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import cv2
from mmdeploy_runtime import Detector, Segmentor, Classifier
import numpy as np
import gradio as gr
import math
import os
# Load models globally to avoid redundancy
helmet_detector = Detector(model_path='/mnt/e/AI/mmdeploy/output/helmet', device_name='cuda', device_id=0)
red_tree_segmentor = Segmentor(model_path='/mnt/e/AI/mmdeploy/output/red_tree', device_name='cuda', device_id=0)
vest_detector = Detector(model_path='/mnt/e/AI/mmdeploy/output/vest_detection', device_name='cuda', device_id=0)
car_detector = Detector(model_path='/mnt/e/AI/mmdeploy/output/car_calculation', device_name='cuda', device_id=0)
crack_classifier = Classifier(model_path='/mnt/e/AI/mmdeploy/output/crack_classification', device_name='cuda', device_id=0)
disease_object_detector = Detector(model_path='/mnt/e/AI/mmdeploy/output/disease_object_detection', device_name='cuda', device_id=0)
crack_segmentor = Segmentor(model_path='/mnt/e/AI/mmdeploy/output/crack_detection2', device_name='cuda', device_id=0)
leaf_disease_segmentor = Segmentor(model_path='/mnt/e/AI/mmdeploy/output/disease_leaf', device_name='cuda', device_id=0)
single_label_disease_segmentor = Segmentor(model_path='/mnt/e/AI/mmdeploy/output/disease_detection', device_name='cuda', device_id=0)
fall_detector = Detector(model_path='/mnt/e/AI/mmdeploy/output/fall_detection_fastercnn', device_name='cuda', device_id=0)
mask_detector = Detector(model_path='/mnt/e/AI/mmdeploy/output/mask_detection', device_name='cuda', device_id=0)
smoker_detector_object = Detector(model_path='/mnt/e/AI/mmdeploy/output/smoker_nonsmoker', device_name='cuda', device_id=0)
def smoker_detector(frame, confidence_threshold=0.3):
SMOKE_LABELS = ['smoker', 'nonsmoker'] # 新的标签列表
bboxes, labels, masks = smoker_detector_object(frame) # 修改检测器名字
# 获取有效的bbox索引
valid_indices = [(i, SMOKE_LABELS[label]) for i, label in enumerate(labels) if SMOKE_LABELS[label] == 'smoker' and bboxes[i][4] >= confidence_threshold]
smoker_count = 0
for i, label_name in valid_indices:
bbox = bboxes[i]
[left, top, right, bottom], score = bbox[0:4].astype(int), bbox[4]
if label_name == 'smoker':
color = (255, 0, 0) # 绿色用于'smoker'
smoker_count += 1
line_thickness = 2
font_scale = 0.8
cv2.rectangle(frame, (left, top), (right, bottom), color, thickness=line_thickness)
label_text = f"{label_name} ({score:.2f})"
cv2.putText(frame, label_text, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, line_thickness)
if masks and masks[i].size:
mask = masks[i]
blue, green, red = cv2.split(frame)
if mask.shape == frame.shape[:2]:
mask_img = blue
else:
x0 = int(max(math.floor(bbox[0]) - 1, 0))
y0 = int(max(math.floor(bbox[1]) - 1, 0))
mask_img = blue[y0:y0 + mask.shape[0], x0:x0 + mask.shape[1]]
cv2.bitwise_or(mask, mask_img, mask_img)
frame = cv2.merge([blue, green, red])
# 显示smoker的数量
frame_height, frame_width = frame.shape[:2]
summary_text = f"Smokers: {smoker_count}"
cv2.putText(frame, summary_text, (frame_width - 200, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
return frame, smoker_count
def crack_classification(frame, confidence_threshold=0.5):
# 定义标签
labels_dict = {0: 'Negative', 1: 'Positive'}
# 使用裂缝分类器进行预测
result = crack_classifier(frame)
# 获取最大置信度的标签ID
label_id, score = max(result, key=lambda x: x[1])
if label_id == 1 and score > confidence_threshold: # 如果检测到有裂缝,并且置信度超过阈值
