Spaces:
Running
Running
File size: 19,295 Bytes
7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f101391 7138195 a95c2e3 f1e245d a95c2e3 7138195 a95c2e3 7a8db87 7138195 f101391 7138195 f101391 7138195 f101391 7138195 f6b1175 7138195 69ff527 013f5dd 7138195 658a947 b7f597e 7138195 51a44df 7138195 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 |
# -*- coding: utf-8 -*-
"""Yet another copy of Final CNN Pose Notebook.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1IdEBDyEyKQdRRT9R-GkfrJINmHdf3_pF
"""
# from google.colab import drive
# drive.mount('/content/drive')
# pip install gradio
import gradio as gr
import torch
from torch.utils.data import DataLoader, Dataset, random_split
from torchvision import transforms, utils
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from PIL import Image
import os
import numpy as np
import json
import matplotlib.pyplot as plt
from torch.utils.data.dataloader import default_collate
# Define the dataset class
# class HumanPoseDataset(Dataset):
# def __init__(self, annotations, img_dir, transform=None):
# self.annotations = annotations
# self.img_dir = img_dir
# self.transform = transform
# def __len__(self):
# return len(self.annotations)
# def __getitem__(self, idx):
# img_key = list(self.annotations.keys())[idx]
# annotation_list = self.annotations[img_key]
# # Skip the image if there are no annotations
# if not annotation_list:
# return None
# # Use the first annotation for simplicity
# annotation = annotation_list[0]
# if not annotation['landmarks']: # Check if landmarks are not empty
# return None
# img_name = os.path.join(self.img_dir, annotation['file'])
# image = Image.open(img_name).convert('RGB')
# original_image_size = image.size
# keypoints = annotation['landmarks']
# keypoints_array = np.array([[k['x'], k['y'], k['z'], k['visibility']] for k in keypoints])
# if self.transform:
# image = self.transform(image)
# sample = {'image': image, 'keypoints': keypoints_array, 'original_image_size': original_image_size}
# print(sample)
# return sample
# # Custom collate function to filter out None values
# def custom_collate(batch):
# batch = [b for b in batch if b is not None]
# return default_collate(batch)
# # Load the annotations JSON into a dictionary
# annotations_path = '/content/drive/MyDrive/annotations_CNN (3).json' # Update this path
# with open(annotations_path) as f:
# annotations_data = json.load(f)
# print("Annotations data loaded. Number of images:", len(annotations_data))
# x = annotations_data.keys()
"""# Do data preprocessing. For example, resize to 32 by 32 and normalization.
"""
# img_dir = '/content/drive/MyDrive/CNN_Dataset'
# # Define the transformations with resizing and augmentation
# transform = transforms.Compose([
# transforms.Resize((32, 32)), # Resize the images to 256x256
# transforms.ToTensor(),
# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
# transforms.RandomHorizontalFlip(), # Example augmentation
# # Add more augmentations if needed
# ])
# test_transform=transforms.Compose([
# transforms.ToTensor(),
# transforms.Resize((32,32)),
# ])
# # Create the dataset
# human_pose_dataset = HumanPoseDataset(annotations_data, img_dir, transform=transform)
# testing_pose_dataset = HumanPoseDataset(annotations_data, img_dir, transform=test_transform)
# print("Dataset created. Length of dataset:", len(human_pose_dataset))
# sorted(x) == sorted(os.listdir('/content/drive/MyDrive/CNN_Dataset'))
"""#2. Load parameters of a pretrained model. If a pretrained model for the entire network is not available, then load parameters for the backbone network/feature extraction network/encoder.
Pose net model is not available so we will be using an architecture similar to PoseNet, a human pose detection CNN architecture. In the above architecture, we are given a brief description about the PoseNet Architecture. We will be using the Regression Network to find the keypoint coordinates.
"""
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
class SimpleCNN(nn.Module):
def __init__(self):
super(SimpleCNN, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.conv4 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
# Assuming the input image size is 256x256, after four pooling layers the image size will be 16x16
self.fc1 = nn.Linear(2 * 16 * 16, 1000)
self.fc2 = nn.Linear(1000, 33 * 4) # Assuming 33 keypoints
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.pool(F.relu(self.conv3(x)))
x = self.pool(F.relu(self.conv4(x)))
x = torch.flatten(x, 1) # Flatten the tensor for the fully connected layer
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
# Initialize the model
model = SimpleCNN()
# print("Model initialized.")
# print(model) # Print the model architecture
#!pip install mediapipe
"""#3 Replace the output layer if necessary and finetune the network for your dataset. Use validation dataset to pick a good learning rate and momentum.
