:
|
|
drawBox(*box)
|
|
|
|
# Put efficiency information.
|
|
t, _ = net.getPerfProfile()
|
|
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
|
|
cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
|
|
|
|
showLegend(classes)
|
|
|
|
cv.imshow(winName, frame)
|