seg = crack_segmentor(frame)
crack_pixel_count = np.sum(seg == 1)
current_palette = [(255, 255, 255), (255, 0, 0)] # 背景为白色,裂缝为红色
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
for label, color in enumerate(current_palette):
color_seg[seg == label, :] = color
frame = frame * 0.5 + color_seg * 0.5
frame = frame.astype(np.uint8)
elif label_id == 0 and score <= confidence_threshold:
crack_pixel_count = None
else:
crack_pixel_count = None
label_id = 0 # 这里我默认设置为0,即"Negative",但你可以根据实际情况进行调整
# 在图像右上角显示预测结果和置信度
label_text = labels_dict[label_id] + f" ({score:.2f})"
color = (255, 0, 0) if label_id == 1 else (0, 255, 0) # 裂缝为红色,否则为绿色
font_scale = 0.8
line_thickness = 2
text_size = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, line_thickness)[0]
cv2.putText(frame, label_text, (frame.shape[1] - text_size[0] - 10, text_size[1] + 10), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, line_thickness)
return frame, labels_dict[label_id], crack_pixel_count
def crack_detection(frame):
# 使用裂缝检测器进行检测
seg = crack_segmentor(frame)
crack_pixel_count = np.sum(seg == 1)
# 如果检测到裂缝,进行可视化处理
if crack_pixel_count > 0:
current_palette = [(255, 255, 255), (255, 0, 0)] # 背景为白色,裂缝为红色
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
for label, color in enumerate(current_palette):
color_seg[seg == label, :] = color
frame = frame * 0.5 + color_seg * 0.5
frame = frame.astype(np.uint8)
# 在图像右上角显示检测到的裂缝像素数量
label_text = f"Crack Pixels: {crack_pixel_count}"
color = (255, 0, 0) if crack_pixel_count > 0 else (0, 255, 0) # 如果有裂缝则为红色,否则为绿色
font_scale = 0.8
line_thickness = 2
text_size = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, line_thickness)[0]
cv2.putText(frame, label_text, (frame.shape[1] - text_size[0] - 10, text_size[1] + 10), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, line_thickness)
return frame, crack_pixel_count
def car_calculation(frame, confidence_threshold=0.7):
CAR_LABEL = 'car' # 这里只有一个车辆标签
bboxes, labels, masks = car_detector(frame)
valid_indices = [i for i, label in enumerate(labels) if bboxes[i][4] >= confidence_threshold]
car_count = 0
for i in valid_indices:
bbox = bboxes[i]
[left, top, right, bottom], score = bbox[0:4].astype(int), bbox[4]
color = (0, 255, 0) # 使用绿色标记车辆
line_thickness = 2
font_scale = 0.8
cv2.rectangle(frame, (left, top), (right, bottom), color, thickness=line_thickness)
label_text = CAR_LABEL + f" ({score:.2f})"
cv2.putText(frame, label_text, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, line_thickness)
if masks and masks[i].size:
mask = masks[i]
blue, green, red = cv2.split(frame)
if mask.shape == frame.shape[:2]:
mask_img = blue
else:
x0 = int(max(math.floor(bbox[0]) - 1, 0))
y0 = int(max(math.floor(bbox[1]) - 1, 0))
mask_img = blue[y0:y0 + mask.shape[0], x0:x0 + mask.shape[1]]
cv2.bitwise_or(mask, mask_img, mask_img)
frame = cv2.merge([blue, green, red])
car_count += 1
return frame, car_count
def vest_detection(frame, confidence_threshold=0.3):
VEST_LABELS = ['other_clothes', 'vest'] # 新的标签列表
bboxes, labels, masks = vest_detector(frame)
# 获取有效的bbox索引
valid_indices = [(i, VEST_LABELS[label]) for i, label in enumerate(labels) if VEST_LABELS[label] in ['vest', 'other_clothes'] and bboxes[i][4] >= confidence_threshold]
vest_count = 0
other_clothes_count = 0
for i, label_name in valid_indices:
bbox = bboxes[i]