1. Training for a very less samples
"""
# Split the dataset into training, validation, and test sets
# train_size = int(0.04* len(human_pose_dataset))
# validation_size = int(0.1 * len(human_pose_dataset))
# test_size = len(human_pose_dataset) - train_size - validation_size
# train_dataset, remaining_dataset = random_split(human_pose_dataset, [train_size, validation_size + test_size])
# validation_dataset, test_dataset = random_split(remaining_dataset, [validation_size, test_size])
# test_pose_dataset , remaining_data = random_split(testing_pose_dataset,[6,194])
# # Define the batch size
# batch_size = 8
# # Create data loaders for each set with the custom collate function
# train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=custom_collate)
# validation_loader = DataLoader(validation_dataset, batch_size=batch_size, shuffle=False, collate_fn=custom_collate)
# test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, collate_fn=custom_collate)
# test_image_loader = DataLoader(test_pose_dataset, batch_size=batch_size, shuffle=False, collate_fn=custom_collate)
# print("Data loaders created.")
# len(train_dataset)
# # Loss function
# criterion = nn.MSELoss()
# # Optimizer
# optimizer = optim.Adam(model.parameters(), lr=1e-4)
# # Convert the model parameters to float
# model = model.float()
# # Ensure that the tensors are also floats
# sample_batch = next(iter(train_loader))
# #import mediapipe as mp
# images = sample_batch['image'].float() # Convert images to float
# keypoints = sample_batch['keypoints'].view(-1, 132).float() # Convert keypoints to float and reshape
# # Now proceed with the optimization loop
# loss=0
# for epochs in range(10):
# optimizer.zero_grad()
# outputs = model(images)
# loss = criterion(outputs, keypoints)
# loss.backward()
# optimizer.step()
# print("Optimization step completed.")
# print(loss.item())
# loss=loss.item()
# import torch
# def calculate_accuracy(outputs, targets):
# accuracy = torch.mean(torch.abs(outputs - targets))
# return accuracy
# print(outputs.shape)
# # Calculate accuracy
# with torch.no_grad():
# accuracy = calculate_accuracy(outputs, keypoints)
# accuracy= 1- accuracy/132
# print("Loss:", loss)
# print("Accuracy:", accuracy.item()*100, '%')
# """As you can see, the accuracy is very close to 100% (Overfitting)
# Now taking 80-10-10 split on the dataset, we create new train, val and test loaders
# """
# # Split the dataset into training, validation, and test sets
# train_size = int(0.8* len(human_pose_dataset))
# validation_size = int(0.1 * len(human_pose_dataset))
# test_size = len(human_pose_dataset) - train_size - validation_size
# train_dataset, remaining_dataset = random_split(human_pose_dataset, [train_size, validation_size + test_size])
# validation_dataset, test_dataset = random_split(remaining_dataset, [validation_size, test_size])
# test_pose_dataset , remaining_data = random_split(testing_pose_dataset,[6,194])
# # Define the batch size
# batch_size = 8
# # Create data loaders for each set with the custom collate function
# train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=custom_collate)
# validation_loader = DataLoader(validation_dataset, batch_size=batch_size, shuffle=False, collate_fn=custom_collate)
# test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, collate_fn=custom_collate)
# test_image_loader = DataLoader(test_pose_dataset, batch_size=batch_size, shuffle=False, collate_fn=custom_collate)
# print("Data loaders created.")
# len(test_dataset)
# import torch
# import torch.nn as nn
# import torch.optim as optim
# from torch.utils.data import DataLoader, random_split
# from torchvision import transforms
# import torch.nn.functional as F
# class SimpleCNN(nn.Module):
# # Define hyperparameters to search over
# learning_rates = [0.001, 0.01, 0.1]
# momentums = [0.9, 0.95, 0.99]
# weight_decays = [0.0001, 0.001, 0.01]
# best_loss = float('inf')
# best_lr, best_momentum, best_weight_decay = None, None, None
# # Grid search over hyperparameters
# for lr in learning_rates:
# for momentum in momentums:
# for weight_decay in weight_decays:
