pythonAnalyser.py

import numpy as np
import argparse
import cv2
import os

def analyseImage(labelsPath, weightsPath, configPath, imgPath):
    LABELS = open(labelsPath).read().strip().split("\n")
    np.random.seed(42)
    COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),dtype="uint8")

    net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)

    image = cv2.imread(imgPath)
    (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)
    layerOutputs = net.forward(ln)

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

    for output in layerOutputs:
        for detection in output:
            scores = detection[5:]
            classID = np.argmax(scores)
            confidence = scores[classID]

            if confidence > 0.2:
                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)])
                returnboxes.append([int(centerX), int(centerY), int(width), int(height)])
                confidences.append(float(confidence))
                classIDs.append(classID)

    idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.2, 0.2)

    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]]]
            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)
    
    return returnboxes, image