[left, top, right, bottom], score = bbox[0:4].astype(int), bbox[4]
if label_name == 'vest':
color = (0, 255, 255) # 黄色用于'vest'
vest_count += 1
else:
color = (255, 0, 0) # 蓝色用于'other_clothes'
other_clothes_count += 1
line_thickness = 2
font_scale = 0.8
cv2.rectangle(frame, (left, top), (right, bottom), color, thickness=line_thickness)
label_text = f"{label_name} ({score:.2f})"
cv2.putText(frame, label_text, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, line_thickness)
if masks and masks[i].size:
mask = masks[i]
blue, green, red = cv2.split(frame)
if mask.shape == frame.shape[:2]:
mask_img = blue
else:
x0 = int(max(math.floor(bbox[0]) - 1, 0))
y0 = int(max(math.floor(bbox[1]) - 1, 0))
mask_img = blue[y0:y0 + mask.shape[0], x0:x0 + mask.shape[1]]
cv2.bitwise_or(mask, mask_img, mask_img)
frame = cv2.merge([blue, green, red])
# 显示vest和other_clothes的数量和置信度
frame_height, frame_width = frame.shape[:2]
summary_text = f"Vests: {vest_count}, Other Clothes: {other_clothes_count}"
cv2.putText(frame, summary_text, (frame_width - 300, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
return frame, vest_count, other_clothes_count
def detect_falls(frame, confidence_threshold=0.5):
# 假设输入图像是RGB格式,转换为BGR
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
LABELS = ['fall', 'person']
# 初始化摔倒计数器
fall_count = 0
# 使用模型进行检测
bboxes, labels, masks = fall_detector(frame)
for bbox, label_id in zip(bboxes, labels):
[left, top, right, bottom], score = bbox[0:4].astype(int), bbox[4]
if score < confidence_threshold:
continue
if LABELS[label_id] == 'fall': # 仅显示摔倒的标注框
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
label_text = f"{LABELS[label_id]}: {int(score*100)}%"
cv2.putText(frame, label_text, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
# 递增摔倒计数器
fall_count += 1
# 转换图像回RGB格式
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 返回处理后的图像和摔倒的数量
return frame, fall_count
def leaf_disease_detection(frame, confidence_threshold=0.3):
# 假设输入图像是RGB格式,转换为BGR
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
LABELS = ['disease']
# 初始化病害计数器
disease_count = 0
bboxes, labels, masks = disease_object_detector(frame)
indices = [i for i in range(len(bboxes))]
for index, bbox, label_id in zip(indices, bboxes, labels):
[left, top, right, bottom], score = bbox[0:4].astype(int), bbox[4]
if score < confidence_threshold:
continue
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 1)
label_text = f"{LABELS[label_id]}: {int(score*100)}%"
cv2.putText(frame, label_text, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
if masks[index].size:
mask = masks[index]
blue, green, red = cv2.split(frame)
if mask.shape == frame.shape[:2]:
mask_img = blue
else:
x0 = int(max(math.floor(bbox[0]) - 1, 0))
y0 = int(max(math.floor(bbox[1]) - 1, 0))
mask_img = blue[y0:y0 + mask.shape[0], x0:x0 + mask.shape[1]]
cv2.bitwise_or(mask, mask_img, mask_img)
frame = cv2.merge([blue, green, red])
# 递增病害计数器
disease_count += 1
# 转换图像回RGB格式
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 返回处理后的图像、病害计数和保存的图像路径
return frame, disease_count
def detect_masks(frame, confidence_threshold=0.5):
# 假设输入图像是RGB格式,转换为BGR
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
LABELS = ['unfit', 'mask', 'nomask']
# 初始化三个标签的计数器
mask_count, nomask_count, unfit_count = 0, 0, 0
# 使用模型进行检测
bboxes, labels, masks = mask_detector(frame)
