我需要从数组中恢复一个数组...我用mongodb shell制作了这句话：
db.users.aggregate([
{$match: {'userQueries.queryId': 'gr591dd6b06149d9.07403049'}},
{$project: {
userQueries: {$filter: {
input: '$userQueries',
as: 'query',
cond: {$eq: ['$$query.queryId', 'gr591dd6b06149d9.07403049']}
}},
_id: 0
}}
])
我正在使用此驱动程序：https://secure.php.net/manual/en/set.mongodb.php
我的文档如下：
{
"_id" : ObjectId("591dd67cc4ef6009ae38e292"),
"userId" : 0,
"email" : "dmance@dmance.org",
"password" : "dmance",
"fullName" : "daniel",
"organization" : "dmance",
"userQueries" : [
{
"queryResultArray" : [
{
"docsArray" : [
{
"hgnc_id" : "HGNC:26970"
}
],
"queryResultId" : 0,
"terms" : [
"COX2",
"COX20"
]
},
{
"docsArray" : [
{
"hgnc_id" : "HGNC:130"
}
],
"queryResultId" : 1,
"terms" : [
"ACT"
]
},
{
"docsArray" : [
{
"hgnc_id" : "HGNC:7422"
}
],
"queryResultId" : 2,
"terms" : [
"COX3"
]
}
],
"queryId" : "gr591dd6b06149d9.07403049",
"queryName" : "Unnamed query"
},
{
"queryResultArray" : [
{
"docsArray" : [
{
"hgnc_id" : "HGNC:26970"
}
],
"queryResultId" : 0,
"terms" : [
"COX2",
"COX20"
]
},
{
"docsArray" : [
{
"hgnc_id" : "HGNC:130"
}
],
"queryResultId" : 2,
"terms" : [
"COX3"
]
}
],
"queryId" : "gr591de08337e901.19728995",
"queryName" : "Unnamed query"
}
]
}
我想只得到与queryId字段匹配的数组。
正如我所说，我是在mongo shield中制作的，但我无法将其翻译成php。
答案 0 :(得分：0)
已经回答了。对不起这就是我解决问题的方法。
import argparse
import cv2
import numpy as np
import scipy.io as sio
import time
frame = None
# returns red and green color LED position
#tracks color on intensity thresholded spots
def colorTracking(thresholdedImage, originalImage):
# LED positions
redLEDPos = (-1,-1)
greenLEDPos = (-1,-1)
#convert color from BGR to HSV
hsvThresholdedImage = cv2.cvtColor(thresholdedImage, cv2.COLOR_BGR2HSV)
#define lower and upper limit of hue range for red color
lowerRedHueRange = cv2.inRange(hsvThresholdedImage, np.array([0, 100, 100]), np.array([10, 255, 255]))
upperRedHueRange = cv2.inRange(hsvThresholdedImage, np.array([160, 100, 100]), np.array([179, 255, 255]))
#Hue Range for Green
lowerGreenHue = np.array([50, 50, 120])
upperGreenHue = np.array([70, 255, 255])
greenMask = cv2.inRange(hsvThresholdedImage, lowerGreenHue, upperGreenHue)
#Calculates the weighted sum of two arrays.
redHueThresholdedImage = cv2.addWeighted(lowerRedHueRange, 1.0, upperRedHueRange, 1.0, 0.0)
#Blurs an image using a Gaussian filter
redHueThresholdedImage = cv2.GaussianBlur(redHueThresholdedImage, (9, 9), 2, 2)
#Blurs an image using a Gaussian filter
greenHueThresholdedImage = cv2.GaussianBlur(greenMask, (9, 9), 2, 2)
#find contours in the red hue image formed after weighted adding of lower and upper ranges of red
redContours = cv2.findContours(redHueThresholdedImage.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
# only proceed if at least one contour was found
if len(redContours) > 0:
# find the largest contour in the mask, then use it to compute the minimum enclosing circle and centroid
maxContour = max(redContours, key=cv2.contourArea)
M = cv2.moments(maxContour)
redLEDPos = int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"])
#find contours in the green hue image formed
greenContours = cv2.findContours(greenHueThresholdedImage.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
# only proceed if at least one contour was found
if len(greenContours) > 0:
# find the largest contour in the mask, then use it to compute the minimum enclosing circle and centroid
maxContour = max(greenContours, key=cv2.contourArea)
M = cv2.moments(maxContour)
greenLEDPos = int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"])
return redLEDPos, greenLEDPos
#main loop
def posTrack():
red_x = []
red_y = []
green_x = []
green_y = []
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",required=True, help="Path to the video file")
ap.add_argument("-pf","--posFileName",required=True, help="Path to the mat file")
args = vars(ap.parse_args())
# load the video,and setup the mouse callback function
cap = cv2.VideoCapture(args["video"])
width = cap.get(3)
height = cap.get(4)
if not cap.isOpened():
print "Fatal Error: Could not open the specified file."
exit(-1)
#cv2.namedWindow("VideoFrame")
# keep looping until the 'q' key is pressed
while True:
#read the vide frame by frame
ret, frame = cap.read()
if frame is None:
break
frameClone = frame.copy()
#display the frame and wait for a keypress
#Median Blur: smoothening the images makes median of all the pixels under kernel area and central element= median value.
frame = cv2.medianBlur(frame,5)
#Intensity threholding the Main Frame
thresholdedFrame = cv2.threshold(frame, 160, 255, cv2.THRESH_BINARY)[-1]
#red and green LED position in Main Video
redLED, greenLED = colorTracking(thresholdedFrame, frame)
red_x.append(redLED[0])
red_y.append(redLED[1])
green_x.append(greenLED[0])
green_y.append(greenLED[1])
key = cv2.waitKey(1) & 0xFF
# if the 'q' key is pressed, break from the loop
if key == ord("q"):
break
# close all open windows
cv2.destroyAllWindows()
return args["posFileName"], red_x, red_y, green_x, green_y, width, height
startTime = time.time()
print startTime
#retrieves red_x, red_y, green_x, green_y
filename, red_x, red_y, green_x, green_y, width, height = posTrack()
print "\n"
print time.time() - startTime
#save the mat file
sio.savemat(filename, mdict={'width':width, 'height': height, 'red_x':red_x, 'red_y':red_y, 'green_x':green_x, 'green_y':green_y})