Spaces:
Running
Running
File size: 4,647 Bytes
b485e09 f9f1c14 b485e09 14abc40 b485e09 2057317 b485e09 2057317 d2ad9ac 14abc40 2057317 f9f1c14 b485e09 d2ad9ac |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
# app.py
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import torchvision.transforms as T
from PIL import Image, ImageDraw, ImageFont
import gradio as gr
from ultralytics import YOLO
from transformers import ResNetModel
import cv2
from huggingface_hub import hf_hub_download
class FlakeLayerClassifier(nn.Module):
def __init__(self, num_materials, material_dim, num_classes=2, dropout_prob=0.1, freeze_cnn=False):
super().__init__()
self.cnn = ResNetModel.from_pretrained("microsoft/resnet-18")
if freeze_cnn:
for p in self.cnn.parameters():
p.requires_grad = False
img_feat_dim = self.cnn.config.hidden_sizes[-1]
self.material_embedding = nn.Embedding(num_materials, material_dim)
self.dropout = nn.Dropout(dropout_prob)
self.fc_img = nn.Sequential(
nn.Linear(img_feat_dim, img_feat_dim),
nn.ReLU(inplace=True),
self.dropout,
nn.Linear(img_feat_dim, num_classes)
)
combined_dim = img_feat_dim + material_dim
self.fc_comb = nn.Sequential(
nn.Linear(combined_dim, combined_dim),
nn.ReLU(inplace=True),
self.dropout,
nn.Linear(combined_dim, num_classes)
)
def forward(self, pixel_values, material=None):
outputs = self.cnn(pixel_values=pixel_values)
img_feats = outputs.pooler_output.view(outputs.pooler_output.size(0), -1)
if material is None:
return self.fc_img(img_feats)
mat_emb = self.material_embedding(material)
combined = torch.cat([img_feats, mat_emb], dim=1)
return self.fc_comb(combined)
def calibration(source_img, target_img):
source_lab = cv2.cvtColor(source_img, cv2.COLOR_BGR2LAB)
target_lab = cv2.cvtColor(target_img, cv2.COLOR_BGR2LAB)
for i in range(3):
src_mean, src_std = cv2.meanStdDev(source_lab[:, :, i])
tgt_mean, tgt_std = cv2.meanStdDev(target_lab[:, :, i])
target_lab[:, :, i] = (
(target_lab[:, :, i] - tgt_mean) * (src_std / tgt_std) + src_mean
).clip(0, 255)
corrected_img = cv2.cvtColor(target_lab, cv2.COLOR_LAB2BGR)
return corrected_img.astype(np.uint8)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
# Load YOLO detector
yolo = YOLO("models/uark_detector_v3.pt")
# Load classifier model checkpoint
ckpt_path = "models/flake_monolayer_classifier.pth"
ckpt = torch.load(ckpt_path, map_location=device)
num_classes = len(ckpt["class_to_idx"])
classifier = FlakeLayerClassifier(
num_materials=num_classes,
material_dim=64,
num_classes=num_classes,
dropout_prob=0.1,
freeze_cnn=False
).to(device)
classifier.load_state_dict(ckpt["model_state_dict"])
classifier.eval()
# Image processing transforms
clf_tf = T.Compose([
T.Resize((224, 224)),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
try:
FONT = ImageFont.truetype("arial.ttf", 20)
except IOError:
FONT = ImageFont.load_default()
def detect_and_classify(image: Image.Image):
#image = calibration(
# np.array(Image.open("/home/sankalp/gradio_flake_app/quantum-flake-pipeline/template/image.png")),
#np.array(image.convert("RGB")),
#)
#image = Image.fromarray(image)
img_rgb = np.array(image.convert("RGB"))
img_bgr = img_rgb[:, :, ::-1]
results = yolo(img_bgr, device=str(device))
boxes = results[0].boxes.xyxy.cpu().numpy()
scores = results[0].boxes.conf.cpu().numpy()
draw = ImageDraw.Draw(image)
for (x1, y1, x2, y2), conf in zip(boxes, scores):
crop = image.crop((x1, y1, x2, y2))
inp = clf_tf(crop).unsqueeze(0).to(device) # (1,C,H,W)
with torch.no_grad():
logits = classifier(pixel_values=inp)
pred = logits.argmax(1).item()
prob = F.softmax(logits, dim=1)[0, pred].item()
label = f"Layer {pred+1} ({prob:.2f})"
draw.rectangle([x1, y1, x2, y2], outline="red", width=2)
draw.text((x1, max(0, y1-18)), label, fill="red", font=FONT)
return image
demo = gr.Interface(
fn=detect_and_classify,
inputs=gr.Image(type="pil", label="Upload Flake Image"),
outputs=gr.Image(type="pil", label="Annotated Output"),
title="Flake Detection + Layer Classification",
description="Upload an image → YOLO finds flakes → ResNet-18 head classifies their layer.",
)
if __name__ == "__main__":
demo.launch(share=True) |