for bbox, label_id in zip(bboxes, labels):
[left, top, right, bottom], score = bbox[0:4].astype(int), bbox[4]
if score < confidence_threshold:
continue
# 根据标签ID判断类别,并进行相应的计数
if LABELS[label_id] == 'mask':
mask_count += 1
color = (0, 255, 0)
elif LABELS[label_id] == 'nomask':
nomask_count += 1
color = (0, 0, 255)
elif LABELS[label_id] == 'unfit':
unfit_count += 1
color = (255, 0, 0)
cv2.rectangle(frame, (left, top), (right, bottom), color, 2)
label_text = f"{LABELS[label_id]}: {int(score*100)}%"
cv2.putText(frame, label_text, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
# 转换图像回RGB格式
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 返回处理后的图像和每个标签的数量
return frame, mask_count, nomask_count, unfit_count
def helmet_detection(frame, confidence_threshold=0.3):
HEL_LABELS = ['head', 'helmet']
bboxes, labels, masks = helmet_detector(frame)
valid_indices = [i for i, bbox in enumerate(bboxes) if bbox[4] >= confidence_threshold]
helmet_count = 0
head_count = 0
for i in valid_indices:
bbox = bboxes[i]
label_id = labels[i]
[left, top, right, bottom], score = bbox[0:4].astype(int), bbox[4]
if HEL_LABELS[label_id] == 'helmet':
color = (0, 255, 0) # Green color for 'helmet'
line_thickness = 1
font_scale = 0.5
elif HEL_LABELS[label_id] == 'head':
color = (255, 0, 0) # Red color for 'head'
line_thickness = 1 # Increased line thickness for 'head' boxes
font_scale = 0.5 # Decreased font size for 'head' labels
cv2.rectangle(frame, (left, top), (right, bottom), color, thickness=line_thickness)
label_text = HEL_LABELS[label_id] + f" ({score:.2f})"
cv2.putText(frame, label_text, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, line_thickness)
if HEL_LABELS[label_id] == 'helmet':
helmet_count += 1
elif HEL_LABELS[label_id] == 'head':
head_count += 1
return frame, helmet_count, head_count
def human_calculation(frame, confidence_threshold=0.3):
"""
Process the given image to count the number of humans.
"""
HEL_LABELS = ['head', 'helmet']
bboxes, labels, masks = helmet_detector(frame)
human_count = 0 # Initialize human count
for i in range(len(bboxes)):
bbox = bboxes[i]
label_id = labels[i]
score = bbox[4]
# Check if the label is 'head' or 'helmet' and the score is greater than confidence_threshold
if HEL_LABELS[label_id] in ['head', 'helmet'] and score > confidence_threshold:
human_count += 1
[left, top, right, bottom] = bbox[0:4].astype(int)
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), thickness=1) # Red color for boxes
label_text = f"human ({score:.2f})" # Include confidence score in label_text
cv2.putText(frame, label_text, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
return frame, human_count
def red_tree(img):
img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
def get_palette(num_classes=2):
return [(255, 255, 255), (255, 0, 0)]
seg = red_tree_segmentor(img_bgr)
red_tree_pixel_count = np.sum(seg == 1)
current_palette = get_palette()
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
for label, color in enumerate(current_palette):
color_seg[seg == label, :] = color
color_seg_bgr = color_seg[..., ::-1]
img_bgr = img_bgr * 0.5 + color_seg_bgr * 0.5
img_bgr = img_bgr.astype(np.uint8)
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
return img_rgb, red_tree_pixel_count
def leaf_disease(img):
img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
def get_palette(num_classes=3):
