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| import subprocess | |
| import sys | |
| print("Reinstalling mmcv") | |
| subprocess.check_call([sys.executable, "-m", "pip", "uninstall", "-y", "mmcv-full==1.3.17"]) | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", "mmcv-full==1.3.17", "-f", "https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html"]) | |
| print("mmcv install complete") | |
| ## Only works if we reinstall mmcv here. | |
| from gradio.outputs import Label | |
| from icevision.all import * | |
| from icevision.models.checkpoint import * | |
| import PIL | |
| import gradio as gr | |
| import os | |
| # Load model | |
| checkpoint_path = "models/model_checkpoint.pth" | |
| checkpoint_and_model = model_from_checkpoint(checkpoint_path) | |
| model = checkpoint_and_model["model"] | |
| model_type = checkpoint_and_model["model_type"] | |
| class_map = checkpoint_and_model["class_map"] | |
| # Transforms | |
| img_size = checkpoint_and_model["img_size"] | |
| valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()]) | |
| for root, dirs, files in os.walk(r"sample_images/"): | |
| for filename in files: | |
| print("Loading sample image:", filename) | |
| # Populate examples in Gradio interface | |
| example_images = [["sample_images/" + file] for file in files] | |
| # Columns: Input Image | Label | Box | Detection Threshold | |
| #examples = [ | |
| # [example_images[0], False, True, 0.5], | |
| # [example_images[1], True, True, 0.5], | |
| # [example_images[2], False, True, 0.7], | |
| # [example_images[3], True, True, 0.7], | |
| # [example_images[4], False, True, 0.5], | |
| # [example_images[5], False, True, 0.5], | |
| # [example_images[6], False, True, 0.6], | |
| # [example_images[7], False, True, 0.6], | |
| #] | |
| examples = [['sample_images/IMG_20191212_151351.jpg'],['sample_images/IMG_20191212_153420.jpg'],['sample_images/IMG_20191212_154100.jpg']] | |
| #def show_preds(input_image, display_label, display_bbox, detection_threshold): | |
| def show_preds(input_image): | |
| # if detection_threshold == 0: | |
| #detection_threshold = 0.5 | |
| img = PIL.Image.fromarray(input_image, "RGB") | |
| pred_dict = model_type.end2end_detect(img, valid_tfms, model, class_map=class_map, detection_threshold=0.5, | |
| display_label=False, display_bbox=True, return_img=True, | |
| font_size=16, label_color="#FF59D6") | |
| #pred_dict = model_type.end2end_detect( | |
| # img, | |
| # valid_tfms, | |
| # model, | |
| # class_map=class_map, | |
| # detection_threshold=detection_threshold, | |
| # display_label=display_label, | |
| # display_bbox=display_bbox, | |
| # return_img=True, | |
| # font_size=16, | |
| # label_color="#FF59D6", | |
| #) | |
| return pred_dict["img"], len(pred_dict["detection"]["bboxes"]) | |
| # display_chkbox = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display", default=True) | |
| display_chkbox_label = gr.inputs.Checkbox(label="Label", default=False) | |
| display_chkbox_box = gr.inputs.Checkbox(label="Box", default=True) | |
| detection_threshold_slider = gr.inputs.Slider( | |
| minimum=0, maximum=1, step=0.1, default=0.5, label="Detection Threshold" | |
| ) | |
| outputs = [ | |
| gr.outputs.Image(type="pil", label="RetinaNet Inference"), | |
| gr.outputs.Textbox(type="number", label="Microalgae Count"), | |
| ] | |
| article = "<p style='text-align: center'><a href='https://dicksonneoh.com/' target='_blank'>Blog post</a></p>" | |
| # Option 1: Get an image from local drive | |
| gr_interface = gr.Interface( | |
| fn=show_preds, | |
| inputs=[ | |
| "image"#, | |
| #display_chkbox_label, | |
| #display_chkbox_box, | |
| #detection_threshold_slider, | |
| ], | |
| outputs=outputs, | |
| title="Microalgae Detector with RetinaNet", | |
| description="This RetinaNet model counts microalgaes on a given image. Upload an image or click an example image below to use.", | |
| article=article, | |
| examples=examples, | |
| ) | |
| # # Option 2: Grab an image from a webcam | |
| # gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=False) | |
| # # Option 3: Continuous image stream from the webcam | |
| # gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=True) | |
| gr_interface.launch() |