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# takn from: https://huggingface.co/spaces/frgfm/torch-cam/blob/main/app.py
# streamlit run app.py
from io import BytesIO
import os
import sys
import cv2
import matplotlib.pyplot as plt
import numpy as np
import streamlit as st
import torch
from PIL import Image
from torchvision import models
from torchvision.transforms.functional import normalize, resize, to_pil_image, to_tensor
from torchvision import transforms
from torchcam.methods import CAM
from torchcam import methods as torchcam_methods
from torchcam.utils import overlay_mask
import os.path as osp
root_path = osp.abspath(osp.join(__file__, osp.pardir))
sys.path.append(root_path)
from preprocessing.dataset_creation import EyeDentityDatasetCreation
from utils import get_model
from registry_utils import import_registered_modules
import_registered_modules()
# from torchcam.methods._utils import locate_candidate_layer
CAM_METHODS = [
"CAM",
# "GradCAM",
# "GradCAMpp",
# "SmoothGradCAMpp",
# "ScoreCAM",
# "SSCAM",
# "ISCAM",
# "XGradCAM",
# "LayerCAM",
]
TV_MODELS = [
"ResNet18",
"ResNet50",
]
SR_METHODS = ["GFPGAN", "CodeFormer", "RealESRGAN", "SRResNet", "HAT"]
UPSCALE = [2, 4]
UPSCALE_METHODS = ["BILINEAR", "BICUBIC"]
LABEL_MAP = ["left_pupil", "right_pupil"]
@torch.no_grad()
def _load_model(model_configs, device="cpu"):
model_path = os.path.join(root_path, model_configs["model_path"])
model_configs.pop("model_path")
model_dict = torch.load(model_path, map_location=device)
model = get_model(model_configs=model_configs)
model.load_state_dict(model_dict)
model = model.to(device)
model = model.eval()
return model
def main():
# Wide mode
st.set_page_config(page_title="Pupil Diameter Estimator", layout="wide")
# Designing the interface
st.title("EyeDentify Playground")
# For newline
st.write("\n")
# Set the columns
cols = st.columns((1, 1))
# cols = st.columns((1, 1, 1))
cols[0].header("Input image")
# cols[1].header("Raw CAM")
cols[-1].header("Prediction")
# Sidebar
# File selection
st.sidebar.title("Upload Face or Eye")
# Disabling warning
st.set_option("deprecation.showfileUploaderEncoding", False)
# Choose your own image
uploaded_file = st.sidebar.file_uploader(
"Upload Image", type=["png", "jpeg", "jpg"]
)
if uploaded_file is not None:
input_img = Image.open(BytesIO(uploaded_file.read()), mode="r").convert("RGB")
# print("input_img before = ", input_img.size)
max_size = [input_img.size[0], input_img.size[1]]
cols[0].text(f"Input Image: {max_size[0]} x {max_size[1]}")
if input_img.size[0] == input_img.size[1] and input_img.size[0] >= 256:
max_size[0] = 256
max_size[1] = 256
else:
if input_img.size[0] >= 640:
max_size[0] = 640
elif input_img.size[0] < 64:
max_size[0] = 64
if input_img.size[1] >= 480:
max_size[1] = 480
elif input_img.size[1] < 32:
max_size[1] = 32
input_img.thumbnail((max_size[0], max_size[1])) # Bicubic resampling
# print("input_img after = ", input_img.size)
# cols[0].image(input_img)
fig0, axs0 = plt.subplots(1, 1, figsize=(10, 10))
# Display the input image
axs0.imshow(input_img)
axs0.axis("off")
axs0.set_title("Input Image")
# Display the plot
cols[0].pyplot(fig0)
cols[0].text(f"Input Image Resized: {max_size[0]} x {max_size[1]}")
st.sidebar.title("Setup")
# Upscale selection
upscale = "-"
# upscale = st.sidebar.selectbox(
# "Upscale",
# ["-"] + UPSCALE,
# help="Upscale the uploaded image 2 or 4 times. Keep blank for no upscaling",
# )
# Upscale method selection
if upscale != "-":
upscale_method_or_model = st.sidebar.selectbox(
"Upscale Method / Model",
UPSCALE_METHODS + SR_METHODS,
help="Select a method or model to upscale the uploaded image",
)
else:
upscale_method_or_model = None
# Pupil selection
pupil_selection = st.sidebar.selectbox(
"Pupil Selection",
["-"] + LABEL_MAP,
help="Select left or right pupil OR keep blank for both pupil diameter estimation",
)
# Model selection
tv_model = st.sidebar.selectbox(
"Classification model",
TV_MODELS,
help="Supported Models for Pupil Diameter Estimation",
)
cam_method = "CAM"
# cam_method = st.sidebar.selectbox(
# "CAM method",
# CAM_METHODS,
# help="The way your class activation map will be computed",
# )
# target_layer = st.sidebar.text_input(
# "Target layer",
# default_layer,
# help='If you want to target several layers, add a "+" separator (e.g. "layer3+layer4")',
# )
st.sidebar.write("\n")
if st.sidebar.button("Predict Diameter & Compute CAM"):
if uploaded_file is None:
st.sidebar.error("Please upload an image first")
else:
with st.spinner("Analyzing..."):
if upscale == "-":
sr_configs = None
else:
sr_configs = {
"method": upscale_method_or_model,
"params": {"upscale": upscale},
}
config_file = {
"sr_configs": sr_configs,
"feature_extraction_configs": {
"blink_detection": False,
"upscale": upscale,
"extraction_library": "mediapipe",