return [(255, 255, 255), (0, 255, 0), (255, 0, 0)]
seg = leaf_disease_segmentor(img_bgr)
leaf_pixel_count = np.sum(seg == 1)
disease_pixel_count = np.sum(seg == 2)
current_palette = get_palette()
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
for label, color in enumerate(current_palette):
color_seg[seg == label, :] = color
color_seg_bgr = color_seg[..., ::-1]
img_bgr = img_bgr * 0.5 + color_seg_bgr * 0.5
img_bgr = img_bgr.astype(np.uint8)
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
return img_rgb, leaf_pixel_count, disease_pixel_count
def single_label_disease(img):
img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
def get_palette(num_classes=2):
return [(255, 255, 255), (255, 0, 0)]
seg = single_label_disease_segmentor(img_bgr)
disease_pixel_count = np.sum(seg == 1)
current_palette = get_palette()
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
for label, color in enumerate(current_palette):
color_seg[seg == label, :] = color
color_seg_bgr = color_seg[..., ::-1]
img_bgr = img_bgr * 0.5 + color_seg_bgr * 0.5
img_bgr = img_bgr.astype(np.uint8)
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
return img_rgb, disease_pixel_count
def get_image_examples():
image_dir = "/mnt/e/AI/mmdeploy/gradio/photo"
image_files = [f for f in os.listdir(image_dir) if f.endswith(('.png', '.jpg', '.jpeg'))]
image_files.sort(key=lambda f: int(''.join(filter(str.isdigit, f)))) # 按数字排序
example_choices = [
'红树林识别', '红树林识别', '红树林识别',
'安全帽检测', '安全帽检测', '安全帽检测',
'人数统计', '人数统计', '人数统计',
'反光衣检测','反光衣检测','反光衣检测',
'道路车辆统计', '道路车辆统计', '道路车辆统计',
'裂缝识别', '裂缝识别', '裂缝识别',
'吸烟检测','吸烟检测','吸烟检测',
'树叶病害识别1','树叶病害识别1','树叶病害识别1',
'树叶病害识别2','树叶病害识别2','树叶病害识别2',
'树叶病害检测3','树叶病害检测3','树叶病害检测3',
'摔倒检测','摔倒检测','摔倒检测',
'口罩佩戴检测','口罩佩戴检测','口罩佩戴检测',
]
confidence_thresholds = [
0, 0, 0,
0.7, 0.8, 0.6,
0.3, 0.8, 0.5,
0.8, 0.7, 0.8,
0.5, 0.2, 0.7,
0, 0, 0,
0.6, 0.9, 0.5,
0, 0, 0,
0, 0, 0,
0.4, 0.4, 0.5,
0.9, 0.9, 0.5,
0.8, 0.6, 0.5
]
examples = [[example_choices[i], f"{image_dir}/{image_file}", confidence_thresholds[i]] for i, image_file in enumerate(image_files)]
return examples
model_choices = ['红树林识别','裂缝识别','树叶病害识别1','树叶病害识别2','树叶病害检测3', '安全帽检测','反光衣检测', '吸烟检测','摔倒检测', '口罩佩戴检测','人数统计','道路车辆统计']
def create_blank_image(width=640, height=480, color=(255, 255, 255)):
blank_image = np.zeros((height, width, 3), np.uint8)
blank_image[:, :] = color
return blank_image
def process_image(model_choice, image_array=None, confidence_threshold=0.3):
output_text = '当前未有图片输入,请上传图片后再次点击运行。'
if image_array is None:
img = create_blank_image()
else:
if model_choice not in model_choices:
model_choice = "安全帽检测"
# 以下是模型选择和执行逻辑
if model_choice == "红树林识别":
img, red_tree_pixel_count = red_tree(image_array) # 语义分割模型
output_text = f"红树林的像素点有 {red_tree_pixel_count} 个。"
elif model_choice == "安全帽检测":
img, helmet_count, head_count = helmet_detection(image_array, confidence_threshold)
output_text = f"佩戴安全帽的人数为:{helmet_count},未佩戴安全帽的人数为:{head_count}。"
elif model_choice == "人数统计":
img, human_count = human_calculation(image_array, confidence_threshold)
output_text = f"该图片人员总人数为: {human_count}。"
elif model_choice == "反光衣检测":
img, vest_count, other_clothes_count= vest_detection(image_array, confidence_threshold)
output_text = f"该图片中总有 {vest_count} 人配备了反光衣,{other_clothes_count} 人没有配备反光衣。"
elif model_choice == "道路车辆统计":
