image_functions.py

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 = """<h1 id="title">Object Detection App with DETR and YOLOS</h1>"""

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")

# Test command line when in python terminal: image_functions.detect_objects('facebook/detr-resnet-50', "", image_functions.TEST_IMAGE, 0.7)