daisi.py

import argparse
import os
import platform
import sys
import streamlit as st
import torch
import torch.backends.cudnn as cudnn
import numpy as np
from pathlib import Path
from PIL import Image

from torchvision import transforms, models



FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync

weights="appledd-yolov5s-800.pb"
device = torch.device("cuda" if torch.cuda.is_available()  else "cpu")


#### Class for classification model
import torch.nn as nn
import torch.nn.functional as F
class NaturalSceneClassification(nn.Module):
    def __init__(self):
        super().__init__()
        self.network = torch.hub.load('pytorch/vision:v0.10.0', 'mobilenet_v2', pretrained=True)
        
        self.network.fc = nn.Sequential(nn.Linear(2048, 512),
                                 nn.ReLU(),
                                 nn.Dropout(0.2),
                                 nn.Linear(512, 10),
                                 nn.Softmax(dim=1))
        
    
    def forward(self, xb):
        return self.network(xb)

    def training_step(self, batch):
        images, labels = batch 
        images, labels = images.to(device), labels.to(device)
        out = self(images)                  # Generate predictions
        loss = F.cross_entropy(out, labels) # Calculate loss
        return loss
    
    def validation_step(self, batch):
        images, labels = batch 
        images, labels = images.to(device), labels.to(device)
        out = self(images)                    # Generate predictions
        loss = F.cross_entropy(out, labels)   # Calculate loss
        acc = accuracy(out, labels)           # Calculate accuracy
        return {'val_loss': loss.detach(), 'val_acc': acc}
        
    def validation_epoch_end(self, outputs):
        batch_losses = [x['val_loss'] for x in outputs]
        epoch_loss = torch.stack(batch_losses).mean()   # Combine losses
        batch_accs = [x['val_acc'] for x in outputs]
        epoch_acc = torch.stack(batch_accs).mean()      # Combine accuracies
        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}
    
    def epoch_end(self, epoch, result):
        print("Epoch [{}], train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}".format(
            epoch, result['train_loss'], result['val_loss'], result['val_acc']))


			
			
def increase_contrast(image):
    if isinstance(image, Image.Image):
        # Convert the PIL image to a numpy array
        image = np.array(image)

    if not isinstance(image, np.ndarray):
        raise ValueError("Input must be a valid numpy array")

    # Convert the image to grayscale if it's in color
    if len(image.shape) == 3:
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Calculate min and max values
    min_val = image.min()
    max_val = image.max()

    if min_val == max_val:
        return image  # Avoid division by zero

    # Apply contrast stretching
    contrast_stretched = cv2.convertScaleAbs(image, alpha=255.0 / (max_val - min_val), beta=-min_val)

    return contrast_stretched

def reduce_noise(image, kernel_size=(3, 3)):
    # Apply Gaussian blur to reduce noise
    blurred = cv2.GaussianBlur(image, kernel_size, 0)

    return blurred
@torch.no_grad()
def run(
        weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
        data=ROOT / 'data.yaml',  # dataset.yaml path
        imgsz=(640, 640),  # inference size (height, width)
        conf_thres=0.25,  # confidence threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=1000,  # maximum detections per image
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img=False,  # show results
        save_txt=False,  # save results to *.txt
        save_conf=False,  # save confidences in --save-txt labels
        save_crop=False,  # save cropped prediction boxes
        nosave=False,  # do not save images/videos
        classes=None,  # filter by class: --class 0, or --class 0 2 3
        agnostic_nms=False,  # class-agnostic NMS
        augment=False,  # augmented inference
        visualize=False,  # visualize features
        update=False,  # update all models
        project=ROOT / 'runs/detect',  # save results to project/name
        name='exp',  # save results to project/name
        exist_ok=True,  # existing project/name ok, do not increment
        line_thickness=2,  # bounding box thickness (pixels)
        hide_labels=False,  # hide labels
        hide_conf=False,  # hide confidences
        half=False,  # use FP16 half-precision inference
        dnn=False,  # use OpenCV DNN for ONNX inference

        upl_image: np.ndarray=None, 
        #return_type: list=["Image", "Labels"]
):
    
    source = str(source)
    save_img = not nosave and not source.endswith('.txt')  # save inference images
    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
    if is_url and is_file:
        source = check_file(source)  # download

    # Directories
    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # Load model
    device = select_device(device)
    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
    stride, names, pt = model.stride, model.names, model.pt
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    # Dataloader
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
        bs = len(dataset)  # batch_size
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
        bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
    seen, windows, dt = 0, [], [0.0, 0.0, 0.0]
    for path, im, im0s, vid_cap, s in dataset:
        t1 = time_sync()
        #im=upl_image
        im = torch.from_numpy(im).to(device)
        im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
        im /= 255  # 0 - 255 to 0.0 - 1.0
        if len(im.shape) == 3:
            im = im[None]  # expand for batch dim
        t2 = time_sync()
        dt[0] += t2 - t1
         # Contrast enhancement
        # im = increase_contrast(im)

        # # Noise reduction
        # im = reduce_noise(im)
        # Inference
        visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
        pred = model(im, augment=augment, visualize=visualize)
        t3 = time_sync()
        dt[1] += t3 - t2
       
        # NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        dt[2] += time_sync() - t3

        # Second-stage classifier (optional)
        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)

        # Process predictions
        for i, det in enumerate(pred):  # per image
            seen += 1
            if webcam:  # batch_size >= 1
                p, im0, frame = path[i], im0s[i].copy(), dataset.count
                s += f'{i}: '
            else:
                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # im.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
            s += '%gx%g ' % im.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            imc = im0.copy() if save_crop else im0  # for save_crop
            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                        with open(f'{txt_path}.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')

                    if save_img or save_crop or view_img:  # Add bbox to image
                        c = int(cls)  # integer class
                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                        annotator.box_label(xyxy, label, color=colors(c, True))
                    if save_crop:
                        save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

            # Stream results
            im0 = annotator.result()
            if view_img:
                if platform.system() == 'Linux' and p not in windows:
                    windows.append(p)
                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    #cv2.imwrite(save_path, im0)
                    print("Save")
                else:  # 'video' or 'stream'
                    if vid_path[i] != save_path:  # new video
                        vid_path[i] = save_path
                        if isinstance(vid_writer[i], cv2.VideoWriter):
                            vid_writer[i].release()  # release previous video writer
                        if vid_cap:  # video
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else:  # stream
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                        save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer[i].write(im0)

        # Print time (inference-only)
        LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')

    # Print results
    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
    if save_txt or save_img:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
    if update:
        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)
    im0 = cv2.cvtColor(im0, cv2.COLOR_BGR2RGB)
    return im0


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
    parser.add_argument('--data', type=str, default=ROOT / 'data.yaml', help='(optional) dataset.yaml path')
    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[800], help='inference size h,w')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='show results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--visualize', action='store_true', help='visualize features')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
    print_args(vars(opt))
    return opt

def classify(model,img):
    img = img.to(device)
    prediction =  model(img)
    sc, preds = torch.max(prediction, dim = 1)
    return sc[0].item(),preds[0].item()
    

def main(opt,model,labels):
    #check_requirements(exclude=('tensorboard', 'thop'))
    #run(**vars(opt))
    st.image("logo.jpg", caption="")
    st.title("#Welcome to Deep Diagnosis")
    # st.write("By: Dr. Asif Iqbal Khan")
    st.markdown(
        """
            This app allows you to detect different apple diseases from leaf images. 
            1) Scab
            2) Alternaria
            3) MLB
            4) Mossaic
            5) Powdery Mildew
            6) Necrosis		
        """
    )
    url="https://www.sciencedirect.com/science/article/abs/pii/S0168169922004100"
    st.write("Link to the research paper: [link] (%s)" %url)

    st.write("This app allows you to provide an image, and one of the most advanced Object Detection algorithms available will try to classify it for you. Upload your data to get started!")
    
    with st.sidebar:
        # st.image("logo.jpg", caption="")
        uploaded_file = st.file_uploader("Choose an Image", type=["png","jpg","jpeg"])
        return_types = st.multiselect("Select Return Type", ["Image", "Labels"], ["Image", "Labels"])
        
    if not uploaded_file:
        file_name = "sample.jpg"
        st.write("Upload apple leaf image to detect diseases")
        st.image("sample.jpg", caption='Sample Image',width=400)
	
    else:
        file_name = uploaded_file.name
	
        #image = np.array(Image.open(image_file_buffer))
	    #Saving upload
        file_details = {"filename":uploaded_file.name, "filetype":uploaded_file.type,"filesize":uploaded_file.size}
        #st.write(file_details)
        with open(file_name,"wb") as f:
            f.write((uploaded_file).getbuffer())
        
        img = Image.open(uploaded_file)
        if img.format.lower() != "jpeg" or img.format.lower() !="jpg" :
        # Convert the image to RGB format (JPEG-compatible) and save as a temporary JPEG file
            img = img.convert("RGB")
            temp_jpeg_file = "temp_image.jpg"
            img.save(temp_jpeg_file, "JPEG")
            
            img.close()
        
            # Load the temporary JPEG file for processing
            img = Image.open(temp_jpeg_file)
        
      
            
        img = transforms.Resize((360,360))(img)
        img = transforms.ToTensor()(img)
        img = img.unsqueeze(0).to(device)
        res=classify(model,img)
        

        lb=labels[res[1]]
        sc=res[0]
        st.write(lb+" "+str(sc))
        if(lb=="noleaf"):
            st.write("Invalid image! Try Some other image")
        elif(lb=="healthy"):
            st.write("Looks healthy to me")
        elif(lb=="demaged"):
            st.write("No recognizable disease found")
        else:
            if(sc>7):
                final_result = run(weights,file_name)
                st.image(final_result, caption='Diseases Detected', width=400)

            else:
                st.write("No disease detected")	
        				
		#final_result = run(weights,file_name)
        #st.image(final_result, caption='Diseases Detected')
        os.remove(file_name) 
        #Remove the temporary JPEG file after processing
        os.remove(temp_jpeg_file)
    


if __name__ == "__main__":
    opt = parse_opt()
    model=NaturalSceneClassification()
    model=torch.load("mobilenetv2-apple-10-class-pytorch.pth",map_location=device )
    model.eval()
    
    labels=[]
    with open("labels.txt") as file:
        for line in file: 
           line = line.strip() #or some other preprocessing
           labels.append(line) #st
    main(opt,model,labels)