# # Initialize the model with the current set of hyperparameters
# model = SimpleCNN()
# # Define loss function and optimizer
# criterion = nn.MSELoss()
# optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum, weight_decay=weight_decay)
# # Ensure that the tensors are also floats
# sample_batch = next(iter(train_loader))
# images = sample_batch['image'].float() # Convert images to float
# keypoints = sample_batch['keypoints'].view(-1, 132).float() # Convert keypoints to float and reshape
# # Now proceed with the optimization loop
# optimizer.zero_grad()
# outputs = model(images)
# print("Output shape after forward pass:", outputs.shape)
# outputs = model(images)
# loss = criterion(outputs, keypoints)
# print("Initial loss:", loss.item())
# loss.backward()
# optimizer.step()
# print("Optimization step completed.")
# total_loss = 0
# avg_loss = total_loss / len(train_loader)
# model.train()
# # Check if the current set of hyperparameters resulted in a better performance
# if avg_loss < best_loss:
# best_loss = avg_loss
# best_lr, best_momentum, best_weight_decay = lr, momentum, weight_decay
# # After the grid search, choose the hyperparameters that performed the best
# print("Best Hyperparameters - lr: {}, momentum: {}, weight_decay: {}".format(
# best_lr, best_momentum, best_weight_decay))
# # Train the final model with the selected hyperparameters on the full dataset
# model = SimpleCNN()
# optimizer = optim.SGD(model.parameters(), lr=best_lr, momentum=best_momentum, weight_decay=best_weight_decay)
# """#3. Plotting Validation and Test Loss
# The best parameters are:
# * Learning Rate: 0.001
# * Momentum: 0.9
# * Weight Decay: 0.0001
# """
# import torch
# import matplotlib.pyplot as plt
# # Assuming you have already defined your model, optimizer, and criterion
# # Ensure that the tensors are also floats for training
# sample_batch = next(iter(train_loader))
# images = sample_batch['image'].float()
# keypoints = sample_batch['keypoints'].view(-1, 132).float()
# # Ensure that the tensors are also floats for validation
# validation_sample_batch = next(iter(validation_loader))
# validation_images = validation_sample_batch['image'].float()
# validation_keypoints = validation_sample_batch['keypoints'].view(-1, 132).float()
# # Now proceed with the optimization loop
# optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# criterion = torch.nn.MSELoss()
# train_loss = []
# val_loss = []
# for epoch in range(20):
# model.train()
# optimizer.zero_grad()
# outputs = model(images)
# current_loss = criterion(outputs, keypoints)
# current_loss.backward()
# optimizer.step()
# model.eval() # Switch to evaluation mode for validation
# with torch.no_grad():
# # Calculate validation loss
# val_outputs = model(validation_images)
# val_current_loss = criterion(val_outputs, validation_keypoints)
# print(f"Epoch [{epoch + 1}/100], Loss: {current_loss.item():.4f}, Val Loss: {val_current_loss.item():.4f}")
# train_loss.append(current_loss.item())
# val_loss.append(val_current_loss.item())
# plotting_val_loss = val_loss
# plotting_train_loss = train_loss
# import matplotlib.pyplot as plt
# # Plotting
# plt.figure(figsize=(8, 4))
# plt.plot( plotting_train_loss, marker='o', linestyle='-', color='b',label='train loss')
# plt.plot( plotting_val_loss, marker='o', linestyle= '-', color='r', label='val loss')
# plt.title('Loss vs Epochs')
# plt.xlabel('Epochs')
# plt.ylabel('Loss')
# plt.grid(True)
# plt.legend()
# # Show the legend in a small box
# plt.legend(loc='upper right')
# plt.show()
# """#4. Final Run on Test Dataset"""
# # Ensure that the tensors are also floats
# sample_batch = next(iter(test_loader))
# #import mediapipe as mp
# test_images = sample_batch['image'].float() # Convert images to float
# test_keypoints = sample_batch['keypoints'].view(-1, 132).float() # Convert keypoints to float and reshape
# model.eval()
# optimizer.zero_grad()
# outputs = model(test_images)
# print("Testing Done")
# test_images.shape
# test_actual_plot = test_keypoints.reshape(len(test_images),33,4)[0]
# test_predict_plot = outputs.reshape(len(test_images),33,4)[0]
# test_predict_plot.shape
# """# 4. Finally, evaluate on the test dataset."""
