Alessio Grancini
commited on
Update app.py
Browse files
app.py
CHANGED
@@ -143,32 +143,56 @@ def get_camera_matrix(depth_estimator):
|
|
143 |
"cy": depth_estimator.cy_depth
|
144 |
}
|
145 |
|
|
|
146 |
@spaces.GPU
|
147 |
def get_detection_data(image):
|
148 |
-
"""Get structured detection data with depth information"""
|
149 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
150 |
|
151 |
try:
|
152 |
-
#
|
|
|
|
|
|
|
|
|
|
|
153 |
image = utils.resize(image)
|
154 |
|
155 |
-
#
|
156 |
if hasattr(image, "shape"):
|
157 |
-
height, width = image.shape[:2]
|
158 |
|
159 |
# Get detections and depth
|
160 |
image_segmentation, objects_data = img_seg.predict(image)
|
161 |
depthmap, depth_colormap = depth_estimator.make_prediction(image)
|
162 |
|
163 |
-
#
|
|
|
|
|
|
|
|
|
164 |
detections = []
|
165 |
for data in objects_data:
|
166 |
cls_id, cls_name, cls_center, cls_mask, cls_clr = data
|
167 |
-
|
168 |
-
# Get masked depth for this object
|
169 |
masked_depth, mean_depth = utils.get_masked_depth(depthmap, cls_mask)
|
170 |
|
171 |
-
# Get bounding box from mask
|
172 |
y_indices, x_indices = np.where(cls_mask > 0)
|
173 |
if len(x_indices) > 0 and len(y_indices) > 0:
|
174 |
x1, x2 = np.min(x_indices), np.max(x_indices)
|
@@ -192,44 +216,35 @@ def get_detection_data(image):
|
|
192 |
float(cls_center[1] / height),
|
193 |
],
|
194 |
"bbox": bbox_normalized,
|
195 |
-
"depth": float(mean_depth * 10), # Convert to meters
|
196 |
"color": [float(c / 255) for c in cls_clr],
|
197 |
"mask": cls_mask.tolist(),
|
198 |
-
"confidence": 1.0, #
|
199 |
}
|
200 |
detections.append(detection)
|
201 |
|
202 |
-
#
|
203 |
-
|
204 |
-
|
205 |
-
|
206 |
-
|
207 |
-
|
208 |
-
|
209 |
-
}
|
210 |
-
except AttributeError:
|
211 |
-
print("⚠️ Camera parameters are not properly set in depth_estimator.")
|
212 |
-
camera_params = {"fx": 0, "fy": 0, "cx": width // 2, "cy": height // 2}
|
213 |
-
|
214 |
-
# Generate point cloud data if needed
|
215 |
-
point_clouds = utils.generate_obj_pcd(depthmap, objects_data)
|
216 |
-
pcd_data = [
|
217 |
-
{"points": np.asarray(pcd.points).tolist(), "color": [float(c / 255) for c in color]}
|
218 |
-
for pcd, color in point_clouds
|
219 |
-
]
|
220 |
|
221 |
return {
|
222 |
"detections": detections,
|
223 |
-
"depth_map":
|
|
|
224 |
"camera_params": camera_params,
|
225 |
"image_size": {"width": width, "height": height},
|
226 |
-
"point_clouds": pcd_data,
|
227 |
}
|
228 |
|
229 |
except Exception as e:
|
230 |
print(f"🚨 Error in get_detection_data: {str(e)}")
|
231 |
return {"error": str(e)}
|
232 |
|
|
|
|
|
233 |
|
234 |
def cancel():
|
235 |
CANCEL_PROCESSING = True
|
|
|
143 |
"cy": depth_estimator.cy_depth
|
144 |
}
|
145 |
|
146 |
+
|
147 |
@spaces.GPU
|
148 |
def get_detection_data(image):
|
149 |
+
"""Get structured detection data with depth information, using Base64 image encoding."""
|
150 |
+
|
151 |
+
def decode_base64_image(base64_string):
|
152 |
+
"""Decodes Base64 string into a NumPy image."""
