app.py

# -*- 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)