| Feature | MIDV‑195 4K | |---------|-------------| | Sensor | 35 mm full‑frame back‑illuminated CMOS, 24.5 MP (effective) | | Dynamic Range | 16+ stops (measured with DSC Labs) | | Resolution & Frame Rates | 4K (4096×2160) @ 24/25/30/48/50/60/120 fps; 2K @ up to 240 fps | | Bit Depth | 10‑bit 4:2:2 internally; 12‑bit 4:2:2 in ProRes RAW; 16‑bit RAW via HDMI | | Codec Options | ProRes 422 HQ/LT, DNxHR HQX, Apple ProRes RAW, Blackmagic RAW (via external recorder) | | ISO Range | Native 100‑25,600 (extendable to 50‑102,400) | | Shutter | Electronic rolling shutter with global‑shutter mode (up to 1/8000 s) | | Autofocus | Dual‑pixel PDAF with 5,000‑point coverage, face/eye detection, continuous tracking | | Stabilization | 5‑axis in‑body (up to 6 EV) + optional electronic stabilization | | Viewfinder/Display | 0.5‑inch OLED EVF (3.69 M dots) + 3.2‑inch touchscreen LCD (4.5 M dots) | | Connectivity | 12‑G‑SDI, HDMI 2.1 (12‑bit RAW), USB‑C 3.2, 2× 3.5 mm audio, Wi‑Fi 6, Bluetooth 5.2 | | Storage | Dual CFast 2.0 slots + one SD UHS‑III slot (flexible recording) | | Battery | 1× BP‑X300 (12 Wh) – up to 130 min continuous 4K/60p recording; optional V‑Mount plate | | Dimensions / Weight | 115 mm × 80 mm × 80 mm; 0.95 kg (body only) | | Ruggedness | IP68 dust & water‑proof, MIL‑STD‑810G shock‑tested |
This example:
Save as train_embeddings.py and run.
import os, random, math
from glob import glob
from PIL import Image
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as T
import torchvision.models as models
import torch.nn.functional as F
from tqdm import tqdm
# Simple dataset: expects folders per ID (if available) or flat folder.
class ImageFolderDataset(Dataset):
def __init__(self, root, size=256, augment=False):
self.paths = []
self.labels = []
classes = sorted([d for d in os.listdir(root) if os.path.isdir(os.path.join(root,d))])
if len(classes)==0:
# flat folder
self.paths = sorted(glob(os.path.join(root,"*.jpg"))+glob(os.path.join(root,"*.png")))
self.labels = [0]*len(self.paths)
else:
for idx,c in enumerate(classes):
files = glob(os.path.join(root,c,"*.jpg"))+glob(os.path.join(root,c,"*.png"))
for f in files:
self.paths.append(f); self.labels.append(idx)
self.size = size
self.augment = augment
self.base_tr = T.Compose([
T.Resize((size,size)),
T.ToTensor(),
T.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])
])
self.aug_tr = T.Compose([
T.RandomResizedCrop(size, scale=(0.7,1.0)),
T.RandomHorizontalFlip(),
T.ColorJitter(0.2,0.2,0.2,0.05),
T.RandomApply([T.GaussianBlur(3)], p=0.2),
T.ToTensor(),
T.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])
])
def __len__(self): return len(self.paths)
def __getitem__(self, i):
img = Image.open(self.paths[i]).convert('RGB')
if self.augment:
x1 = self.aug_tr(img)
x2 = self.aug_tr(img)
return x1, x2, self.labels[i]
else:
return self.base_tr(img), self.labels[i]
# Model: ResNet-50 backbone + MLP projection to 512
class EmbedNet(nn.Module):
def __init__(self, out_dim=512, backbone='resnet50', pretrained=True):
super().__init__()
if backbone=='resnet50':
net = models.resnet50(pretrained=pretrained)
dims = net.fc.in_features
modules = list(net.children())[:-1] # remove fc
self.backbone = nn.Sequential(*modules)
else:
raise ValueError("only resnet50 in this snippet")
self.head = nn.Sequential(
nn.Linear(dims, 2048),
nn.ReLU(inplace=True),
nn.BatchNorm1d(2048),
nn.Linear(2048, out_dim)
)
def forward(self, x):
x = self.backbone(x) # B x C x 1 x 1
x = x.view(x.size(0), -1)
x = self.head(x)
x = F.normalize(x, p=2, dim=1)
return x
# NT-Xent loss (contrastive with temperature)
def nt_xent_loss(z1, z2, temperature=0.1):
z = torch.cat([z1, z2], dim=0) # 2N x D
sim = torch.matmul(z, z.T) # 2N x 2N
sim = sim / temperature
N = z1.size(0)
labels = torch.arange(N, device=z.device)
labels = torch.cat([labels + N, labels], dim=0)
# mask out self-similarity
mask = (~torch.eye(2*N, dtype=torch.bool, device=z.device)).float()
exp_sim = torch.exp(sim) * mask
denom = exp_sim.sum(dim=1)
pos_sim = torch.exp(torch.sum(z1*z2, dim=1)/temperature)
pos_sim = torch.cat([pos_sim, pos_sim], dim=0)
loss = -torch.log(pos_sim / denom)
return loss.mean()
def train(root, epochs=20, bs=64, lr=1e-4, size=256, device='cuda'):
ds = ImageFolderDataset(root, size=size, augment=True)
dl = DataLoader(ds, batch_size=bs, shuffle=True, num_workers=8, drop_last=True)
model = EmbedNet(out_dim=512).to(device)
opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
scaler = torch.cuda.amp.GradScaler()
for ep in range(epochs):
model.train()
pbar = tqdm(dl, desc=f"Epoch ep+1/epochs")
for x1,x2,_lbl in pbar:
x1 = x1.to(device); x2 = x2.to(device)
