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from flask import Flask
from flask_cors import CORS, cross_origin
from flask import request
import os
import cv2
import json
import urllib
import time
from flask import request
# Khởi tạo Flask Server Backend
app = Flask(__name__)
# Apply Flask CORS
CORS(app)
app.config['CORS_HEADERS'] = 'Content-Type'
app.config['UPLOAD_FOLDER'] = 'static'
# yolov6_model = my_yolov6.my_yolov6("weights/yolov6s.pt", 'cpu', 'data/coco.yaml', 640, True)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
from torch import nn, optim
import torch.nn.functional as F
import torchvision
from torchvision import datasets, transforms, models
from torch.autograd import Variable
from torch.utils.data.sampler import SubsetRandomSampler
import warnings
warnings.filterwarnings('ignore')
from pytorch_grad_cam import GradCAM, EigenCAM, LayerCAM, XGradCAM
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from pytorch_grad_cam.utils.image import show_cam_on_image, \
deprocess_image, \
preprocess_image
from PIL import Image
import copy
# Load GoogleNet model
model = models.googlenet(pretrained=True)
model.fc= nn.Linear(1024, 4)
model.load_state_dict(torch.load('./model_transfer_batch_2_epoch50.pt'))
data_transforms ={
"train_transforms": transforms.Compose([transforms.RandomRotation(30),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])]),
"valid_transforms": transforms.Compose([transforms.Resize(225),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])]),
"test_transforms": transforms.Compose([transforms.Resize(225),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
}
transform = transforms.Compose([transforms.Resize(225),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
use_cuda = torch.cuda.is_available()
classes = ['BrownSpot', 'Healthy', 'Hispa', 'LeafBlast']
def yolo_format(x, y, w, h, image_size):
x_center_norm = (x+w/2)/image_size[1]
y_center_norm = (y+h/2)/image_size[0]
w_norm = w/image_size[1]
h_norm = h/image_size[0]
return (x_center_norm, y_center_norm, w_norm, h_norm)
def predict_image(image_url):
img = np.array(Image.open(image_url))
img_cp = np.copy(img)
img_cp = cv2.resize(img_cp, (224, 224))
img_cp = np.float32(img_cp) / 255
input_tensor = preprocess_image(img_cp, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
input_tensor = torch.Tensor(input_tensor)
input_tensor.cuda()
output = model(input_tensor)
# print(torch.max(output, 1))
_, preds_tensor = torch.max(output, 1)
preds = np.squeeze(preds, preds_tensor.numpy()) if not use_cuda else np.squeeze(preds_tensor.cpu().numpy())
print(preds)
class_name = classes[preds]
if preds == 1:
grad_bounding_box = (0,0,0,0)
else:
img = np.array(Image.open(image_url))
img = cv2.resize(img, (224, 224))
img = np.float32(img) / 255
input_tensor = torch.Tensor(input_tensor)
input_tensor.cuda()
input_tensor = preprocess_image(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
targets = [ClassifierOutputTarget(0)]
target_layers = [model.inception5b.branch4[1].conv]
with EigenCAM(model=model, target_layers=target_layers) as cam:
grayscale_cams = cam(input_tensor=input_tensor, targets=targets)
cam_image = show_cam_on_image(img, grayscale_cams[0, :], use_rgb=True)
cam = np.uint8(255*grayscale_cams[0, :])
img = np.uint8(255*img)
ret, thresh1 = cv2.threshold(cam, 120, 255, cv2.THRESH_BINARY +
cv2.THRESH_OTSU)
img_otsu = cam < thresh1
img_bin = np.multiply(img_otsu, 1)
img_bin = np.array(img_bin, np.uint8)
contours, _ = cv2.findContours(img_bin,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cnt = contours[0]
x,y,w,h = cv2.boundingRect(cnt)
# grad_bounding_box = (x,y,x+w, y+h)
grad_bounding_box = yolo_format(x, y, w, h, (224, 224))
# print(grad_bounding_box)
return class_name, grad_bounding_box
def yolo2bbox(x, y, w, h, img_size=(224, 224)):
x = x * img_size[1]
y = y * img_size[0]
w = w * img_size[1]
h = h * img_size[0]
x1, y1 = x-w/2, y-h/2
x2, y2 = x+w/2, y+h/2
return int(x1), int(y1), int(x2), int(y2)
def bb_intersection_over_union(boxA, boxB):
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
iou = interArea / float(boxAArea + boxBArea - interArea)
return iou
def read_annot_file(label_file):
with open(os.path.join(label_file), "r") as file1:
# Reading from a file
t = file1.read()
box = t[t.find(" ")+1:]
box = list(box.split(" "))
# list(map(float, box))
for i in range(len(box)):
box[i] = float(box[i])
return box
@app.route('/', methods=['POST'] )
@cross_origin(origin='*')
def predict_leaf():
# image = request.files['file']
img_url = request.form['url']
print(img_url)
image = Image.open(urllib.request.urlopen(img_url))
date = time.time()
filename = str(date) + '.jpg'
if image:
# Lưu file
#save image
path_to_save = os.path.join(app.config['UPLOAD_FOLDER'], filename)
image.save(path_to_save)
# print("Save= ", path_to_save)
predicted_class, grad_bounding_box = predict_image(path_to_save)
# print(predicted_class)
# print(grad_bounding_box)
result_dict = {'class': predicted_class, 'bounding_box': grad_bounding_box}
json_object = json.dumps(result_dict)
print(json_object)
return json_object
return 'Upload file to detect: '
# Start Backend
if __name__ == '__main__':
app.run(host='0.0.0.0', port='6868') |