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import cv2
import numpy as np
import gradio as gr
from PIL import Image, ImageOps
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
import os
import time
import io
import base64
class Net2(nn.Module):
def __init__(self):
super(Net2, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.pool1 = nn.MaxPool2d(2, 2)
self.dropout1 = nn.Dropout(0.25)
self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
self.bn2 = nn.BatchNorm2d(64)
self.pool2 = nn.MaxPool2d(2, 2)
self.dropout2 = nn.Dropout(0.25)
self.conv3 = nn.Conv2d(64, 64, 3, padding=1)
self.bn3 = nn.BatchNorm2d(64)
self.pool3 = nn.MaxPool2d(2, 2)
self.dropout3 = nn.Dropout(0.25)
self.conv4 = nn.Conv2d(64, 64, 3, padding=1)
self.bn4 = nn.BatchNorm2d(64)
self.pool4 = nn.MaxPool2d(2, 2)
self.dropout4 = nn.Dropout(0.25)
self.flatten = nn.Flatten()
self.fc1 = nn.Linear(64 * 5 * 5, 200)
self.fc2 = nn.Linear(200, 150)
self.fc3 = nn.Linear(150, 2)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = self.pool1(x)
x = self.dropout1(x)
x = F.relu(self.bn2(self.conv2(x)))
x = self.pool2(x)
x = self.dropout2(x)
x = F.relu(self.bn3(self.conv3(x)))
x = self.pool3(x)
x = self.dropout3(x)
x = F.relu(self.bn4(self.conv4(x)))
x = self.pool4(x)
x = self.dropout4(x)
x = self.flatten(x)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.softmax(self.fc3(x), dim=1)
return x
model2 = None
model2_path = "model4.pth"
if os.path.exists(model2_path):
state_dict = torch.load(model2_path, map_location=torch.device('cpu'))
new_state_dict = {}
for key, value in state_dict.items():
new_key = key.replace("module.", "")
new_state_dict[new_key] = value
model = Net2()
model.load_state_dict(new_state_dict)
model.eval()
else:
print("Model file not found at", model2_path)
def process_image(input_image):
image = Image.open(io.BytesIO(input_image)).convert("RGB")
start_time = time.time()
heatmap = scanmap(np.array(image), model)
elapsed_time = time.time() - start_time
heatmap_img = Image.fromarray(np.uint8(plt.cm.hot(heatmap) * 255)).convert('RGB')
heatmap_img = heatmap_img.resize(image.size)
return image, heatmap_img, int(elapsed_time)
def scanmap(image_np, model):
image_np = image_np.astype(np.float32) / 255.0
window_size = (80, 80)
stride = 10
height, width, channels = image_np.shape
probabilities_map = []
for y in range(0, height - window_size[1] + 1, stride):
row_probabilities = []
for x in range(0, width - window_size[0] + 1, stride):
cropped_window = image_np[y:y + window_size[1], x:x + window_size[0]]
cropped_window_torch = transforms.ToTensor()(cropped_window).unsqueeze(0)
with torch.no_grad():
probabilities = model(cropped_window_torch)
row_probabilities.append(probabilities[0, 1].item())
probabilities_map.append(row_probabilities)
probabilities_map = np.array(probabilities_map)
return probabilities_map
def gradio_process_image(input_image):
original, heatmap, elapsed_time = process_image(input_image.read())
return original, heatmap, f"Elapsed Time (seconds): {elapsed_time}"
inputs = gr.Image(label="Upload Image")
outputs = [
gr.Image(label="Original Image"),
gr.Image(label="Heatmap"),
gr.Textbox(label="Elapsed Time")
]
iface = gr.Interface(fn=gradio_process_image, inputs=inputs, outputs=outputs)
iface.launch()
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