import os
import time
import cv2
import numpy as np
from yolo.model.yolo_model import YOLO
import streamlit as st


def process_image(img):
    """ 이미지 리사이즈하고, 차원 확장 
    img : 원본이미지
    결과는 ( 64, 64, 3 ) 으로 프로세싱된 이미지 반환 """
    image_org = cv2.resize(img, (416, 416), interpolation=cv2.INTER_CUBIC)
    image_org = np.array(image_org, dtype='float32')
    image_org = image_org / 255.0
    image_org = np.expand_dims(image_org, axis=0)
    return image_org


def get_classes(file):
    """ 클래스의 이름을 가져온다.
    리스트로 클래스 이름을 반환한다. """
    with open(file) as f:
        name_of_class = f.readlines()
    name_of_class = [class_name.strip() for class_name in name_of_class]
    return name_of_class


def box_draw(image, boxes, scores, classes, all_classes):
    """ image : 원본이미지
    boxes : 오브젝트의 박스데이터, ndarry
    classes : 오브젝트의 클래스
    scores : 오브젝트의 확률
    all_classes : 모든 클래스 이름 """
    for box, score, cl in zip(boxes, scores, classes):
        x, y, w, h = box
        top = max(0, np.floor(x + 0.5).astype(int))
        left = max(0, np.floor(y + 0.5).astype(int))
        right = min(image.shape[1], np.floor(x + w + 0.5).astype(int))
        bottom = min(image.shape[0], np.floor(y + h + 0.5).astype(int))
        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 3)
        cv2.putText(image, '{0} {1:.2f}'.format(all_classes[cl], score), (
            top, left - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 3,
            cv2.LINE_AA)
        print('class: {0}, score: {1:.2f}'.format(all_classes[cl], score))
        print('box coordinate x,y,w,h: {0}'.format(box))
    print()


def detect_image(image, yolo, all_classes):
    """ image : 원본 이미지
    yolo : YOLO 모델
    all_classes : 전체 클래스 이름 
    변환된 이미지 리턴 """
    pimage = process_image(image)
    image_boxes, image_classes, image_scores = yolo.predict(pimage, image.shape
        )
    if image_boxes is not None:
        box_draw(image, image_boxes, image_scores, image_classes, all_classes)
    return image


def yolo_img(image_path):
    yolo = YOLO(0.6, 0.5)
    all_classes = get_classes('yolo/data/coco_classes.txt')
    image = cv2.imread(str(image_path))
    result_image = detect_image(image, yolo, all_classes)
    result_image = cv2.resize(result_image, (800, 600))
    result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
    st.image(result_image)

fbed00cbe34749201fd6cb3a69288faf469a8101
<mask token>


def get_classes(file):
    """ 클래스의 이름을 가져온다.
    리스트로 클래스 이름을 반환한다. """
    with open(file) as f:
        name_of_class = f.readlines()
    name_of_class = [class_name.strip() for class_name in name_of_class]
    return name_of_class


<mask token>


def detect_image(image, yolo, all_classes):
    """ image : 원본 이미지
    yolo : YOLO 모델
    all_classes : 전체 클래스 이름 
    변환된 이미지 리턴 """
    pimage = process_image(image)
    image_boxes, image_classes, image_scores = yolo.predict(pimage, image.shape
        )
    if image_boxes is not None:
        box_draw(image, image_boxes, image_scores, image_classes, all_classes)
    return image


def yolo_img(image_path):
    yolo = YOLO(0.6, 0.5)
    all_classes = get_classes('yolo/data/coco_classes.txt')
    image = cv2.imread(str(image_path))
    result_image = detect_image(image, yolo, all_classes)
    result_image = cv2.resize(result_image, (800, 600))
    result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
    st.image(result_image)

<mask token>


def get_classes(file):
    """ 클래스의 이름을 가져온다.
    리스트로 클래스 이름을 반환한다. """
    with open(file) as f:
        name_of_class = f.readlines()
    name_of_class = [class_name.strip() for class_name in name_of_class]
    return name_of_class


def box_draw(image, boxes, scores, classes, all_classes):
    """ image : 원본이미지
    boxes : 오브젝트의 박스데이터, ndarry
    classes : 오브젝트의 클래스
    scores : 오브젝트의 확률
    all_classes : 모든 클래스 이름 """
    for box, score, cl in zip(boxes, scores, classes):
        x, y, w, h = box
        top = max(0, np.floor(x + 0.5).astype(int))
        left = max(0, np.floor(y + 0.5).astype(int))
        right = min(image.shape[1], np.floor(x + w + 0.5).astype(int))
        bottom = min(image.shape[0], np.floor(y + h + 0.5).astype(int))
        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 3)
        cv2.putText(image, '{0} {1:.2f}'.format(all_classes[cl], score), (
            top, left - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 3,
            cv2.LINE_AA)
        print('class: {0}, score: {1:.2f}'.format(all_classes[cl], score))
        print('box coordinate x,y,w,h: {0}'.format(box))
    print()


def detect_image(image, yolo, all_classes):
    """ image : 원본 이미지
    yolo : YOLO 모델
    all_classes : 전체 클래스 이름 
    변환된 이미지 리턴 """
    pimage = process_image(image)
    image_boxes, image_classes, image_scores = yolo.predict(pimage, image.shape
        )
    if image_boxes is not None:
        box_draw(image, image_boxes, image_scores, image_classes, all_classes)
    return image


def yolo_img(image_path):
    yolo = YOLO(0.6, 0.5)
    all_classes = get_classes('yolo/data/coco_classes.txt')
    image = cv2.imread(str(image_path))
    result_image = detect_image(image, yolo, all_classes)
    result_image = cv2.resize(result_image, (800, 600))
    result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
    st.image(result_image)