img, car_count = car_calculation(image_array, confidence_threshold)
output_text = f"该道路上目前共有 {car_count} 台车辆。"
elif model_choice == "裂缝识别":
img, crack_result, crack_pixel_count = crack_classification(image_array, confidence_threshold)
if crack_result == "Positive":
output_text = f"该图片内存在裂缝,裂缝的像素点有 {crack_pixel_count} 个。"
else:
output_text = "该图片不存在裂缝。"
elif model_choice == "树叶病害检测3":
img, disease_count = leaf_disease_detection(image_array, confidence_threshold)
if disease_count > 0:
output_text = f"共检测到 {disease_count} 处病害。"
else:
output_text = "并未检测到病害。"
elif model_choice == "吸烟检测":
img, smoker_count = smoker_detector(image_array, confidence_threshold)
output_text = f"当前图片有 {smoker_count} 人在吸烟。"
elif model_choice == "树叶病害识别1":
img, leaf_pixel_count, disease_pixel_count = leaf_disease(image_array) # 语义分割模型
if disease_pixel_count == 0:
output_text = "该树叶并未出现病害。"
else:
output_text = f"病害的像素点有 {disease_pixel_count} 个。"
elif model_choice == "树叶病害识别2":
img, disease_pixel_count = single_label_disease(image_array) # 语义分割模型
output_text = f"病害的像素点有 {disease_pixel_count} 个。"
elif model_choice == "摔倒检测": # 您可以根据实际情况调整模型选择的名称
img, fall_count = detect_falls(image_array,confidence_threshold)
output_text = f"图像中摔倒的人数为 {fall_count} 人。"
elif model_choice == "口罩佩戴检测": # 您可以根据实际情况调整模型选择的名称
img, mask_count, nomask_count, unfit_count = detect_masks(image_array,confidence_threshold)
output_text = f"当前佩戴口罩的人数为 {mask_count},未正确佩戴口罩的人数为 {unfit_count},没有佩戴口罩的人数为 {nomask_count}。"
return img, output_text
def process_video(model_choice, video=None, confidence_threshold=0.3):
# 内部函数:创建空白视频
def create_blank_video(filename, duration=5, fps=30, width=640, height=480, color=(255, 255, 255)):
fourcc = cv2.VideoWriter_fourcc(*'mp4v') # 使用mp4v编解码器
out = cv2.VideoWriter(filename, fourcc, fps, (width, height))
blank_image = np.zeros((height, width, 3), np.uint8)
blank_image[:, :] = color
for _ in range(int(fps * duration)):
out.write(blank_image)
out.release()
# 检查视频是否存在
if video is None:
video_output_path = '/mnt/e/AI/mmdeploy/gradio/video/none.mp4'
create_blank_video(video_output_path)
output_text2 = '当前未有视频输入,请上传视频后再次点击运行。'
return video_output_path, output_text2
else:
video_output_path = '/mnt/e/AI/mmdeploy/gradio/video/output_video.mp4'
cap = cv2.VideoCapture(video)
if not cap.isOpened():
raise ValueError("无法打开视频文件")
fps = int(cap.get(cv2.CAP_PROP_FPS))
num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
# 获取输入视频的分辨率
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# 使用帧采样的逻辑,但考虑到所有帧都需要处理,我们使用间隔为1的采样。
clip_len, frame_interval, num_clips = 1, 1, num_frames
avg_interval = (num_frames - clip_len * frame_interval + 1) / float(num_clips)
frame_inds = []
for i in range(num_clips):
clip_offset = int(i * avg_interval + avg_interval / 2.0)
for j in range(clip_len):
ind = (j * frame_interval + clip_offset) % num_frames
if num_frames <= clip_len * frame_interval - 1:
ind = j % num_frames
frame_inds.append(ind)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
processed_frames = []
for frame_id in sorted(frame_inds):
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id) # 设置读取特定的帧
ret, frame = cap.read()
if not ret:
break
# 将帧率添加到视频的左上角
cv2.putText(frame, "FPS: {}".format(fps), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
if model_choice == "红树林识别":
# 在此处调用红树林模型处理帧
processed_frame, red_tree_pixel_count = red_tree(frame)
