import io import matplotlib.pyplot as plt import requests, validators import torch import pathlib from PIL import Image from transformers import AutoFeatureExtractor, DetrForObjectDetection, YolosForObjectDetection from ultralyticsplus import YOLO, render_result import os # colors for visualization COLORS = [ [0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125], [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933] ] YOLOV8_LABELS = ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor'] def make_prediction(img, feature_extractor, model): inputs = feature_extractor(img, return_tensors="pt") outputs = model(**inputs) img_size = torch.tensor([tuple(reversed(img.size))]) processed_outputs = feature_extractor.post_process(outputs, img_size) return processed_outputs def fig2img(fig): buf = io.BytesIO() fig.savefig(buf) buf.seek(0) img = Image.open(buf) return img def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None): keep = output_dict["scores"] > threshold boxes = output_dict["boxes"][keep].tolist() scores = output_dict["scores"][keep].tolist() labels = output_dict["labels"][keep].tolist() if id2label is not None: labels = [id2label[x] for x in labels] # print("Labels " + str(labels)) plt.figure(figsize=(16, 10)) plt.imshow(pil_img) ax = plt.gca() colors = COLORS * 100 for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors): ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3)) ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5)) plt.axis("off") return fig2img(plt.gcf()) def detect_objects(model_name,url_input,image_input,threshold): if 'yolov8' in model_name: # Working on getting this to work, another approach # https://docs.ultralytics.com/modes/predict/#key-features-of-predict-mode model = YOLO(model_name) # set model parameters model.overrides['conf'] = 0.15 # NMS confidence threshold model.overrides['iou'] = 0.05 # NMS IoU threshold https://www.google.com/search?client=firefox-b-1-d&q=intersection+over+union+meaning model.overrides['agnostic_nms'] = False # NMS class-agnostic model.overrides['max_det'] = 1000 # maximum number of detections per image results = model.predict(image_input) render = render_result(model=model, image=image_input, result=results[0]) final_str = "" final_str_abv = "" final_str_else = "" for result in results: boxes = result.boxes.cpu().numpy() for i, box in enumerate(boxes): # r = box.xyxy[0].astype(int) coordinates = box.xyxy[0].astype(int) try: label = YOLOV8_LABELS[int(box.cls)] except: label = "ERROR" try: confi = float(box.conf) except: confi = 0.0 # final_str_abv += str() + "__" + str(box.cls) + "__" + str(box.conf) + "__" + str(box) + "\n" if confi >= threshold: final_str_abv += f"Detected `{label}` with confidence `{confi}` at location `{coordinates}`\n" else: final_str_else += f"Detected `{label}` with confidence `{confi}` at location `{coordinates}`\n" final_str = "{:*^50}\n".format("ABOVE THRESHOLD OR EQUAL") + final_str_abv + "\n{:*^50}\n".format("BELOW THRESHOLD")+final_str_else return render, final_str else: #Extract model and feature extractor feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) if 'detr' in model_name: model = DetrForObjectDetection.from_pretrained(model_name) elif 'yolos' in model_name: model = YolosForObjectDetection.from_pretrained(model_name) tb_label = "" if validators.url(url_input): image = Image.open(requests.get(url_input, stream=True).raw) tb_label = "Confidence Values URL" elif image_input: image = image_input tb_label = "Confidence Values Upload" #Make prediction processed_output_list = make_prediction(image, feature_extractor, model) # print("After make_prediction" + str(processed_output_list)) processed_outputs = processed_output_list[0] #Visualize prediction viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label) # return [viz_img, processed_outputs] # print(type(viz_img)) final_str_abv = "" final_str_else = "" for score, label, box in sorted(zip(processed_outputs["scores"], processed_outputs["labels"], processed_outputs["boxes"]), key = lambda x: x[0].item(), reverse=True): box = [round(i, 2) for i in box.tolist()] if score.item() >= threshold: final_str_abv += f"Detected `{model.config.id2label[label.item()]}` with confidence `{round(score.item(), 3)}` at location `{box}`\n" else: final_str_else += f"Detected `{model.config.id2label[label.item()]}` with confidence `{round(score.item(), 3)}` at location `{box}`\n" # https://docs.python.org/3/library/string.html#format-examples final_str = "{:*^50}\n".format("ABOVE THRESHOLD OR EQUAL") + final_str_abv + "\n{:*^50}\n".format("BELOW THRESHOLD")+final_str_else return viz_img, final_str title = """

Object Detection App with DETR and YOLOS

""" description = """ Links to HuggingFace Models: - [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) - [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101) - [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) - [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) - [facebook/detr-resnet-101-dc5](https://huggingface.co/facebook/detr-resnet-101-dc5) - [hustvl/yolos-small-300](https://huggingface.co/hustvl/yolos-small-300) - [mshamrai/yolov8x-visdrone](https://huggingface.co/mshamrai/yolov8x-visdrone) """ models = ["facebook/detr-resnet-50","facebook/detr-resnet-101",'hustvl/yolos-small','hustvl/yolos-tiny','facebook/detr-resnet-101-dc5', 'hustvl/yolos-small-300', 'mshamrai/yolov8x-visdrone'] urls = ["https://c8.alamy.com/comp/J2AB4K/the-new-york-stock-exchange-on-the-wall-street-in-new-york-J2AB4K.jpg"] TEST_IMAGE = Image.open(r"images/Test_Street_VisDrone.JPG") # image_functions.detect_objects('facebook/detr-resnet-50', "", image_functions.TEST_IMAGE, 0.7)