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| import gradio as gr | |
| import cv2 | |
| import requests | |
| import os | |
| import torch | |
| import numpy as np | |
| from yolov5.models.experimental import attempt_load | |
| from yolov5.utils.general import non_max_suppression | |
| from yolov5.utils.augmentations import letterbox | |
| # Example URLs for downloading images | |
| file_urls = [ | |
| "https://www.dropbox.com/scl/fi/n3bs5xnl2kanqmwv483k3/1_jpg.rf.4a59a63d0a7339d280dd18ef3c2e675a.jpg?rlkey=4n9dnls1byb4wm54ycxzx3ovi&st=ue5xv8yx&dl=0", | |
| "https://www.dropbox.com/scl/fi/asrmao4b4fpsrhqex8kog/2_jpg.rf.b87583d95aa220d4b7b532ae1948e7b7.jpg?rlkey=jkmux5jjy8euzhxizupdmpesb&st=v3ld14tx&dl=0", | |
| "https://www.dropbox.com/scl/fi/fi0e8zxqqy06asnu0robz/3_jpg.rf.d2932cce7e88c2675e300ececf9f1b82.jpg?rlkey=hfdqwxkxetabe38ukzbb39pl5&st=ga1uouhj&dl=0", | |
| "https://www.dropbox.com/scl/fi/ruobyat1ld1c33ch5yjpv/4_jpg.rf.3395c50b4db0ec0ed3448276965b2459.jpg?rlkey=j1m4qa0pmdh3rlr344v82u3am&st=lex8h3qi&dl=0", | |
| "https://www.dropbox.com/scl/fi/ok3izk4jj1pg6psxja3aj/5_jpg.rf.62f3dc64b6c894fbb165d8f6e2ee1382.jpg?rlkey=euu16z8fd8u8za4aflvu5qg4v&st=pwno39nc&dl=0", | |
| "https://www.dropbox.com/scl/fi/8r1fpwxkwq7c2i6ky6qv5/10_jpg.rf.c1785c33dd3552e860bf043c2fd0a379.jpg?rlkey=fcw41ppgzu0ao7xo6ijbpdi4c&st=to2udvxb&dl=0", | |
| "https://www.dropbox.com/scl/fi/ihiid7hbz1vvaoqrstwa5/7_jpg.rf.dfc30f9dc198cf6697d9023ac076e822.jpg?rlkey=yh67p4ex52wn9t0bfw0jr77ef&st=02qw80xa&dl=0", | |
| ] | |
| def download_file(url, save_name): | |
| """Downloads a file from a URL.""" | |
| if not os.path.exists(save_name): | |
| file = requests.get(url) | |
| with open(save_name, 'wb') as f: | |
| f.write(file.content) | |
| # Download images | |
| for i, url in enumerate(file_urls): | |
| download_file(url, f"image_{i}.jpg") | |
| # Load YOLOv5 model (placeholder) | |
| model_path = "/Users/basilshaji/Desktop/Lung_Nodules_Segmentation/best.pt" # Path to your YOLOv5 model | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Use GPU if available | |
| model = attempt_load(model_path, device=device) # Placeholder for model loading | |
| model.eval() # Set the model to evaluation mode | |
| def preprocess_image(image_path): | |
| img0 = cv2.imread(image_path) | |
| img = letterbox(img0, 640, stride=32, auto=True)[0] # Resize and pad to 640x640 | |
| img = img.transpose(2, 0, 1)[::-1] # Convert BGR to RGB, to 3x416x416 | |
| img = np.ascontiguousarray(img) | |
| img = torch.from_numpy(img).to(device) | |
| img = img.float() # uint8 to fp16/32 | |
| img /= 255.0 # 0 - 255 to 0.0 - 1.0 | |
| if img.ndimension() == 3: | |
| img = img.unsqueeze(0) | |
| return img, img0 | |
| def infer(model, img): | |
| with torch.no_grad(): | |
| pred = model(img)[0] | |
| return pred | |
| def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): | |
| if ratio_pad is None: # calculate from img0_shape | |
| gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new | |
| pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding | |
| else: | |
| gain = ratio_pad[0] | |
| pad = ratio_pad[1] | |
| coords[:, [0, 2]] -= pad[0] # x padding | |
| coords[:, [1, 3]] -= pad[1] # y padding | |
| coords[:, :4] /= gain | |
| coords[:, :4].clip_(min=0, max=img1_shape[0]) # clip boxes | |
| return coords | |
| def postprocess(pred, img0_shape, img): | |
| pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False) | |
| results = [] | |
| for det in pred: # detections per image | |
| if len(det): | |
| det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0_shape).round() | |
| for *xyxy, conf, cls in reversed(det): | |
| results.append((xyxy, conf, cls)) | |
| return results | |
| def detect_objects(image_path): | |
| img, img0 = preprocess_image(image_path) | |
| pred = infer(model, img) | |
| results = postprocess(pred, img0.shape, img) | |
| return results | |
| def draw_bounding_boxes(img, results): | |
| for (x1, y1, x2, y2), conf, cls in results: | |
| x1, y1, x2, y2 = map(int, [x1, y1, x2, y2]) | |
| cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2) | |
| cv2.putText(img, f'{model.names[int(cls)]} {conf:.2f}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2) | |
| return img | |
| def show_preds_image(filepath): | |
| results = detect_objects(filepath) | |
| img0 = cv2.imread(filepath) | |
| img_with_boxes = draw_bounding_boxes(img0, results) | |
| return cv2.cvtColor(img_with_boxes, cv2.COLOR_BGR2RGB) | |
| # Define Gradio components | |
| input_component = gr.components.Image(type="filepath", label="Input Image") | |
| output_component = gr.components.Image(type="numpy", label="Output Image") | |
| # Create Gradio interface | |
| interface = gr.Interface( | |
| fn=show_preds_image, | |
| inputs=input_component, | |
| outputs=output_component, | |
| title="Lung Nodule Detection [ Segmentation Model ]", | |
| examples=[ | |
| "/Users/basilshaji/Desktop/image_1.jpg", | |
| "/Users/basilshaji/Desktop/image_2.jpg", | |
| "/Users/basilshaji/Desktop/image_3.jpg", | |
| "/Users/basilshaji/Desktop/image_4.jpg", | |
| "/Users/basilshaji/Desktop/image_5.jpg", | |
| "/Users/basilshaji/Desktop/image_6.jpg", | |
| ], | |
| description=' "This online deployment proves the effectiveness and efficient function of the machine learning model in identifying lung cancer nodules. The implementation of YOLO for core detection tasks is employed that is an efficient and accurate algorithm for object detection. Through the precise hyper-parameter tuning process, the model proposed in this paper has given an impressive boost in the performance. Moreover, the model uses Retinanet algorithm which is recognized as the powerful tool effective in dense object detection. In an attempt to enhance the model’s performance, the backbone of this architecture consists of a Feature Pyramid Network (FPN). The FPN plays an important role in boosting the model’s capacity in recognizing objects in different scales through the construction of high semantic feature map in different resolutions. In conclusion, this deployment encompasses YOLOv5, hyperparameter optimization, Retinanet, and FPN as one of the most effective and modern solutions for the detection of lung cancer nodules." ~ Basil Shaji 😇', | |
| live=False, | |
| ) | |
| interface.launch() | |