# 在处理后的帧的右上角添加文字
cv2.putText(processed_frame, "Number of pixels in this frame: {}".format(red_tree_pixel_count),
(processed_frame.shape[1] - 300, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (255, 255, 255), 2)
elif model_choice == "安全帽检测":
# 在此处调用安全帽检测模型处理帧
processed_frame, helmet_count, head_count = helmet_detection(frame, confidence_threshold)
cv2.putText(processed_frame, "Number of people wearing helmets: {}".format(helmet_count),
(processed_frame.shape[1] - 400, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (255, 255, 255), 2)
# 在上一行文字下方添加表示未佩戴安全帽的人数的文字
cv2.putText(processed_frame, "Number of people without helmets: {}".format(head_count - helmet_count),
(processed_frame.shape[1] - 450, 60), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (255, 255, 255), 2)
elif model_choice == "人数统计":
# 在此处调用人数统计模型处理帧
processed_frame, human_count = human_calculation(frame, confidence_threshold)
cv2.putText(processed_frame, "Current number of people: {}".format(human_count),
(processed_frame.shape[1] - 300, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (255, 255, 255), 2)
elif model_choice == "反光衣检测":
# 在此处调用反光衣检测模型处理帧
processed_frame, vest_count, other_clothes_count= vest_detection(image_array, confidence_threshold)
cv2.putText(processed_frame, "Number of reflective vests: {}".format(vest_count),
(processed_frame.shape[1] - 350, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (255, 255, 255), 2)
cv2.putText(processed_frame, "Number without reflective vests: {}".format(other_clothes_count),
(processed_frame.shape[1] - 450, 60), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (255, 255, 255), 2)
elif model_choice == "道路车辆统计":
# 在此处调用道路车辆统计模型处理帧
processed_frame, car_count = car_calculation(frame, confidence_threshold)
cv2.putText(processed_frame, "Number of vehicles: {}".format(car_count),
(processed_frame.shape[1] - 250, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (255, 255, 255), 2)
elif model_choice == "裂缝识别":
# 在此处调用裂缝识别模型处理帧
processed_frame, crack_pixel_count= crack_detection(frame)
elif model_choice == "树叶病害检测3":
# 在此处调用树叶病害检测模型处理帧
processed_frame, disease_count= leaf_disease_detection(frame, confidence_threshold)
# 在图像右上角显示叶片的病害数量
label_text = f"Leaf Disease Count: {disease_count}"
color = (0, 0, 255) # 红色
font_scale = 0.8
line_thickness = 2
text_size = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, line_thickness)[0]
cv2.putText(processed_frame, label_text, (processed_frame.shape[1] - text_size[0] - 10, text_size[1] + 10), cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, line_thickness)
elif model_choice == "吸烟检测":
# 在此处调用吸烟检测模型处理帧
processed_frame, smoker_count = smoker_detector(frame, confidence_threshold)
# 准备要显示的文本
text = f"吸烟者数量: {smoker_count}"
# 获取文本大小
text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)[0]
# 计算文本的位置,以便它出现在帧的右上角
text_position = (processed_frame.shape[1] - text_size[0] - 10, text_size[1] + 10)
# 将文本绘制到帧上
cv2.putText(processed_frame, text, text_position, cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
elif model_choice == "树叶病害识别1":
# 在此处调用树叶病害识别模型处理帧
processed_frame, _, disease_pixel_count = leaf_disease(frame)
text = f"Current disease pixel count on the leaf: {disease_pixel_count}"
text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)[0]
cv2.putText(processed_frame, text,
