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"""
ISL Sign Language Translation - TechMatrix Solvers Initiative
Core ISL Processing and Translation Models
Developed by: TechMatrix Solvers Team
- Abhay Gupta (Team Lead)
- Kripanshu Gupta (Backend Developer)
- Dipanshu Patel (UI/UX Designer)
- Bhumika Patel (Deployment & Female Presenter)
Institution: Shri Ram Group of Institutions
"""
import keras
import numpy as np
import cv2
import torch
try:
from scipy.ndimage.filters import gaussian_filter
except ImportError:
from scipy.ndimage import gaussian_filter
import math
import os
from skimage.measure import label
import pose_utils as utils
# Simple TorchModuleWrapper replacement for compatibility
class TorchModuleWrapper:
"""
Simple wrapper to make PyTorch models compatible with Keras-style usage
"""
def __init__(self, torch_model):
self.torch_model = torch_model
self.trainable = False
def __call__(self, x):
"""Forward pass through the PyTorch model"""
return self.torch_model(x)
def eval(self):
"""Set model to evaluation mode"""
if hasattr(self.torch_model, 'eval'):
self.torch_model.eval()
def train(self, mode=True):
"""Set model to train mode"""
if hasattr(self.torch_model, 'train'):
self.torch_model.train(mode)
class ISLPoseEstimator(keras.Model):
"""
ISL Pose Estimation Model combining body and hand pose detection
Developed by TechMatrix Solvers for accurate sign language recognition
"""
def __init__(self, pytorch_body_model, pytorch_hand_model):
super().__init__()
self.pytorch_body_wrapper = TorchModuleWrapper(pytorch_body_model)
self.pytorch_body_wrapper.trainable = False
self.pytorch_hand_wrapper = TorchModuleWrapper(pytorch_hand_model)
self.pytorch_hand_wrapper.trainable = False
self.num_body_joints = 26
self.num_body_pafs = 52
def call(self, input_image):
"""
Process input image and extract pose information
Args:
input_image: Input image tensor
Returns:
tuple: (body_candidates, body_subset, hand_peaks)
"""
candidate, subset = self.extract_body_pose(input_image.cpu().numpy())
hand_regions = utils.detect_hand_regions(candidate, subset, input_image.cpu().numpy())
all_hand_keypoints = []
for x, y, w, is_left in hand_regions:
hand_peaks = self.extract_hand_pose(input_image.cpu().numpy()[y:y+w, x:x+w, :])
hand_peaks[:, 0] = np.where(hand_peaks[:, 0] == 0, hand_peaks[:, 0], hand_peaks[:, 0] + x)
hand_peaks[:, 1] = np.where(hand_peaks[:, 1] == 0, hand_peaks[:, 1], hand_peaks[:, 1] + y)
all_hand_keypoints.append(hand_peaks)
return candidate, subset, all_hand_keypoints
def extract_body_pose(self, input_image):
"""
Extract body pose keypoints from input image
Args:
input_image: Input image array
Returns:
tuple: (candidates, subset) containing pose information
"""
model_type = 'body25'
scale_factors = [0.5]
box_size = 368
stride = 8
padding_value = 128
threshold_1 = 0.1
threshold_2 = 0.05
# Calculate scale multipliers
multiplier = [x * box_size / input_image.shape[0] for x in scale_factors]
heatmap_average = np.zeros((input_image.shape[0], input_image.shape[1], self.num_body_joints))
paf_average = np.zeros((input_image.shape[0], input_image.shape[1], self.num_body_pafs))
for m in range(len(multiplier)):
scale = multiplier[m]
test_image = cv2.resize(input_image, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
padded_image, pad = utils.pad_image_corner(test_image, stride, padding_value)
# Prepare image tensor
image_tensor = np.transpose(np.float32(padded_image[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
image_tensor = np.ascontiguousarray(image_tensor)
# Convert to PyTorch tensor
data = torch.from_numpy(image_tensor).float()
if torch.cuda.is_available():
data = data.cuda()
with torch.no_grad():
stage6_L1, stage6_L2 = self.pytorch_body_wrapper(data)
stage6_L1 = stage6_L1.cpu().numpy()
stage6_L2 = stage6_L2.cpu().numpy()
# Process heatmaps
heatmap = np.transpose(np.squeeze(stage6_L2), (1, 2, 0))
heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
heatmap = heatmap[:padded_image.shape[0] - pad[2], :padded_image.shape[1] - pad[3], :]
heatmap = cv2.resize(heatmap, (input_image.shape[1], input_image.shape[0]), interpolation=cv2.INTER_CUBIC)
# Process PAFs (Part Affinity Fields)
paf = np.transpose(np.squeeze(stage6_L1), (1, 2, 0))
paf = cv2.resize(paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
paf = paf[:padded_image.shape[0] - pad[2], :padded_image.shape[1] - pad[3], :]
paf = cv2.resize(paf, (input_image.shape[1], input_image.shape[0]), interpolation=cv2.INTER_CUBIC)
heatmap_average += heatmap / len(multiplier)
paf_average += paf / len(multiplier)
# Extract peaks from heatmaps
all_peaks = []
peak_counter = 0
for part in range(self.num_body_joints - 1):
original_map = heatmap_average[:, :, part]
smoothed_heatmap = gaussian_filter(original_map, sigma=3)
# Find local maxima
left_map = np.zeros(smoothed_heatmap.shape)
left_map[1:, :] = smoothed_heatmap[:-1, :]
right_map = np.zeros(smoothed_heatmap.shape)
right_map[:-1, :] = smoothed_heatmap[1:, :]
up_map = np.zeros(smoothed_heatmap.shape)
up_map[:, 1:] = smoothed_heatmap[:, :-1]
down_map = np.zeros(smoothed_heatmap.shape)
down_map[:, :-1] = smoothed_heatmap[:, 1:]
peaks_binary = np.logical_and.reduce(
(smoothed_heatmap >= left_map, smoothed_heatmap >= right_map,
smoothed_heatmap >= up_map, smoothed_heatmap >= down_map,
smoothed_heatmap > threshold_1)
)
peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0]))
peaks_with_score = [x + (original_map[x[1], x[0]],) for x in peaks]
peak_id = range(peak_counter, peak_counter + len(peaks))
peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))]
all_peaks.append(peaks_with_score_and_id)
peak_counter += len(peaks)
# Define limb connections for body25 model
if model_type == 'body25':
limb_sequence = [
[1,0],[1,2],[2,3],[3,4],[1,5],[5,6],[6,7],[1,8],[8,9],[9,10],
[10,11],[8,12],[12,13],[13,14],[0,15],[0,16],[15,17],[16,18],
[11,24],[11,22],[14,21],[14,19],[22,23],[19,20]
]
map_index = [
[30,31],[14,15],[16,17],[18,19],[22,23],[24,25],[26,27],[0,1],[6,7],
[2,3],[4,5],[8,9],[10,11],[12,13],[32,33],[34,35],[36,37],[38,39],
[50,51],[46,47],[44,45],[40,41],[48,49],[42,43]
]
# Find connections between body parts
connection_all = []
special_k = []
mid_num = 10
for k in range(len(map_index)):
score_mid = paf_average[:, :, map_index[k]]
candA = all_peaks[limb_sequence[k][0]]
candB = all_peaks[limb_sequence[k][1]]
nA = len(candA)
nB = len(candB)
indexA, indexB = limb_sequence[k]
if nA != 0 and nB != 0:
connection_candidate = []
for i in range(nA):
for j in range(nB):
vec = np.subtract(candB[j][:2], candA[i][:2])
norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
norm = max(0.001, norm)
vec = np.divide(vec, norm)
startend = list(zip(
np.linspace(candA[i][0], candB[j][0], num=mid_num),
np.linspace(candA[i][1], candB[j][1], num=mid_num)
))
vec_x = np.array([
score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0]
for I in range(len(startend))
])
vec_y = np.array([
score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1]
for I in range(len(startend))
])
score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
score_with_dist_prior = (sum(score_midpts) / len(score_midpts) +
min(0.5 * input_image.shape[0] / norm - 1, 0))
criterion1 = len(np.nonzero(score_midpts > threshold_2)[0]) > 0.8 * len(score_midpts)
criterion2 = score_with_dist_prior > 0
if criterion1 and criterion2:
connection_candidate.append([
i, j, score_with_dist_prior,
score_with_dist_prior + candA[i][2] + candB[j][2]
])
connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
connection = np.zeros((0, 5))
for c in range(len(connection_candidate)):
i, j, s = connection_candidate[c][0:3]
if i not in connection[:, 3] and j not in connection[:, 4]:
connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
if len(connection) >= min(nA, nB):
break
