working.txt

import cv2
import numpy as np
import time

labelsPath = 'obj.names'
LABELS = open(labelsPath).read().strip().split("\n")

weightsPath = 'crop_weed_detection.weights'
configPath = 'crop_weed.cfg'
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8")
print("Loading YOLO model...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)

cap = cv2.VideoCapture(0)

detections = []

while True:
    ret, frame = cap.read()

    if not ret:
        print("Error reading from camera.")
        break

    (H, W) = frame.shape[:2]

    confi = 0.5
    thresh = 0.5

    ln = net.getLayerNames()
    ln = [ln[i - 1] for i in net.getUnconnectedOutLayers()]

    blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (512, 512), swapRB=True, crop=False)
    net.setInput(blob)
    start = time.time()
    layerOutputs = net.forward(ln)
    end = time.time()

    print("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 > confi:
                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, confi, thresh)
    print("Detections done\n")

    if len(idxs) > 0:
        for i in idxs.flatten():
            x, y, w, h = boxes[i]

            detection_info = {
                "x": x,
                "y": y,
                "width": w,
                "height": h,
                "label": LABELS[classIDs[i]]
            }

            detections.append(detection_info)

            print("[OUTPUT]   : detected label -> ", LABELS[classIDs[i]])

    print("Detection Results:", detections)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()
print("[STATUS]   : Completed")
print("[END]")