objectdetector.py

import cv2
import numpy as np
import textgen

# textpart
img_size = None

# cvpart
print("Loading YOLO ...")
net = cv2.dnn.readNet("yolov3.cfg", "yolov3.weights")
# net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i-1] for i in net.getUnconnectedOutLayers()]
print("YOLO loaded")

# object detection


def detect_objects(img_path, img_show=False, img_highlight=False):
    print("Detecting objects ...")
    objs = []
    cv2.destroyAllWindows()

    img = cv2.imread(img_path)
    # img=cv2.resize(img,(500,500))
    height, width, channels = img.shape
    img_size = (width, height)  # for text
    blob = cv2.dnn.blobFromImage(
        img, 1/255.0, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)
    outs = net.forward(output_layers)

    class_ids = []
    confidences = []
    boxes = []
    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5:
                center_x = int(detection[0]*width)
                center_y = int(detection[1]*height)
                w = int(detection[2]*width)
                h = int(detection[3]*height)

                x = int(center_x - w / 2)
                y = int(center_y - h / 2)

                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)

    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
    colors = np.random.uniform(0, 255, size=(len(classes), 3))

    for i in range(len(boxes)):
        if i in indexes:
            x, y, w, h = boxes[i]
            label = str(classes[class_ids[i]])

            objs.append(dict(cls=label, x=x, y=y, w=w, h=h))

            # rectagle highlighting
            if img_highlight:
                color = colors[class_ids[i]]
                cv2.rectangle(img, (x, y), (x+w, y+h), color, 2)
                cv2.putText(img, label, (x, y-5),
                            cv2.FONT_HERSHEY_SIMPLEX, 1/2, color, 2)

    print("Object detection complete")
    if img_show:
        cv2.imshow("Image", img)
        cv2.waitKey(1000)

    return objs


def detectandgenerate(img_path):
    objs = detect_objects(img_path)
    gentext = textgen.getText(objs)
    return gentext