(processed_frame.shape[1] - text_size[0] - 10, text_size[1] + 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
elif model_choice == "树叶病害识别2":
# 在此处调用树叶病害识别模型处理帧
processed_frame, disease_pixel_count= single_label_disease(frame)
text = f"Current disease pixel count on the leaf: {disease_pixel_count}"
text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)[0]
cv2.putText(processed_frame, text,
(processed_frame.shape[1] - text_size[0] - 10, text_size[1] + 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
elif model_choice == "口罩佩戴检测": # 您可以根据实际情况调整模型选择的名称
processed_frame, mask_count, nomask_count, unfit_count = detect_masks(frame,confidence_threshold)
cv2.putText(processed_frame, "Number wearing masks: {}".format(mask_count),
(processed_frame.shape[1] - 350, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (255, 255, 255), 2)
cv2.putText(processed_frame, "Number not wearing masks: {}".format(nomask_count),
(processed_frame.shape[1] - 400, 60), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (255, 255, 255), 2)
cv2.putText(processed_frame, "Number wearing masks incorrectly: {}".format(unfit_count),
(processed_frame.shape[1] - 500, 90), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (255, 255, 255), 2)
elif model_choice == "摔倒检测":
processed_frame, fall_count= detect_falls(frame,confidence_threshold)
cv2.putText(processed_frame, "Number of people who fell: {}".format(fall_count),
(processed_frame.shape[1] - 350, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.7, (255, 255, 255), 2)
processed_frames.append(processed_frame)
out = cv2.VideoWriter(video_output_path, fourcc, fps, (width,height))
for frame in processed_frames:
out.write(frame)
out.release()
cap.release()
output_text2 = '请点击蓝色按钮下载视频。'
return video_output_path, output_text2
with gr.Blocks() as demo:
gr.Markdown("# <center>启云科技AI识别示例样板V1.12</center>")
gr.Markdown("请上传图像或视频进行预测")
with gr.Tab("AI图像处理"):
with gr.Row():
image_input2 = gr.Image(label="上传图像", type="numpy")
with gr.Column():
image_input1 = gr.Dropdown(choices=model_choices, label="模型选择")
image_input3 = gr.Slider(minimum=0, maximum=1, step=0.1, label="置信度阈值")
with gr.Row():
image_output1 = gr.Image(label="处理后的图像", type="numpy")
with gr.Column():
image_output2 = gr.Textbox(label="图像输出信息")
image_button = gr.Button('请点击按钮进行图像预测')
gr.Examples(get_image_examples(),inputs=[image_input1, image_input2, image_input3],outputs=[image_output1, image_output2], fn=process_image ,examples_per_page=6 ,cache_examples=True)
with gr.Tab("AI视频处理"):
with gr.Row():
video_input2 = gr.Video(label = '上传视频', format='mp4',interactive = True)
with gr.Column():
video_input1 = gr.Dropdown(choices=model_choices, label="模型选择")
video_input3 = gr.Slider(minimum=0, maximum=1,
step=0.1, label="置信度阈值")
with gr.Row():
video_output1 = gr.File(label='处理后的视频', type='file')
with gr.Column():
video_output2 = gr.Textbox(label = '视频输出信息')
video_button = gr.Button('请点击按钮进行视频预测')
with gr.Accordion("平台简介"):
gr.Markdown("红树林识别模型、裂缝识别模型、树叶病害识别模型、安全帽检测模型、反光衣检测模型、吸烟检测模型、口罩佩戴检测、摔倒检测、人数统计模型及道路车辆统计模型展示平台。")
image_button.click(process_image, inputs = [image_input1, image_input2, image_input3], outputs=[image_output1, image_output2])
video_button.click(process_video, inputs=[video_input1,video_input2, video_input3], outputs=[video_output1, video_output2])
demo.launch(share=True)
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