# import cv2
# import matplotlib.pyplot as plt
# import numpy as np
# def plot_human_pose(keypoints):
# # Create a figure and axis
# fig, ax = plt.subplots()
# # Plot keypoints
# for i in range(len(keypoints)):
# x, y, _, _ = keypoints[i]
# ax.scatter(x, -y, color='blue') # Invert y-axis
# # Connect body parts
# connect_lines = [(0, 2), (2, 7), # Left eye
# (0, 5), (5, 8), # Right eye
# (9,10), # Left side
# (11, 12), (12, 24), (11, 23), # Right side
# (24,23), (24,26), (23,25), # Connect ears and wrists
# (26, 28), (25, 27),
# (28, 30), (28, 32), (30,32),# Connect left and right pinky fingers
# (27, 29), (27, 31), (31,29), # Connect left and right index fingers
# (12, 14), (11, 13), # Connect left and right thumbs
# (14, 16), (13, 15), # Connect left and right hips
# (16, 18), (18, 20), (16,20), (16,22), # Connect left and right knees
# (15, 17), (15, 19), # Connect left and right ankles
# (17, 19), (15, 21)] # Connect left and right heels
# for line in connect_lines:
# start, end = line
# x_vals = [keypoints[start][0], keypoints[end][0]]
# y_vals = [-keypoints[start][1], -keypoints[end][1]] # Invert y-axis
# ax.plot(x_vals, y_vals, linewidth=2, color='red')
# ax.set_aspect('equal', adjustable='datalim')
# plt.title('Actual Pose')
# plt.axis('off')
# plt.show()
# # Example usage:
# keypoints = test_actual_plot # Replace with your 33 key points
# plot_human_pose(keypoints)
from io import BytesIO
from PIL import Image
def plot_human_pose(keypoints):
# Create a figure and axis
fig, ax = plt.subplots()
# Plot keypoints
for i in range(len(keypoints)):
x, y, _, _ = keypoints[i]
ax.scatter(x, -y, color='blue') # Invert y-axis
# Connect body parts
connect_lines = [(0, 2), (2, 7), # Left eye
(0, 5), (5, 8), # Right eye
(9,10), # Left side
(11, 12), (12, 24), (11, 23), # Right side
(24,23), (24,26), (23,25), # Connect ears and wrists
(26, 28), (25, 27),
(28, 30), (28, 32), (30,32),# Connect left and right pinky fingers
(27, 29), (27, 31), (31,29), # Connect left and right index fingers
(12, 14), (11, 13), # Connect left and right thumbs
(14, 16), (13, 15), # Connect left and right hips
(16, 18), (18, 20), (16,20), (16,22), # Connect left and right knees
(15, 17), (15, 19), # Connect left and right ankles
(17, 19), (15, 21)] # Connect left and right heels
for line in connect_lines:
start, end = line
x_vals = [keypoints[start][0], keypoints[end][0]]
y_vals = [-keypoints[start][1], -keypoints[end][1]] # Invert y-axis
ax.plot(x_vals, y_vals, linewidth=2, color='green')
ax.set_aspect('equal', adjustable='datalim')
plt.title('Predicted Pose')
plt.axis('off')
buffer = BytesIO()
plt.savefig(buffer, format="png")
buffer.seek(0) # Reset the buffer position to the beginning
# Close the plot to release resources
plt.close()
out = Image.open(buffer)
return out
# Example usage:
# keypoints = test_predict_plot.detach().numpy() # Replace with your 33 key points
# plot_human_pose(keypoints)
"""### As you can see, the model predicts the pose of the person very accurately as depicted by its train and validation accuracy"""
# torch.save(model.state_dict(), '/content/drive/MyDrive/Ayush sarangi/model.pth')
# torch.save( model, '/content/drive/MyDrive/Ayush sarangi/entire_model.pt')
import cv2
from torchvision import transforms, utils
from matplotlib import pyplot as plt
import numpy as np
model = SimpleCNN()
model.load_state_dict(torch.load("model.pth"))
model.eval()
def predict_pose(img):
img= cv2.resize(img, (32,32))
convert_tensor = transforms.ToTensor()
tensor_img = convert_tensor(img)
tensor_img = tensor_img[None,:,:,:]
outputs = model(tensor_img)
pred_keypoints = outputs.reshape(1,33,4)[0]
pred_keypoints = pred_keypoints.detach().numpy()
return plot_human_pose(pred_keypoints)
# predict_pose(test_image)
# input_image = [
# gr.components.Image(type = "pil"),
# ]
# output_image = [
# gr.components.Image(type = "pil"),
# ]
pose_detector = gr.Interface(fn = predict_pose, inputs = gr.Image() , outputs = gr.Image())
pose_detector.launch(share = True)
|