Spaces:
Runtime error
Runtime error
File size: 3,847 Bytes
51a3865 0f07c40 1398914 59f67e0 1398914 59f67e0 1398914 59f67e0 0f07c40 59f67e0 1398914 59f67e0 1398914 59f67e0 1398914 0f07c40 1398914 0f07c40 1398914 0f07c40 1398914 0f07c40 1398914 0f07c40 1398914 0f07c40 1398914 0f07c40 1398914 0f07c40 1398914 0f07c40 1398914 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
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(filename)
examples = ["sample_images/" + file for file in files]
article = "<p style='text-align: center'><a href='https://dicksonneoh.com/' target='_blank'>Blog post</a></p>"
enable_queue = True
# 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, 0.5],
[example_images[7], False, True, 0.5],
]
def show_preds(input_image, display_label, display_bbox, detection_threshold):
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=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')
]
# 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(inline=False, share=True, debug=True)
|