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import gradio as gr
import cv2
import mediapipe as mp
import numpy as np
# Bounding Box
def box_yolo(image, only_people):
def get_output_layers(net):
layer_names = net.getLayerNames()
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
return output_layers
def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
label = str(classes[class_id])
color = COLORS[class_id]
cv2.rectangle(img, (x,y), (x_plus_w,y_plus_h), color, 2)
cv2.putText(img, label, (x-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
Width = image.shape[1]
Height = image.shape[0]
scale = 0.00392
classes = None
with open('yolov3.txt', 'r') as f:
classes = [line.strip() for line in f.readlines()]
COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
blob = cv2.dnn.blobFromImage(image, scale, (416,416), (0,0,0), True, crop=False)
net.setInput(blob)
outs = net.forward(get_output_layers(net))
class_ids = []
confidences = []
boxes = []
conf_threshold = 0.5
nms_threshold = 0.4
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
center_x = int(detection[0] * Width)
center_y = int(detection[1] * Height)
w = int(detection[2] * Width)
h = int(detection[3] * Height)
x = center_x - w / 2
y = center_y - h / 2
class_ids.append(class_id)
confidences.append(float(confidence))
boxes.append([x, y, w, h])
indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
if only_people:
for i in indices:
if class_ids[i] == 0:
box = boxes[i]
x = box[0]
y = box[1]
w = box[2]
h = box[3]
draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))
else:
for i in indices:
box = boxes[i]
x = box[0]
y = box[1]
w = box[2]
h = box[3]
draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))
return image
# Pose Estimation
def pose_mediapipe(image, segmentation):
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_pose = mp.solutions.pose
BG_COLOR = (192, 192, 192) # gray
with mp_pose.Pose(
static_image_mode=True,
model_complexity=2,
enable_segmentation=segmentation,
min_detection_confidence=0.5) as pose:
image_height, image_width, _ = image.shape
# Convert the BGR image to RGB before processing.
results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
annotated_image = image.copy()
# Draw segmentation on the image.
if segmentation:
condition = np.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1
bg_image = np.zeros(image.shape, dtype=np.uint8)
bg_image[:] = BG_COLOR
annotated_image = np.where(condition, annotated_image, bg_image)
# Draw pose landmarks on the image.
mp_drawing.draw_landmarks(
annotated_image,
results.pose_landmarks,
mp_pose.POSE_CONNECTIONS,
landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
return annotated_image
# Both
def both(image_, only_people):
def get_output_layers(net):
layer_names = net.getLayerNames()
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
return output_layers
def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
label = str(classes[class_id])
color = COLORS[class_id]
cv2.rectangle(img, (x,y), (x_plus_w,y_plus_h), color, 2)
cv2.putText(img, label, (x-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
image = image_.copy()
Width = image.shape[1]
Height = image.shape[0]
scale = 0.00392
classes = None
with open('yolov3.txt', 'r') as f:
classes = [line.strip() for line in f.readlines()]
COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg')
blob = cv2.dnn.blobFromImage(image, scale, (416,416), (0,0,0), True, crop=False)
net.setInput(blob)
outs = net.forward(get_output_layers(net))
class_ids = []
confidences = []
boxes = []
conf_threshold = 0.5
nms_threshold = 0.4
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
center_x = int(detection[0] * Width)
center_y = int(detection[1] * Height)
w = int(detection[2] * Width)
h = int(detection[3] * Height)
x = center_x - w / 2
y = center_y - h / 2
class_ids.append(class_id)
confidences.append(float(confidence))
boxes.append([x, y, w, h])
indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
if only_people:
for i in indices:
if class_ids[i] == 0:
box = boxes[i]
x = box[0]
y = box[1]
w = box[2]
h = box[3]
draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))
else:
for i in indices:
box = boxes[i]
x = box[0]
y = box[1]
w = box[2]
h = box[3]
draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_pose = mp.solutions.pose
BG_COLOR = (192, 192, 192) # gray
with mp_pose.Pose(
static_image_mode=True,
model_complexity=2,
enable_segmentation=False,
min_detection_confidence=0.5) as pose:
image_height, image_width, _ = image_.shape
# Convert the BGR image to RGB before processing.
results = pose.process(cv2.cvtColor(image_, cv2.COLOR_BGR2RGB))
annotated_image = image.copy()
# Draw pose landmarks on the image.
mp_drawing.draw_landmarks(
annotated_image,
results.pose_landmarks,
mp_pose.POSE_CONNECTIONS,
landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
return annotated_image
def model_picker(image, model, segmentation, only_people):
if model == 0:
result = box_yolo(image, only_people)
elif model == 1:
result = pose_mediapipe(image, segmentation)
elif model == 2:
result = both(image, only_people)
return result
image_in = gr.inputs.Image(label='Input Image')
radio_in = gr.Radio(['Bounding Box', 'Pose Estimation', 'Both'], type='index', label='Model Type')
checkbox_1 = gr.inputs.Checkbox(label='Enable Segmentation (For Pose Estimation)')
checkbox_2 = gr.inputs.Checkbox(label='Bound Only People in a Box')
iface = gr.Interface(fn=model_picker, inputs=[image_in, radio_in, checkbox_1, checkbox_2], outputs='image')
# app.launch(share=True)
iface.launch() |