with torch.cuda.amp.autocast():
z1 = model(x1); z2 = model(x2)
loss = nt_xent_loss(z1, z2, temperature=0.1)
opt.zero_grad()
scaler.scale(loss).backward()
scaler.step(opt)
scaler.update()
pbar.set_postfix(loss=loss.item())
return model
# Embedding extraction utility
def extract_embeddings(model, folder, size=256, device='cuda'):
tr = T.Compose([T.Resize((size,size)), T.ToTensor(),
T.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])])
paths = sorted(glob(os.path.join(folder,"**","*.jpg"), recursive=True)+glob(os.path.join(folder,"**","*.png"), recursive=True))
embs = []
model.eval()
with torch.no_grad():
for p in tqdm(paths):
img = Image.open(p).convert('RGB')
x = tr(img).unsqueeze(0).to(device)
z = model(x).cpu().numpy()[0]
embs.append((p,z))
return embs
if __name__=='__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--data', required=True, help='root image folder')
parser.add_argument('--mode', choices=['train','embed'], default='train')
parser.add_argument('--out', default='model.pth')
args = parser.parse_args()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if args.mode=='train':
m = train(args.data, epochs=20, bs=64, device=device)
torch.save(m.state_dict(), args.out)
else:
m = EmbedNet().to(device)
m.load_state_dict(torch.load(args.out, map_location=device))
embs = extract_embeddings(m, args.data, device=device)
# simple save
import pickle
with open('embeddings.pkl','wb') as f:
pickle.dump(embs, f)
print("Saved embeddings.pkl")
In the world of high-definition digital media, few titles have garnered as much specific interest as MIDV-195, particularly in its 4K remastered format. This release represents a significant technical milestone for the Moodyz studio, showcasing how legacy content can be revitalized for modern ultra-high-definition displays.
The transition to 4K isn't just about a higher pixel count; it is about the preservation of detail and the enhancement of the viewing experience. For a title like MIDV-195, the 4K upgrade provides a clarity that was previously unavailable in standard high-definition releases, making it a standout entry in the MIDV series. Technical Specifications and Visual Quality
The primary draw of the MIDV-195 4K edition is the technical overhaul of the original footage. Resolution: Native 3840 x 2160 pixels for crisp imagery. MIDV-195 4K
Color Depth: Improved saturation and more natural skin tones.
Clarity: Significant reduction in digital noise and compression artifacts.
Bitrate: High-bitrate encoding ensures smooth motion and fine texture detail.
By utilizing advanced upscaling and restoration techniques, the producers have ensured that every frame of MIDV-195 meets the expectations of collectors who own high-end 4K OLED or QLED televisions. Why MIDV-195 Stands Out
While many titles are released monthly, MIDV-195 has maintained a steady presence in search trends and collector discussions. This longevity is often attributed to the "Moodyz Diva" branding, which focuses on high production values and top-tier talent. | Feature | MIDV‑195 4K | |---------|-------------| |
Production Value: Professional lighting and high-end camera equipment.
Directing Style: A focus on aesthetic presentation and "idol-style" cinematography.
Legacy: Part of a long-running series known for consistent quality. Viewing the 4K Version
To truly appreciate the MIDV-195 4K remaster, viewers need the right hardware ecosystem. A standard 1080p monitor will not reveal the nuances of the 4K scan.
Display: A 4K HDR-capable monitor or television is essential. This example:
Bandwidth: If streaming, a stable connection of at least 25 Mbps is recommended.
Storage: For digital files, the 4K version requires significantly more space than the SD or HD versions.
The 4K release of MIDV-195 is more than just a simple re-release; it is a definitive version of a popular title that caters to the growing demand for premium, ultra-clear digital media. For fans of the series, this version provides the most immersive and detailed look at the production to date.
💡 Pro Tip: When searching for this title, ensure you are looking for the "4K" or "UHD" tag to avoid accidentally downloading the older 720p or 1080p versions.
Sixteen stops of usable dynamic range (measured with the SNR‑99 method) gives you leeway to capture high‑contrast scenes—think sunrise over a city skyline or a back‑lit stage performance—without blowing highlights. The built‑in dual‑gain architecture automatically switches between low‑gain (for bright areas) and high‑gain (for shadows) on the fly, preserving detail across the tonal range.