Match the local copy of the images to the ones in Picsellia and push the predictions of the corresponding images to your experiment evaluation dashboard

evaluate_model.py

eval_image_path = os.path.join(os.getcwd(), eval_set_local_dir)
list_eval_image = os.listdir(eval_image_path)

for img_path in list_eval_image:
    eval_image_ = os.path.join(eval_image_path, img_path)
    print(eval_image_)
    eval_image = Image.open(eval_image_)

    with torch.no_grad():
        inputs = image_processor(images=eval_image, return_tensors="pt")
        outputs = model(**inputs)
        target_sizes = torch.tensor([eval_image.size[::-1]])
        results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
    rectangles = [] # ((x, y, w, h), label, confidence)

    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        fname = eval_image_.split('/')[-1]
        asset = picsellia_eval_ds.find_asset(filename=fname)
        box = [int(i) for i in box.tolist()]
        x, y, x1, y1 = box
        label_name_detected = model.config.id2label[label.item()]
        picsellia_label_object = labels_picsellia[label_name_detected] # label (picsellia one)
        conf = float(score)
        rectangles.append((x, y, x1 - x, y1 - y, picsellia_label_object, conf))
        experiment.add_evaluation(asset, rectangles=rectangles)