|
153 |
+
try:
|
154 |
+
img_data = base64.b64decode(base64_string)
|
155 |
+
img = Image.open(BytesIO(img_data))
|
156 |
+
img = np.array(img)
|
157 |
+
return cv2.cvtColor(img, cv2.COLOR_RGB2BGR) # Convert to BGR for OpenCV
|
158 |
+
except Exception as e:
|
159 |
+
print(f"🚨 Error decoding base64 image: {e}")
|
160 |
+
return None
|
161 |
+
|
162 |
+
def encode_base64_image(image):
|
163 |
+
"""Encodes a NumPy image into a Base64 string."""
|
164 |
+
_, buffer = cv2.imencode('.png', image)
|
165 |
+
return base64.b64encode(buffer).decode("utf-8")
|
166 |
+
|
167 |
+
width, height = 640, 480 # Default values
|
168 |
|
169 |
try:
|
170 |
+
if isinstance(image, str): # Ensure we're handling a Base64 string
|
171 |
+
image = decode_base64_image(image)
|
172 |
+
if image is None:
|
173 |
+
return {"error": "Invalid base64 image data"}
|
174 |
+
|
175 |
+
# Resize image
|
176 |
image = utils.resize(image)
|
177 |
|
178 |
+
# Extract dimensions
|
179 |
if hasattr(image, "shape"):
|
180 |
+
height, width = image.shape[:2]
|
181 |
|
182 |
# Get detections and depth
|
183 |
image_segmentation, objects_data = img_seg.predict(image)
|
184 |
depthmap, depth_colormap = depth_estimator.make_prediction(image)
|
185 |
|
186 |
+
# Encode results as Base64
|
187 |
+
segmentation_b64 = encode_base64_image(image_segmentation)
|
188 |
+
depth_b64 = encode_base64_image(depth_colormap)
|
189 |
+
|
190 |
+
# Process detections
|
191 |
detections = []
|
192 |
for data in objects_data:
|
193 |
cls_id, cls_name, cls_center, cls_mask, cls_clr = data
|
|
|
|
|
194 |
masked_depth, mean_depth = utils.get_masked_depth(depthmap, cls_mask)
|
195 |
|
|
|
196 |
y_indices, x_indices = np.where(cls_mask > 0)
|
197 |
if len(x_indices) > 0 and len(y_indices) > 0:
|
198 |
x1, x2 = np.min(x_indices), np.max(x_indices)
|
|
|
216 |
float(cls_center[1] / height),
|
217 |
],
|
218 |
"bbox": bbox_normalized,
|
219 |
+
"depth": float(mean_depth * 10), # Convert to meters
|
220 |
"color": [float(c / 255) for c in cls_clr],
|
221 |
"mask": cls_mask.tolist(),
|
222 |
+
"confidence": 1.0, # Placeholder confidence
|
223 |
}
|
224 |
detections.append(detection)
|
225 |
|
226 |
+
# Camera parameters
|
227 |
+
camera_params = {
|
228 |
+
"fx": getattr(depth_estimator, "fx_depth", 0),
|
229 |
+
"fy": getattr(depth_estimator, "fy_depth", 0),
|
230 |
+
"cx": getattr(depth_estimator, "cx_depth", width // 2),
|
231 |
+
"cy": getattr(depth_estimator, "cy_depth", height // 2),
|
232 |
+
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
233 |
|
234 |
return {
|
235 |
"detections": detections,
|
236 |
+
"depth_map": depth_b64, # Returning depth as Base64 image
|
237 |
+
"segmentation": segmentation_b64, # Returning segmentation as Base64 image
|
238 |
"camera_params": camera_params,
|
239 |
"image_size": {"width": width, "height": height},
|
|
|
240 |
}
|
241 |
|
242 |
except Exception as e:
|
243 |
print(f"🚨 Error in get_detection_data: {str(e)}")
|
244 |
return {"error": str(e)}
|
245 |
|
246 |
+
|
247 |
+
|
248 |
|
249 |
def cancel():
|
250 |
CANCEL_PROCESSING = True
|