Spaces:
Sleeping
Sleeping
Mehmet Batuhan Duman
commited on
Commit
•
7943976
1
Parent(s):
40d71ff
Changes
Browse files- app.py +99 -12
- model4.pth +3 -0
app.py
CHANGED
@@ -1,6 +1,7 @@
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import cv2
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import numpy as np
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import gradio as gr
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from PIL import Image, ImageOps
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import matplotlib.pyplot as plt
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import torch
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@@ -8,11 +9,63 @@ import torch.nn as nn
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import torch.nn.functional as F
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from torchvision import transforms
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import os
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# Add your model classes (Net and Net2) here.
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# Loading model
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model = None
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model2 = None
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model2_path = "model4.pth"
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@@ -30,23 +83,57 @@ if os.path.exists(model2_path):
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else:
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print("Model file not found at", model2_path)
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# Add the scanmap function here.
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def process_image(
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start_time = time.time()
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heatmap = scanmap(
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elapsed_time = time.time() - start_time
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heatmap_img = Image.fromarray(np.uint8(plt.cm.hot(heatmap) * 255)).convert('RGB')
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heatmap_img = heatmap_img.resize(image.size)
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return heatmap_img, elapsed_time
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outputs = [
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gr.
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gr.
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]
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iface = gr.Interface(fn=
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iface.launch()
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import cv2
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import numpy as np
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import gradio as gr
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from gradio import Interface, Input, Output, Image
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from PIL import Image, ImageOps
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import matplotlib.pyplot as plt
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import torch
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import torch.nn.functional as F
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from torchvision import transforms
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import os
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import time
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import io
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import base64
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class Net2(nn.Module):
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def __init__(self):
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super(Net2, self).__init__()
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self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
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self.bn1 = nn.BatchNorm2d(64)
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self.pool1 = nn.MaxPool2d(2, 2)
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self.dropout1 = nn.Dropout(0.25)
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self.conv2 = nn.Conv2d(64, 64, 3, padding=1)
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self.bn2 = nn.BatchNorm2d(64)
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self.pool2 = nn.MaxPool2d(2, 2)
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self.dropout2 = nn.Dropout(0.25)
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self.conv3 = nn.Conv2d(64, 64, 3, padding=1)
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self.bn3 = nn.BatchNorm2d(64)
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self.pool3 = nn.MaxPool2d(2, 2)
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self.dropout3 = nn.Dropout(0.25)
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self.conv4 = nn.Conv2d(64, 64, 3, padding=1)
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self.bn4 = nn.BatchNorm2d(64)
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self.pool4 = nn.MaxPool2d(2, 2)
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self.dropout4 = nn.Dropout(0.25)
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self.flatten = nn.Flatten()
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self.fc1 = nn.Linear(64 * 5 * 5, 200)
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self.fc2 = nn.Linear(200, 150)
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self.fc3 = nn.Linear(150, 2)
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def forward(self, x):
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x = F.relu(self.bn1(self.conv1(x)))
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x = self.pool1(x)
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x = self.dropout1(x)
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x = F.relu(self.bn2(self.conv2(x)))
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x = self.pool2(x)
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x = self.dropout2(x)
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x = F.relu(self.bn3(self.conv3(x)))
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x = self.pool3(x)
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x = self.dropout3(x)
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x = F.relu(self.bn4(self.conv4(x)))
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x = self.pool4(x)
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x = self.dropout4(x)
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x = self.flatten(x)
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = F.softmax(self.fc3(x), dim=1)
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return x
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model2 = None
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model2_path = "model4.pth"
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else:
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print("Model file not found at", model2_path)
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def process_image(input_image):
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image = Image.open(io.BytesIO(input_image)).convert("RGB")
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start_time = time.time()
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heatmap = scanmap(np.array(image), model)
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elapsed_time = time.time() - start_time
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heatmap_img = Image.fromarray(np.uint8(plt.cm.hot(heatmap) * 255)).convert('RGB')
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heatmap_img = heatmap_img.resize(image.size)
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return image, heatmap_img, int(elapsed_time)
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def scanmap(image_np, model):
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image_np = image_np.astype(np.float32) / 255.0
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window_size = (80, 80)
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stride = 10
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height, width, channels = image_np.shape
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probabilities_map = []
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for y in range(0, height - window_size[1] + 1, stride):
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row_probabilities = []
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for x in range(0, width - window_size[0] + 1, stride):
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cropped_window = image_np[y:y + window_size[1], x:x + window_size[0]]
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cropped_window_torch = transforms.ToTensor()(cropped_window).unsqueeze(0)
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with torch.no_grad():
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probabilities = model(cropped_window_torch)
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row_probabilities.append(probabilities[0, 1].item())
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probabilities_map.append(row_probabilities)
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probabilities_map = np.array(probabilities_map)
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return probabilities_map
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def gradio_process_image(input_image):
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original, heatmap, elapsed_time = process_image(input_image)
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return original, heatmap, f"Elapsed Time (seconds): {elapsed_time}"
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inputs = gr.Image(label="Upload Image")
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outputs = [
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gr.Image(label="Original Image"),
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gr.Image(label="Heatmap"),
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gr.Textbox(label="Elapsed Time")
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]
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iface = gr.Interface(fn=gradio_process_image, inputs=inputs, outputs=outputs)
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iface.launch()
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model4.pth
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:90d2dcf5a7c630275f3238f399b59b5de6da5688bc9dd10c95476cffc675e342
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size 1867947
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