},
}
img = np.array(input_img)
# img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
# if img.shape[0] > max_size or img.shape[1] > max_size:
# img = cv2.resize(img, (max_size, max_size))
ds_results = EyeDentityDatasetCreation(
feature_extraction_configs=config_file[
"feature_extraction_configs"
],
sr_configs=config_file["sr_configs"],
)(img)
# if ds_results is not None:
# print("ds_results = ", ds_results.keys())
preprocess_steps = [
transforms.ToTensor(),
transforms.Resize(
[32, 64],
# interpolation=transforms.InterpolationMode.BILINEAR,
interpolation=transforms.InterpolationMode.BICUBIC,
antialias=True,
),
]
preprocess_function = transforms.Compose(preprocess_steps)
left_eye = None
right_eye = None
if ds_results is None:
# print("type of input_img = ", type(input_img))
input_img = preprocess_function(input_img)
input_img = input_img.unsqueeze(0)
if pupil_selection == "left_pupil":
left_eye = input_img
elif pupil_selection == "right_pupil":
right_eye = input_img
else:
left_eye = input_img
right_eye = input_img
# print("type of left_eye = ", type(left_eye))
# print("type of right_eye = ", type(right_eye))
elif "eyes" in ds_results.keys():
if (
"left_eye" in ds_results["eyes"].keys()
and ds_results["eyes"]["left_eye"] is not None
):
left_eye = ds_results["eyes"]["left_eye"]
# print("type of left_eye = ", type(left_eye))
left_eye = to_pil_image(left_eye).convert("RGB")
# print("type of left_eye = ", type(left_eye))
left_eye = preprocess_function(left_eye)
# print("type of left_eye = ", type(left_eye))
left_eye = left_eye.unsqueeze(0)
if (
"right_eye" in ds_results["eyes"].keys()
and ds_results["eyes"]["right_eye"] is not None
):
right_eye = ds_results["eyes"]["right_eye"]
# print("type of right_eye = ", type(right_eye))
right_eye = to_pil_image(right_eye).convert("RGB")
# print("type of right_eye = ", type(right_eye))
right_eye = preprocess_function(right_eye)
# print("type of right_eye = ", type(right_eye))
right_eye = right_eye.unsqueeze(0)
else:
# print("type of input_img = ", type(input_img))
input_img = preprocess_function(input_img)
input_img = input_img.unsqueeze(0)
if pupil_selection == "left_pupil":
left_eye = input_img
elif pupil_selection == "right_pupil":
right_eye = input_img
else:
left_eye = input_img
right_eye = input_img
# print("type of left_eye = ", type(left_eye))
# print("type of right_eye = ", type(right_eye))
# print("left_eye = ", left_eye.shape)
# print("right_eye = ", right_eye.shape)
if pupil_selection == "-":
selected_eyes = ["left_eye", "right_eye"]
elif pupil_selection == "left_pupil":
selected_eyes = ["left_eye"]
elif pupil_selection == "right_pupil":
selected_eyes = ["right_eye"]
for eye_type in selected_eyes:
model_configs = {
"model_path": root_path
+ f"/pre_trained_models/{tv_model}/{eye_type}.pt",
"registered_model_name": tv_model,
"num_classes": 1,
}
registered_model_name = model_configs["registered_model_name"]
model = _load_model(model_configs)
if registered_model_name == "ResNet18":
target_layer = model.resnet.layer4[-1].conv2
elif registered_model_name == "ResNet50":
target_layer = model.resnet.layer4[-1].conv3
else:
raise Exception(
f"No target layer available for selected model: {registered_model_name}"
)
if left_eye is not None and eye_type == "left_eye":
input_img = left_eye
elif right_eye is not None and eye_type == "right_eye":
input_img = right_eye
else:
raise Exception("Wrong Data")
if cam_method is not None:
cam_extractor = torchcam_methods.__dict__[cam_method](
model,
target_layer=target_layer,
fc_layer=model.resnet.fc,
input_shape=input_img.shape,
)
# with torch.no_grad():
out = model(input_img)
cols[-1].markdown(
f"<h3>Predicted Pupil Diameter: {out[0].item():.2f} mm</h3>",
unsafe_allow_html=True,
)
# cols[-1].text(f"Predicted Pupil Diameter: {out[0].item():.2f}")
# Retrieve the CAM
act_maps = cam_extractor(0, out)
# Fuse the CAMs if there are several
activation_map = (
act_maps[0]
if len(act_maps) == 1
else cam_extractor.fuse_cams(act_maps)
)
# Convert input image and activation map to PIL images
input_image_pil = to_pil_image(input_img.squeeze(0))
activation_map_pil = to_pil_image(activation_map, mode="F")
# Create the overlayed CAM result
result = overlay_mask(
input_image_pil,
activation_map_pil,
alpha=0.5,
)
# Create a subplot with 1 row and 2 columns
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
# Display the input image
axs[0].imshow(input_image_pil)
axs[0].axis("off")
axs[0].set_title("Input Image")
# Display the overlayed CAM result
axs[1].imshow(result)
axs[1].axis("off")
axs[1].set_title("Overlayed CAM")
# Display the plot
cols[-1].pyplot(fig)
cols[-1].text(
f"eye image size: {input_img.shape[-1]} x {input_img.shape[-2]}"
)
if __name__ == "__main__":
main()
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