<mask token>


def process_image(img):
    """ 이미지 리사이즈하고, 차원 확장 
    img : 원본이미지
    결과는 ( 64, 64, 3 ) 으로 프로세싱된 이미지 반환 """
    image_org = cv2.resize(img, (416, 416), interpolation=cv2.INTER_CUBIC)
    image_org = np.array(image_org, dtype='float32')
    image_org = image_org / 255.0
    image_org = np.expand_dims(image_org, axis=0)
    return image_org


def get_classes(file):
    """ 클래스의 이름을 가져온다.
    리스트로 클래스 이름을 반환한다. """
    with open(file) as f:
        name_of_class = f.readlines()
    name_of_class = [class_name.strip() for class_name in name_of_class]
    return name_of_class


def box_draw(image, boxes, scores, classes, all_classes):
    """ image : 원본이미지
    boxes : 오브젝트의 박스데이터, ndarry
    classes : 오브젝트의 클래스
    scores : 오브젝트의 확률
    all_classes : 모든 클래스 이름 """
    for box, score, cl in zip(boxes, scores, classes):
        x, y, w, h = box
        top = max(0, np.floor(x + 0.5).astype(int))
        left = max(0, np.floor(y + 0.5).astype(int))
        right = min(image.shape[1], np.floor(x + w + 0.5).astype(int))
        bottom = min(image.shape[0], np.floor(y + h + 0.5).astype(int))
        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 3)
        cv2.putText(image, '{0} {1:.2f}'.format(all_classes[cl], score), (
            top, left - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 3,
            cv2.LINE_AA)
        print('class: {0}, score: {1:.2f}'.format(all_classes[cl], score))
        print('box coordinate x,y,w,h: {0}'.format(box))
    print()


def detect_image(image, yolo, all_classes):
    """ image : 원본 이미지
    yolo : YOLO 모델
    all_classes : 전체 클래스 이름 
    변환된 이미지 리턴 """
    pimage = process_image(image)
    image_boxes, image_classes, image_scores = yolo.predict(pimage, image.shape
        )
    if image_boxes is not None:
        box_draw(image, image_boxes, image_scores, image_classes, all_classes)
    return image


def yolo_img(image_path):
    yolo = YOLO(0.6, 0.5)
    all_classes = get_classes('yolo/data/coco_classes.txt')
    image = cv2.imread(str(image_path))
    result_image = detect_image(image, yolo, all_classes)
    result_image = cv2.resize(result_image, (800, 600))
    result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
    st.image(result_image)

import os
import time
import cv2
import numpy as np
from yolo.model.yolo_model import YOLO
import streamlit as st


def process_image(img):
    """ 이미지 리사이즈하고, 차원 확장 
    img : 원본이미지
    결과는 ( 64, 64, 3 ) 으로 프로세싱된 이미지 반환 """
    image_org = cv2.resize(img, (416, 416), interpolation=cv2.INTER_CUBIC)
    image_org = np.array(image_org, dtype='float32')
    image_org = image_org / 255.0
    image_org = np.expand_dims(image_org, axis=0)
    return image_org


def get_classes(file):
    """ 클래스의 이름을 가져온다.
    리스트로 클래스 이름을 반환한다. """
    with open(file) as f:
        name_of_class = f.readlines()
    name_of_class = [class_name.strip() for class_name in name_of_class]
    return name_of_class


def box_draw(image, boxes, scores, classes, all_classes):
    """ image : 원본이미지
    boxes : 오브젝트의 박스데이터, ndarry
    classes : 오브젝트의 클래스
    scores : 오브젝트의 확률
    all_classes : 모든 클래스 이름 """
    for box, score, cl in zip(boxes, scores, classes):
        x, y, w, h = box
        top = max(0, np.floor(x + 0.5).astype(int))
        left = max(0, np.floor(y + 0.5).astype(int))
        right = min(image.shape[1], np.floor(x + w + 0.5).astype(int))
        bottom = min(image.shape[0], np.floor(y + h + 0.5).astype(int))
        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 3)
        cv2.putText(image, '{0} {1:.2f}'.format(all_classes[cl], score), (
            top, left - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 3,
            cv2.LINE_AA)
        print('class: {0}, score: {1:.2f}'.format(all_classes[cl], score))
        print('box coordinate x,y,w,h: {0}'.format(box))
    print()


def detect_image(image, yolo, all_classes):
    """ image : 원본 이미지
    yolo : YOLO 모델
    all_classes : 전체 클래스 이름 
    변환된 이미지 리턴 """
    pimage = process_image(image)
    image_boxes, image_classes, image_scores = yolo.predict(pimage, image.shape
        )
    if image_boxes is not None:
        box_draw(image, image_boxes, image_scores, image_classes, all_classes)
    return image


def yolo_img(image_path):
    yolo = YOLO(0.6, 0.5)
    all_classes = get_classes('yolo/data/coco_classes.txt')
    image = cv2.imread(str(image_path))
    result_image = detect_image(image, yolo, all_classes)
    result_image = cv2.resize(result_image, (800, 600))
    result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
    st.image(result_image)