connection_all.append(connection)
else:
special_k.append(k)
connection_all.append([])
# Create human pose subsets
subset = -1 * np.ones((0, self.num_body_joints + 1))
candidate = np.array([item for sublist in all_peaks for item in sublist])
for k in range(len(map_index)):
if k not in special_k:
partAs = connection_all[k][:, 0]
partBs = connection_all[k][:, 1]
indexA, indexB = np.array(limb_sequence[k])
for i in range(len(connection_all[k])):
found = 0
subset_idx = [-1, -1]
for j in range(len(subset)):
if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
subset_idx[found] = j
found += 1
if found == 1:
j = subset_idx[0]
if subset[j][indexB] != partBs[i]:
subset[j][indexB] = partBs[i]
subset[j][-1] += 1
subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
elif found == 2:
j1, j2 = subset_idx
membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2]
if len(np.nonzero(membership == 2)[0]) == 0:
subset[j1][:-2] += (subset[j2][:-2] + 1)
subset[j1][-2:] += subset[j2][-2:]
subset[j1][-2] += connection_all[k][i][2]
subset = np.delete(subset, j2, 0)
else:
subset[j1][indexB] = partBs[i]
subset[j1][-1] += 1
subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
elif not found and k < self.num_body_joints - 2:
row = -1 * np.ones(self.num_body_joints + 1)
row[indexA] = partAs[i]
row[indexB] = partBs[i]
row[-1] = 2
row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2]
subset = np.vstack([subset, row])
# Filter out low-quality detections
deleteIdx = []
for i in range(len(subset)):
if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
deleteIdx.append(i)
subset = np.delete(subset, deleteIdx, axis=0)
return candidate, subset
def extract_hand_pose(self, input_image):
"""
Extract hand pose keypoints from input image region
Args:
input_image: Cropped hand region image
Returns:
numpy.ndarray: Hand keypoint coordinates
"""
scale_factors = [0.5, 1.0, 1.5, 2.0]
box_size = 368
stride = 8
padding_value = 128
threshold = 0.05
multiplier = [x * box_size / input_image.shape[0] for x in scale_factors]
heatmap_average = np.zeros((input_image.shape[0], input_image.shape[1], 22))
for m in range(len(multiplier)):
scale = multiplier[m]
test_image = cv2.resize(input_image, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
padded_image, pad = utils.pad_image_corner(test_image, stride, padding_value)
# Prepare image tensor
image_tensor = np.transpose(np.float32(padded_image[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
image_tensor = np.ascontiguousarray(image_tensor)
data = torch.from_numpy(image_tensor).float()
if torch.cuda.is_available():
data = data.cuda()
with torch.no_grad():
output = self.pytorch_hand_wrapper(data).cpu().numpy()
# Process heatmaps
heatmap = np.transpose(np.squeeze(output), (1, 2, 0))
heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC)
heatmap = heatmap[:padded_image.shape[0] - pad[2], :padded_image.shape[1] - pad[3], :]
heatmap = cv2.resize(heatmap, (input_image.shape[1], input_image.shape[0]), interpolation=cv2.INTER_CUBIC)
heatmap_average += heatmap / len(multiplier)
# Extract hand keypoints
all_peaks = []
for part in range(21):
original_map = heatmap_average[:, :, part]
smoothed_heatmap = gaussian_filter(original_map, sigma=3)
binary = np.ascontiguousarray(smoothed_heatmap > threshold, dtype=np.uint8)
if np.sum(binary) == 0:
all_peaks.append([0, 0])
continue
label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim)
max_index = np.argmax([np.sum(original_map[label_img == i]) for i in range(1, label_numbers + 1)]) + 1
label_img[label_img != max_index] = 0
original_map[label_img == 0] = 0
y, x = utils.find_array_maximum(original_map)
all_peaks.append([x, y])
return np.array(all_peaks)
class ISLTranslationModel(keras.Model):
"""
Complete ISL Translation Model combining pose estimation and LSTM translation
Developed by TechMatrix Solvers for end-to-end sign language translation
"""
