在 python 的字典中存储坐标

Question

From a set of code, I got an output of coordinates like

(448, 258)
(445, 362)
(426, 784)
(441, 496)

I need to store them in a dictionary. I want to iterate through the dictionary's elements after the coordinates stored in it. Also, the coordinates are not constant they may vary from one file to other( the number of coordinates may change and value of 'x' and 'y' also ). So it should not be hardcoded. How can I make it possible using python? I am a noob, please guide me.

If it is not possible the coordinates are obtained from a function giving outputs of x and y. Can we form the coordinate dictionary from this?

Edit:

I expect the output as {(448, 258), (445, 362), (426, 784), (441, 496)} Also, have a doubt if I get the output as I expected can I make a loop through it like for x in dict ?

The code follows

import numpy as np
import argparse
import time
import cv2
import os


ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="path to input image")
ap.add_argument("-y", "--yolo", required=True, help="base path to YOLO directory")
ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections")
ap.add_argument("-t", "--threshold", type=float, default=0.3, help="threshold when applying non-maxima suppression")
args = vars(ap.parse_args())
counts = dict()

labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")

np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8")
weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
configPath = os.path.sep.join([args["yolo"], "yolov3-320.cfg"])
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)

image = cv2.imread(args["image"])
(H, W) = image.shape[:2]
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()
print("[INFO] YOLO took {:.6f} seconds".format(end - start))

boxes = []
confidences = []
classIDs = []

for output in layerOutputs:
    for detection in output:
        scores = detection[5:]
        classID = np.argmax(scores)
        confidence = scores[classID]
        if confidence > args["confidence"]:
            box = detection[0:4] * np.array([W, H, W, H])
            (centerX, centerY, width, height) = box.astype("int")
            x = int(centerX - (width / 2))
            y = int(centerY - (height / 2))
            boxes.append([x, y, int(width), int(height)])
            confidences.append(float(confidence))
            classIDs.append(classID)

idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"])

if len(idxs) > 0:
    for i in idxs.flatten():
        (x, y) = (boxes[i][0], boxes[i][1])
        (w, h) = (boxes[i][2], boxes[i][3])
        color = [int(c) for c in COLORS[classIDs[i]]]
        rectangle = cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
        text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
        cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

        x1= y+h
        y1= x+w
        coordinates = (x1,y1)
        print(coordinates)

I have written the print statement for knowing the coordinates of the boxes that was produced after the object detection.

Answer 1

In order to take one tuple and seperate the element into a dict, this works:

t = (440, 765)
dict = {}
print(dict)
dict[t[0]] = t[1]
print(dict)

And the output is:

{}
{440: 765}

This should work as long as you are only giving it one tuple at a time, and none of the x-coordinates are the same. This could be re-rganized into a function like so:

def make_dict(tuple, dict):
     dict[tuple[0]] = tuple[1]

Answer 2

Ok, I see you are just printing the coordinates out. You need to collect them as you go, so you might as well collect them directly into a set() :

out = set()
if len(idxs) > 0:
    for i in idxs.flatten():
        (x, y) = (boxes[i][0], boxes[i][1])
        (w, h) = (boxes[i][2], boxes[i][3])
        color = [int(c) for c in COLORS[classIDs[i]]]
        rectangle = cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
        text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
        cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)

        x1= y+h
        y1= x+w
        out.add( (x1,y1) )
print(out)