Extract Image from bounded box using YOLO

yolo.py

# import the necessary packages
from os import walk
import numpy as np
import argparse
import time
import cv2
import os
import re
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()

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())


# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")
# initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
    dtype="uint8")

    # derive the paths to the YOLO weights and model configuration
weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])
# load our YOLO object detector trained on COCO dataset (80 classes)
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)


mypath = "/Users/xxx/work/scraping/test3"
cropoutputdir = "/Users/xxx/work/scraping/test3_cropped"



def cropimage(mypath, mydir, impath):
    # load our input image and grab its spatial dimensions
    imagepath = mypath + "/" + mydir + "/" + impath
    croppedimagepath = cropoutputdir + "/" + mydir + "/" + impath
    if not os.path.exists(cropoutputdir + "/" + mydir):
        os.makedirs(cropoutputdir + "/" + mydir)
    image = cv2.imread(imagepath)

    
    
    (H, W) = image.shape[:2]
    # determine only the *output* layer names that we need from YOLO
    ln = net.getLayerNames()
    ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    # construct a blob from the input image and then perform a forward
    # pass of the YOLO object detector, giving us our bounding boxes and
    # associated probabilities
    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()
    # show timing information on YOLO
    print("[INFO] YOLO took {:.6f} seconds".format(end - start))

    # initialize our lists of detected bounding boxes, confidences, and
    # class IDs, respectively
    boxes = []
    confidences = []
    classIDs = []

    # loop over each of the layer outputs
    for output in layerOutputs:
        # loop over each of the detections
        for detection in output:
            # extract the class ID and confidence (i.e., probability) of
            # the current object detection
            scores = detection[5:]
            classID = np.argmax(scores)
            confidence = scores[classID]
            # filter out weak predictions by ensuring the detected
            # probability is greater than the minimum probability
            if confidence > args["confidence"]:
                # scale the bounding box coordinates back relative to the
                # size of the image, keeping in mind that YOLO actually
                # returns the center (x, y)-coordinates of the bounding
                # box followed by the boxes' width and height
                box = detection[0:4] * np.array([W, H, W, H])
                (centerX, centerY, width, height) = box.astype("int")
                # use the center (x, y)-coordinates to derive the top and
                # and left corner of the bounding box
                x = int(centerX - (width / 2))
                y = int(centerY - (height / 2))
                # update our list of bounding box coordinates, confidences,
                # and class IDs
                boxes.append([x, y, int(width), int(height)])
                confidences.append(float(confidence))
                classIDs.append(classID)


    # apply non-maxima suppression to suppress weak, overlapping bounding
    # boxes
    idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"],
        args["threshold"])


    # ensure at least one detection exists
    if len(idxs) > 0:
        # loop over the indexes we are keeping
        for i in idxs.flatten():
            # extract the bounding box coordinates
            (x, y) = (boxes[i][0], boxes[i][1])
            (w, h) = (boxes[i][2], boxes[i][3])
            # draw a bounding box rectangle and label on the image
            color = [int(c) for c in COLORS[classIDs[i]]]
            # cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
            if(confidences[i] > 0.5 and LABELS[classIDs[i] == "Bird"]):
       
                print("bird found")
                print(mydir + "/" + impath)
    
                         
                crop = image[y:y+h, x:x+w]
    
                if crop.size != 0:
                    cv2.imwrite(cropoutputdir + "/" + mydir + "/" + impath, crop)
                break   


# crop entire directory
for (dirpath, dirnames, filenames) in walk(mypath):

    for mydir in dirnames:
        print(mydir)
        for(dirpath, dirnames, filenames) in walk(mypath + "/" + mydir):

            for myfile in filenames:
                if re.search('jpg', myfile):
                    cropimage(mypath, mydir, myfile)

    break