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evaluate.py
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test_data = './data/val'
test_labels = './data/val.csv'
# Download utilities.py
import os
import requests
file_name = "utilities.py"
url = "https://raw.githubusercontent.com/mohiteamit/upGrad-Gesture-Recognition/refs/heads/main/utilities.py"
# Check if the file exists
if not os.path.exists(file_name):
print(f"{file_name} not found. Downloading...")
try:
response = requests.get(url)
if response.ok:
with open(file_name, "wb") as file:
file.write(response.content)
print(f"{file_name} downloaded successfully.")
else:
print(f"Failed to download {file_name}. HTTP Status Code: {response.status_code}")
exit(1)
except Exception as e:
print(f"Error downloading {file_name}: {e}")
exit(1)
import os
import requests
# List of model URLs
model_urls = [
"https://github.com/mohiteamit/upGrad-Gesture-Recognition/raw/refs/heads/main/best-models/Conv2D+GRU.keras",
"https://github.com/mohiteamit/upGrad-Gesture-Recognition/raw/refs/heads/main/best-models/Conv2D+LSTM.keras",
"https://github.com/mohiteamit/upGrad-Gesture-Recognition/raw/refs/heads/main/best-models/Conv3D-32-64-128.keras",
"https://github.com/mohiteamit/upGrad-Gesture-Recognition/raw/refs/heads/main/best-models/pretrained-MobileNetV2+GRU.keras",
"https://github.com/mohiteamit/upGrad-Gesture-Recognition/raw/refs/heads/main/best-models/pretrained-MobileNetV3Small+GRU.keras",
"https://github.com/mohiteamit/upGrad-Gesture-Recognition/raw/refs/heads/main/best-models/pretrained-mediapipe+gru.keras",
]
# Directory to save models
output_dir = "models_to_evaluate"
os.makedirs(output_dir, exist_ok=True)
# Function to verify file integrity
def verify_file(file_path, url):
with open(file_path, 'rb') as f:
local_content = f.read()
response = requests.get(url)
return response.ok and local_content == response.content
# Download models
for url in model_urls:
filename = os.path.join(output_dir, os.path.basename(url))
try:
if not os.path.exists(filename) or not verify_file(filename, url):
response = requests.get(url)
if response.ok:
with open(filename, 'wb') as f:
f.write(response.content)
else:
print(f"Failed to download: {url}")
except Exception as e:
print(f"Error processing {url}: {e}")
print("Models downloaded.")
from utilities import GestureDataGenerator
import tensorflow as tf
from tensorflow.keras.models import load_model
if tf.__version__.startswith("2.10"):
image_size = (120, 120)
test_generator = GestureDataGenerator(
data_path=test_data,
labels_csv=test_labels,
image_size=image_size
)
Conv2D_GRU = load_model('models_to_evaluate/Conv2D+GRU.keras') # Best image size 120x120
Conv2D_GRU.summary()
evaluation_results = Conv2D_GRU.evaluate(test_generator)
for metric, value in zip(Conv2D_GRU.metrics_names, evaluation_results):
print(f"{metric}: {value:.4f}")
else:
print("Conv2D_GRU model requires TensorFlow 2.10.x")
if tf.__version__.startswith("2.10"):
image_size = (120, 120)
test_generator = GestureDataGenerator(
data_path=test_data,
labels_csv=test_labels,
image_size=image_size
)
Conv2D_LSTM = load_model('models_to_evaluate/Conv2D+LSTM.keras')
Conv2D_LSTM.summary()
evaluation_results = Conv2D_LSTM.evaluate(test_generator)
for metric, value in zip(Conv2D_LSTM.metrics_names, evaluation_results):
print(f"{metric}: {value:.4f}")
else:
print("Conv2D_LSTM model requires TensorFlow 2.10.x")
if tf.__version__.startswith("2.10"):
image_size = (200, 200)
test_generator = GestureDataGenerator(
data_path=test_data,
labels_csv=test_labels,
image_size=image_size
)
Conv3D_32_64_128 = load_model('models_to_evaluate/Conv3D-32-64-128.keras')
Conv3D_32_64_128.summary()
evaluation_results = Conv3D_32_64_128.evaluate(test_generator)
for metric, value in zip(Conv3D_32_64_128.metrics_names, evaluation_results):
print(f"{metric}: {value:.4f}")
else:
print("Conv3D_32_64_128 model requires TensorFlow 2.10.x")
if tf.__version__.startswith("2.18"):
image_size = (224, 224)
test_generator = GestureDataGenerator(
data_path=test_data,
labels_csv=test_labels,
image_size=image_size,
use_mediapipe=False
)
MobileNetV2_GRU = load_model('models_to_evaluate/pretrained-MobileNetV2+GRU.keras')
MobileNetV2_GRU.summary()
evaluation_results = MobileNetV2_GRU.evaluate(test_generator)
for metric, value in zip(MobileNetV2_GRU.metrics_names, evaluation_results):
print(f"{metric}: {value:.4f}")
else:
print("MobileNetV2_GRU model requires TensorFlow 2.18.x")
if tf.__version__.startswith("2.18"):
image_size = (224, 224)
test_generator = GestureDataGenerator(
data_path=test_data,
labels_csv=test_labels,
image_size=image_size,
)
MobileNetV3Small_GRU = load_model('models_to_evaluate/pretrained-MobileNetV3Small+GRU.keras')
MobileNetV3Small_GRU.summary()
evaluation_results = MobileNetV3Small_GRU.evaluate(test_generator)
for metric, value in zip(MobileNetV3Small_GRU.metrics_names, evaluation_results):
print(f"{metric}: {value:.4f}")
else:
print("MobileNetV3Small_GRU model requires TensorFlow 2.18.x")
if tf.__version__.startswith("2.18"):
image_size = (256, 256)
test_generator = GestureDataGenerator(
data_path=test_data,
labels_csv=test_labels,
image_size=image_size,
use_mediapipe=True
)
mediapipe_GRU = load_model('models_to_evaluate/pretrained-mediapipe+gru.keras')
mediapipe_GRU.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
mediapipe_GRU.summary()
evaluation_results = mediapipe_GRU.evaluate(test_generator)
for metric, value in zip(mediapipe_GRU.metrics_names, evaluation_results):
print(f"{metric}: {value:.4f}")
else:
print("mediapipe_GRU model requires TensorFlow 2.18.x")