def __init__(self, body_model, hand_model, translation_model):
super().__init__()
self.pytorch_body_wrapper = TorchModuleWrapper(body_model)
self.pytorch_body_wrapper.trainable = False
self.pytorch_hand_wrapper = TorchModuleWrapper(hand_model)
self.pytorch_hand_wrapper.trainable = False
self.num_body_joints = 26
self.num_body_pafs = 52
self.model_type = 'body25'
self.translation_network = translation_model
def call(self, frame_sequence):
"""
Process a sequence of frames and return translation prediction
Args:
frame_sequence: Sequence of video frames
Returns:
Translation prediction probabilities
"""
window_size = 20
feature_sequence = []
blank_frame = np.zeros((1, 156))
for idx, frame in enumerate(frame_sequence.cpu()):
# Extract pose features from current frame
candidate, subset = self.extract_body_pose(frame.cpu().numpy())
hand_regions = utils.detect_hand_regions(candidate, subset, frame.cpu().numpy())
all_hand_keypoints = []
for x, y, w, is_left in hand_regions:
peaks = self.extract_hand_pose(frame.cpu().numpy()[y:y+w, x:x+w, :])
peaks[:, 0] = np.where(peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x)
peaks[:, 1] = np.where(peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y)
all_hand_keypoints.append(peaks)
# Extract structured pose data
body_circles, body_sticks = utils.extract_body_pose_data(candidate, subset, self.model_type)
hand_edges, hand_peaks = utils.extract_hand_pose_data(all_hand_keypoints)
# Convert to feature vector
feature_vector = self.create_feature_vector(body_circles, hand_peaks)
feature_sequence.append(feature_vector)
# Pad sequence if needed
if len(feature_sequence) < window_size:
for _ in range(window_size - len(feature_sequence)):
feature_sequence.append(blank_frame)
# Run translation model
return self.translation_network(np.array(feature_sequence).reshape(1, 20, 156))
def create_feature_vector(self, body_circles, hand_peaks):
"""
Create feature vector from pose data
Args:
body_circles: Body keypoint coordinates
hand_peaks: Hand keypoint data
Returns:
numpy.ndarray: 156-dimensional feature vector
"""
features = []
# Body keypoint x-coordinates (15 points)
for idx in range(15):
if idx < len(body_circles):
features.append(body_circles[idx][0])
else:
features.append(0)
# Body keypoint y-coordinates (15 points)
for idx in range(15):
if idx < len(body_circles):
features.append(body_circles[idx][1])
else:
features.append(0)
# Hand features for both hands
for hand_idx in range(2):
# Hand x-coordinates (21 points)
for idx in range(21):
if idx < len(hand_peaks[hand_idx]):
features.append(float(hand_peaks[hand_idx][idx][0]))
else:
features.append(0)
# Hand y-coordinates (21 points)
for idx in range(21):
if idx < len(hand_peaks[hand_idx]):
features.append(float(hand_peaks[hand_idx][idx][1]))
else:
features.append(0)
# Hand peak text/confidence (21 points)
for idx in range(21):
if idx < len(hand_peaks[hand_idx]):
features.append(float(hand_peaks[hand_idx][idx][2]))
else:
features.append(0)
return np.array(features)
def extract_body_pose(self, input_image):
"""Extract body pose - same implementation as ISLPoseEstimator"""
# This method would contain the same implementation as in ISLPoseEstimator
# For brevity, using a placeholder that calls the same logic
pose_estimator = ISLPoseEstimator(None, None)
pose_estimator.pytorch_body_wrapper = self.pytorch_body_wrapper
pose_estimator.num_body_joints = self.num_body_joints
pose_estimator.num_body_pafs = self.num_body_pafs
return pose_estimator.extract_body_pose(input_image)
def extract_hand_pose(self, input_image):
"""Extract hand pose - same implementation as ISLPoseEstimator"""
# This method would contain the same implementation as in ISLPoseEstimator
# For brevity, using a placeholder that calls the same logic
pose_estimator = ISLPoseEstimator(None, None)
pose_estimator.pytorch_hand_wrapper = self.pytorch_hand_wrapper
return pose_estimator.extract_hand_pose(input_image) |